CN104208865B - Fitness monitoring device with altimeter - Google Patents

Abstract

This application is directed to fitness monitoring devices with altimeters. This disclosure discusses biometric monitoring devices, including various techniques that can be implemented in such devices. Furthermore, techniques are provided for utilizing the altimeter in a biometric monitoring device. In some implementations, these techniques may involve: recalibrating the biometric monitoring device based on location data; using altimeter data as an aid for gesture recognition; and/or using altimeter data to manage airplane mode on the biometric monitoring device.

Description

Fitness monitoring device with altimeter

technical field

The present invention is directed to a fitness monitoring device with an altimeter.

Background technique

Recent consumer concerns about personal health have resulted in a variety of personal health monitoring devices being offered on the market. Until recently, such devices tended to be complicated to use and were usually designed for one activity, such as bicycle touring computers.

Recent advances in the miniaturization of sensors, electronics, and power supplies have allowed the size of personal health monitoring devices (also referred to herein as "biometric tracking" or "biometric monitoring" devices) to provide . For example, the Fitbit Ultra is a biometric monitoring device approximately 2 inches long, 0.75 inches wide, and 0.5 inches deep; it has a pixelated display, battery, sensors, wireless communication capabilities, power and interface buttons packed into this small volume , and an integrated clip for attaching the device to a pocket or other part of clothing.

Various embodiments of biometric monitoring devices and techniques that may be used therein (and in some cases, in other devices need not provide biometric tracking functionality) are discussed herein.

Contents of the invention

Details of one or more implementations of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages will be apparent from the description, drawings, and claims. Note that the relative dimensions of the following figures may not be drawn to scale unless expressly indicated as scaled figures.

In some embodiments, a wearable biometric tracking device may be provided. The wearable biometric tracking device may include a pressure sensor configured to detect ambient atmospheric pressure around the wearable biometric tracking device and output data indicative of altitude, wherein the data indicative of altitude is at least Based in part on air pressure measured by pressure sensor data indicative of altitude; a motion sensor configured to detect motion of the wearable biometric tracking device and output corresponding motion data; and control logic, wherein the pressure The sensor, the motion sensor and the control logic are communicatively connected. The control logic may be configured to: (a) receive the data indicative of altitude, (b) receive the athletic data, (c) determine when the data indicative of altitude and the athletic data are combined associated with a first arm movement profile of a plurality of arm movement profiles, and (d) storing data associated with said first arm movement profile in response to said determining in (c).

In some such implementations of the wearable biometric tracking device, the wearable biometric tracking device can be configured to be worn in a location selected from the group consisting of: a person's arm, a person's Forearm, human hand and human fingers.

In some other or additional implementations of the wearable biometric tracking device, the pressure sensor may be a barometric altimeter.

In some other or additional implementations of the wearable biometric tracking device, the motion sensor may comprise one or more sensors selected from the group consisting of accelerometers, gyroscopes, magnetometers, pressure Electrical sensors, electromagnetic trackers, and camera-based imaging sensors.

In some other or additional implementations of the wearable biometric tracking device, the plurality of arm movement profiles may include one or more arm movement profiles that approximate arm movements representing one or more activities, each activity Choose from the group consisting of: running, walking, elliptical exercise, resistance training exercise, pull-ups, push-ups, sit-ups, skipping rope, and aerobic dance.

In some other or additional implementations of the wearable biometric tracking device, the control logic is further configured to: determine a first arm motion profile associated with the first arm motion profile, and determine the first arm motion profile. A degree of deviation between an arm motion profile and a first reference motion profile associated with said first arm motion profile. In some such implementations, both the first arm movement profile and the first reference motion profile may be associated with one or more gestures associated with common actions selected from the group consisting of : Baseball bat swing, softball bat swing, tennis racket swing, badminton racket swing, English squash racket swing, American squash racket swing, golf wood swing, golf iron swing, golf wedge swing, golf putter swing swinging a golf club, swinging a golf club, performing a resistance training exercise, performing a martial arts exercise, performing a gymnastics exercise, performing a yoga exercise, hitting a ball in a pool sport such as pool, playing a billiard sport such as snooker Hitting a ball, hitting a ball in sports such as pool, swimming, bowling, shooting with a rifle, shooting with a pistol, stabbing with a knife, swinging a knife and parrying with a knife. In some other or additional implementations, the wearable biometric tracking device may further comprise an interface device communicatively coupled with the control logic, the control logic further configured to: determine when the degree of deviation exceeds a first threshold amount of deviation, and in response to said determination that said degree of deviation exceeds said first threshold amount, causing said interface device to provide an indication that said degree of deviation exceeds said first threshold amount. The interface device may include one or more items selected from the group consisting of: a display, one or more lights, a vibration motor, and an audio device. The indication may include one or more items selected from the group consisting of: numerical score, vibration, sound, graphic, icon, animation, message indicating fatigue, message warning the wearer to slow down, and warning A message for the wearer to take a break.

In some other or additional implementations of the wearable biometric tracking device, the first arm movement profile may be associated with a resistance training curl-type activity, and the motion data and the data representing altitude Where one instance or two or more substantially consecutive instances are included, said motion data and said data indicative of altitude may be associated with said first arm movement profile, wherein: said motion data is indicative of wearing said A forearm wearing the biometric tracking device transitions from a downwardly inclined orientation to an upwardly inclined orientation during a first time period, the motion data indicating that the forearm wearing the wearable biometric tracking device is after the first time period Transitioning from the upward-sloping orientation to the downward-sloping orientation during a second time period of , and the data representing altitude indicates that the wearable biometric tracking device experienced a distance between 1 ft and 2 ft during the first time period. increase in altitude. In some such implementations, for one example, the motion data and the data indicative of altitude may be related to the first arm movement profile. In some other or additional embodiments, the number of instances may be selected from the group consisting of: i) 1 to 5 instances, ii) 5 to 8 instances, iii) 8 to 10 instances, iv) 10 to 12 examples, v) 12 to 15 examples and vi) 15 to 20 examples. In some such embodiments, the ranges of the numbers (i) to (vi) of instances may be based on indicating whether the wearer of the wearable biometric tracking device is selected from the group consisting of The purpose of the resistance training data selection is to: I) increase strength, II) increase strength and muscle mass substantially equally, III) increase muscle mass and emphasize some strength, IV) increase muscle mass and emphasize some endurance, V) Builds stamina and emphasizes some muscles and VI) Mainly builds stamina.

In some other or additional implementations of the wearable biometric tracking device, the first arm movement profile may be associated with a resistance training deadlift type activity, and the motion data and the data representing altitude When comprising one instance or two or more substantially consecutive instances, said motion data and said data representing altitude are associated with said first arm movement profile, wherein: said data representing altitude indicates said The wearable biometric tracking device experiences an increase in altitude between 1.5 ft and 2 ft during the first time period, the data representing the altitude indicates that the wearable biometric tracking device experiences an increase in altitude during the second time period. a decrease in altitude between 1.5 ft and 2 ft, and the motion data indicates that the forearm wearing the wearable biometric tracking device maintained substantially all of the first time period and the second time period. Vertical and downward orientation.

In some other or additional implementations of the wearable biometric tracking device, the first arm movement profile may be associated with a resistance training squat or bench press type activity, and the motion data and an altitude representative When the data includes one instance or two or more substantially consecutive instances, the motion data and the data representing altitude are associated with the first arm movement profile, wherein: the data representing altitude indicating that the wearable biometric tracking device experienced an increase in altitude between 0.5 ft. and 2 ft. during a first time period, the data representing altitude indicating that the wearable biometric tracking device experienced an increase in altitude during a second time period. A decrease in altitude between 0.5 ft. and 2 ft. is experienced during a time period, and the motion data indicates that the forearm wearing the wearable biometric tracking device is substantially all of the first time period and the second time period. A substantially vertical and upward orientation is maintained during the two time periods.

In some other or additional implementations of the wearable biometric tracking device, the first arm movement profile may be associated with a pre-resistance exercise raise or side raise type activity, and in the motion data and representing altitude Where said data of height comprises one instance or two or more substantially consecutive instances, said movement data and said data representing altitude may be associated with said first arm movement profile, wherein: The data indicates that the wearable biometric tracking device experienced an increase in altitude between 1 ft. and 2 ft. during the first time period, the data representing altitude indicates that the wearable biometric tracking device was at A decrease in altitude between 1 ft. and 2 ft. is experienced during a second time period, the motion data indicating a substantially vertical forearm wearing the wearable biometric tracking device from the beginning of the first time period. and the downward orientation transitions to a substantially horizontal orientation at the end of the first time period, and the motion data indicates that the forearm on which the wearable biometric tracking device is worn has changed from the beginning of the second time period The substantially horizontal orientation transitions to a substantially vertical and downward orientation at the end of the second time period.

In some other or additional implementations of the wearable biometric tracking device, the first arm movement profile may be associated with a resistance training good morning type activity, and the movement data and the data representing altitude include a In one or two or more substantially consecutive instances, the movement data and the data representing altitude may be associated with the first arm movement profile, wherein: the data representing altitude indicates that the data may be The wearing biometric tracking device experiences an increase in altitude between 1.5 ft. and 2 ft. during the first time period, the data representing the altitude indicates that the wearable biometric tracking device experiences during the second time period A decrease in altitude between 1.5 ft. and 2 ft., the motion data indicating that the forearm wearing the wearable biometric tracking device transitions from a substantially horizontal orientation at the beginning of the first time period to the a substantially vertical and upward orientation at the end of the first time period, and the motion data indicates a substantially vertical and upward orientation of the forearm wearing the wearable biometric tracking device from the beginning of the second time period The orientation transitions to a substantially horizontal orientation at the end of the second time period.

In some other or additional implementations of the wearable biometric tracking device, the first arm movement profile may be associated with elliptical machine activity, and the motion data and the Data may be associated with said first arm movement profile: said data representing altitude exhibits cyclic behavior during a first period of time, wherein small altitude changes during each cycle are consistent with the movement of a handle of the elliptical machine during normal use. commensurate with experienced changes in altitude, the athletic data exhibits cyclical behavior having substantially the same frequency as the cyclical behavior of the data representing altitude during the first time period, and the athletic data includes acceleration data indicative of an acceleration magnitude during the first time period that is less than would otherwise be used by the control logic to determine whether the A person wearing a biometric tracking device is engaged in a normal walking or running activity in which the person's feet leave the ground and the accelerometer data increments the pedometer.

In some other or additional implementations of the wearable biometric tracking device, a display may be communicatively coupled to the control logic, and the control logic may be further configured such that: 1) the first arm moves A profile may be associated with watch-watching activity; II) said motion data and said data representing altitude may be associated with said first arm movement profile when said data representing altitude indicates that said data may Wearing the biometric tracking device experiences an increase in altitude of approximately 1 ft. during the first time period, and the motion data indicates that the forearm wearing the wearable biometric tracking device has changed from substantially 1 ft. during the first time period. transitioning from a vertical and downward orientation to a substantially horizontal orientation; and III) the control logic may be further configured to cause, in response to a determination that the motion data and the data representing altitude correlates to a watch-watching activity first movement profile The display presents clock related data. In some such implementations, the motion data and the data indicative of altitude when the motion data additionally indicates that the forearm on which the wearable biometric tracking device is worn was during the period immediately following the first time period The first arm movement profile may be associated with maintaining the substantially horizontal orientation for a second period of time, the second period of time having a non-zero duration.

In some other or additional implementations of the wearable biometric tracking device, the plurality of arm movement profiles may include an arm movement profile that approximates a user at rest, and the control logic may be further configured to: track the a duration for which the first arm movement profile is substantially similar to the user resting arm movement profile; and determining that the duration for which the first arm movement profile is substantially similar to the user resting arm movement profile is greater than a rest duration threshold .

In some embodiments, a wearable biometric tracking device may be provided. The wearable biometric tracking device may include a pressure sensor configured to detect an altitude of the wearable biometric tracking device and output corresponding data representing the altitude; and a motion sensor configured to to detect the movement of the wearable biometric tracking device, and output corresponding movement data; and control logic. The biometric monitoring device, the pressure sensor and the control logic are communicatively connected. The control logic may be configured to: determine when the data representing an altitude indicates that the wearable biometric tracking device has not experienced an altitude change rate that exceeds a first altitude change rate threshold during a first time period, and at least in response to determining that the data representing altitude indicates that the wearable biometric tracking device has not experienced an altitude change rate exceeding the first altitude change threshold during the first time period, during the The wearable biometric tracking device is placed in a lower power consumption state during a second period of time subsequent to the first period of time.

In some such implementations of the wearable biometric tracking device, the control logic may be further configured to: determine when the motion data indicates that the wearable biometric tracking device is in close proximity to the first a time period prior to experiencing an impact event, and may indicate that the wearable biometric tracking device has not experienced exceeding the first altitude change rate threshold during the first time period by except in response to determining the data representing altitude In addition to the rate of change in altitude, placing the wearable biometric tracking device in response to determining that the motion data indicates that the wearable biometric tracking device experienced an impact event immediately before the first time period a lower power consumption state to place the wearable biometric tracking device in the lower power consumption state.

In some other or additional implementations of the wearable biometric tracking device, the control logic may be further configured to: determine when the data representing altitude indicates that the wearable biometric tracking device is at the An altitude rate of change exceeding a second altitude rate of change threshold is experienced during a second time period, and in response to determining that the data indicative of altitude indicates that the wearable biometric tracking device experienced during the second time period more than The altitude change rate of the second altitude change rate threshold value causes the wearable biometric tracking device to exit the lower power consumption state. In some such implementations, the control logic may be further configured to: in response to determining that the data representing altitude indicates that the wearable biometric tracking device experienced more than the first time period during the second time period. An altitude change rate of two altitude rate change thresholds, causing a display of the wearable biometric tracking device to transition from an off or standby state to an on state and display a message.

In some embodiments, a wearable biometric tracking device may be provided. The wearable biometric tracking device may include: one or more biometric sensors; an altitude sensor configured to detect an altitude of the wearable biometric tracking device and output altitude sensor data ; and control logic. The one or more biometric sensors, the altitude sensor, and the control logic are communicatively coupled, and the control logic is configured to: receive the altitude sensor data, analyze the altitude sensor data to determining whether the altitude sensor data is indicative of air travel, and placing the wearable biometric tracking device in relation to air travel at least in part responsive to the determination in (b) that the altitude sensor data is indicative of air travel in linked mode.

In some such implementations of the wearable biometric tracking device, the altitude sensor data may indicate flying when the altitude sensor data indicates an altitude rate of change that exceeds an altitude rate of change threshold. In some such implementations, the altitude change threshold may be greater than 500 feet per minute.

In some other or additional implementations of the wearable biometric tracking device, when the altitude sensor data indicates a total altitude increase over the altitude increase time period of the wearable biometric tracking device, the The altitude sensor data may indicate flying, and the total altitude increase over the altitude increase time period exceeds a total altitude increase threshold. In some such implementations, the total altitude gain threshold can be at least 1500 feet above sea level, and the altitude gain time period can be 180 seconds or less.

In some other or additional implementations of the wearable biometric tracking device, the altitude sensor data may be indicative of flying an airplane when the altitude sensor data indicates that over a period of time the altitude change The altitude change rate exceeds an altitude change rate threshold, and the altitude change rate threshold is at least 500 feet per minute. In some such implementations, the altitude change time period may be a time period of at least 60 seconds. In some implementations, a biometric monitoring device with an altimeter can be configured to detect the rate of one or more altitude changes indicative of aircraft ascent or descent, and can be preceded by automatically engaging or disengaging the aircraft mode of the biometric monitoring device Instead the wearer of the device is prompted to confirm whether the wearer is in an aircraft that is ascending or just landing before engaging or disengaging the aircraft mode. If the user confirms the behavior of the aircraft, the biometric monitoring device may proceed to engage or disengage the aircraft mode as appropriate.

In some other or additional implementations of the wearable biometric tracking device, the mode associated with air travel may be a mode that places the wearable biometric tracking device in a low power state.

In some other or additional implementations of the wearable biometric tracking device, the wearable biometric tracking device may further include wireless communication circuitry communicatively coupled to the control logic and configured to communicate wirelessly with an associated device, wherein the mode associated with air travel may be a mode that places the communication circuit in an inactive or off state.

In some other or additional implementations of the wearable biometric tracking device, the control logic may be further configured to: further analyze the altitude sensor data to determine whether the altitude sensor data is indicative of the aircraft's Landing, and at least in part responsive to a determination that the altitude sensor data indicates landing of the aircraft, causes the wearable biometric tracking device to exit the mode associated with air travel. In some such embodiments, the altitude sensor data may be indicative of the landing of the aircraft when the altitude sensor data indicates a total altitude decrease threshold exceeded within an altitude decrease time period. A total altitude decrease of the wearable biometric tracking device; and the altitude sensor data indicates that the wearable biometric tracking exceeds a total altitude decrease threshold within an altitude decrease time period. The overall altitude of the device is lowered after indicating an altitude change rate of less than 50 feet per minute over a period of 300 seconds. In some other or additional implementations, the altitude reduction time period may be 180 seconds or less. In some other or additional implementations, the control logic may be further configured to: receive biometric data from the one or more biometric sensors; analyze the biometric data to determine whether the biometric data is indicative of the the wearer of the wearable biometric tracking device has taken at least a predetermined number of steps; and the wearable biometric tracking device has taken at least a predetermined number of steps; The altitude sensor data is determined to be indicative of the landing of the aircraft upon further detecting that a wearer of the wearable biometric tracking device has taken at least a predetermined number of steps after a total altitude of the biometric tracking device has decreased. In some other or additional implementations, the total altitude reduction threshold may be an altitude reduction of at least 1500 feet.

In some other or additional implementations of the wearable biometric tracking device, the control logic may be further configured to monitor biometric data from the one or more biometric sensors, and when analyzing the altitude sensor data to determine if the altitude sensor data indicates an aircraft flight and then determine when the biometric data indicates that a wearer of the biometric monitoring device has not moved more than a threshold amount of movement relative to an aircraft frame of reference within a first period of time. In some such implementations, the wearable biometric tracking device can further include a user interface. The control logic may be further configured to respond, at least in part, to the wearer of the biometric monitoring device having not moved relative to the aircraft frame of reference by the threshold amount of movement within the first period of time. Determining causes a notification to be provided to the wearer via the user interface. In some such implementations, the wearable biometric tracking device further includes a light sensor configured to measure an amount of ambient light around the biometric tracking device and output detected light data. The control logic may be further configured to: analyze the detected light data to determine whether the detected light data is indicative of a light level typically associated with a sleep environment in an aircraft, and respond to the biometric monitoring device said determination that said wearer has not moved more than said threshold amount of movement relative to said aircraft frame of reference within said first period of time, and responsive to said detected light data not indicating said The determination of the light level associated with a sleeping environment causes the notification to be provided to the wearer via the user interface. In some other or additional implementations, the control logic may be further configured to: place the wearable biometric tracking device in a sleep tracking mode in response to a motivating factor selected from the group consisting of: (i ) an intentional request provided by the wearer of the wearable biometric tracking device and (ii) an analysis of the biometric data indicating that the wearer of the wearable biometric tracking device may be asleep; and When the wearable biometric tracking device is not in the sleep tracking mode and in response to the wearer of the biometric monitoring device having not moved more than the amount relative to the aircraft frame of reference within the first time period The determination of the threshold amount of movement causes the notification to be provided to the wearer via the user interface. In some other or additional implementations, the user interface may include a digital display, and the notification may be a message displayed on the digital display. In some other or additional implementations, the user interface may include an audio device, and the notification may be an audio notification. In some other or additional implementations, the user interface may include a tactile feedback mechanism, and the notification may be a tactile notification.

In some other or additional implementations of the wearable biometric tracking device, the wearable biometric tracking device may further include an audio sensor configured to output audio data such that the control logic may be further configured to : receiving the audio data; and analyzing the audio data to determine whether the audio data is indicative of background noise consistent with engine noise of an aircraft, wherein the control logic is further configured to be at least partially responsive to the altitude sensor The determination that the data is indicative of the flight of the aircraft and the audio data is indicative of background noise consistent with the engine noise of the aircraft places the wearable biometric monitoring device in relation to air travel. linked in the described mode.

In some other or additional implementations of the wearable biometric tracking device, the wearable biometric tracking device may further include an acceleration sensor configured to detect acceleration and output acceleration data. The control logic may be further configured to: receive the acceleration data; and analyze the acceleration data to determine whether the acceleration data indicates vibrations consistent with vibrations from an aircraft engine, wherein the control logic is further configured to Responsive at least in part to the determination that the altitude data indicates that the aircraft is flying and the determination that the acceleration data indicates vibrations consistent with the vibrations of the aircraft engine, placing the wearable biometric monitoring device on In the described modes associated with air travel.

In some other or additional implementations of the wearable biometric tracking device, the wearable biometric tracking device may further include a communication interface configured to receive communication data, wherein the control logic may be further configured to: receiving the communication data; and analyzing the communication data to determine whether the communication data indicates a communication consistent with a communication from an aircraft communication device, wherein the control logic may be further configured to be at least partially responsive to the altitude placing the wearable biometric monitoring device in response to the determination that the sensor data indicates that the aircraft is flying and further responsive to the determination that the communication data indicates communications consistent with the communication from the aircraft communication device. In the described modes associated with air travel.

In some other or additional implementations of the wearable biometric tracking device, the control logic may be further configured to: provide a stair climbing mode that tracks the number of steps climbed by the wearer of the wearable biometric tracking device a flight of stairs, and turning off the stair climbing mode in response to placing the wearable biometric tracking device in a mode associated with air travel.

In some other or additional implementations of the wearable biometric tracking device, the wearable biometric tracking device may further comprise a wireless communication interface, wherein the control logic may be further configured to: via the wireless communication interface A first input signal is received, and the wearable biometric tracking device is caused to enter the mode associated with air travel in response to the receipt of the first input signal. In some such implementations, the control logic may be further configured to cause the wearable biometric tracking device to exit the mode associated with air travel upon receipt of a second input signal indicative of activity. The second input signal may be selected from the group consisting of: powering on an associated external device, touching the wearable biometric tracking device, selecting a menu option via a user interface of the wearable biometric tracking device , and pressing a button of the wearable biometric monitoring device.

In some embodiments, a system may be provided. The system may include a wearable biometric tracking device and a mobile communication device separate from the wearable biometric tracking device. The wearable biometric tracking device may include one or more biometric sensors, a biometric tracking device communication interface, and biometric tracking device control logic such that the one or more biometric sensors, the biometric tracking A device communication interface and the biometric tracking device control logic are communicatively coupled, and the biometric tracking device control logic is configured to: receive a signal associated with an aircraft mode; and respond to receiving a signal associated with the aircraft mode The associated signal places the wearable biometric tracking device in a mode associated with air travel. The mobile communication device may include: a mobile communication device communication interface, and a mobile communication device control logic, so that the mobile communication device communication interface and the mobile communication device control logic are communicatively coupled, and the mobile communication device controls Logic configured to: transmit the signal associated with the airplane mode via the mobile communication device communication interface.

In some embodiments, a wearable biometric tracking device may be provided. The wearable biometric tracking device may include: one or more biometric sensors; a pressure sensor configured to detect ambient atmospheric pressure around the wearable biometric tracking device, and to output a signal indicative of altitude. data, wherein the data representing altitude is based at least in part on air pressure measured by the pressure sensor; a location determining device configured to be based on data received from one or more devices remote from the wearable biometric tracking device To determine the position, and output corresponding position data; and control logic. The one or more biometric sensors, the pressure sensor, the position determining device, and the control logic may be communicatively coupled, and the control logic may be configured to: receive the position from the position determining device data, analyzing the location data to determine a first location of the wearable biometric tracking device, determining a first terrain altitude from historical terrain data, wherein the first terrain altitude is an altitude associated with the first location receiving said data indicative of altitude from said pressure sensor, analyzing said data indicative of altitude to determine whether to calculate based on said first topographical altitude and (i) from said data indicative of altitude and (ii) recalibrating the pressure sensor with a first difference between altitudes associated with the first location, and when the control logic determines that the pressure sensor should be recalibrated, based on the first Poor recalibrate the pressure sensor.

In some such implementations of the wearable biometric tracking device, the pressure sensor may be a barometric altimeter or a barometric pressure sensor.

In some other or additional implementations of the wearable biometric tracking device, the location determining device may comprise one or more systems selected from the group consisting of: satellite-based global positioning system, Wi-Fi based - Fi's positioning systems, cell tower location based systems, near field communication tag lookup systems, radio frequency identification tag lookup systems, camera image based lookup systems and systems configured to interrogate nearby, external paired devices for location information.

In some other or additional implementations of the wearable biometric tracking device, the control logic may be further configured to recalibrate the pressure sensor in response to a determination that the pressure sensor should be recalibrated based on the first A difference yields corrected data representing altitude. In some such implementations, the control logic can be further configured to repeat at least one or more of: receiving the location data from the location-determining device, analyzing the location data to determine the wearable biological Metering a first location of the tracking device, determining a first terrain altitude from historical terrain data, wherein the first terrain altitude is an altitude associated with the first location, receiving from the pressure sensor the said data representing altitude, and analyzing said data representing altitude to determine whether the first topographic altitude should be based on said first topographic altitude and (i) calculated from said data representing altitude and (ii) associated with said first location The first difference between altitudes to recalibrate the pressure sensor. In some such implementations, each repetition may be separated from the immediately preceding execution by a set time interval. In some such embodiments, the set time interval may be between 1 second and 24 hours.

In some other or additional implementations of the wearable biometric tracking device, the historical terrain data may be stored within the wearable biometric tracking device.

In some other or additional implementations of the wearable biometric tracking device, the wearable biometric tracking device may further include wireless communication circuitry communicatively coupled with the control logic. The historical terrain data may be obtained via the wireless communication circuit from a secondary source remote from the biometric tracking device.

In some other or additional implementations of the wearable biometric tracking device, the control logic may be further configured to: analyze the first location to determine an update time period; and repeat at least the following based on the update time period Operation: receiving the location data from the location determining device, analyzing the location data to determine a first location of the wearable biometric tracking device, determining a first terrain altitude from historical terrain data, wherein the first a terrain altitude being an altitude associated with said first location, said data indicative of altitude being received from said pressure sensor, and said data indicative of altitude being analyzed to determine if The pressure sensor is recalibrated with a first difference between (i) calculating from the data representing altitude and (ii) an altitude associated with the first location. In some such implementations, analyzing the first location to determine an update time period may include determining that the historical terrain data indicates a second location having a second terrain altitude, determining the first location and the second terrain altitude. Two locations are separated by a first distance, and the second location is determined to be the closest location to the first location, wherein the absolute difference between the first terrain altitude and the second terrain altitude is greater than the first terrain an altitude difference threshold, and determining the update time period based at least in part on the first distance. In some other or additional implementations, analyzing the first location to determine an update time period may include determining from the historical terrain data that the first location is within a first threshold distance of a location of a building. In some such implementations, the control logic may be further configured to stop recalibrating the pressure sensor when the first location is within the first threshold distance of the location of the building.

In some other or additional implementations of the biometric tracking device, the memory may further store program instructions for controlling the one or more processors to: detect the wearable biometric tracking device when a second location has been traveled to at least a first distance away from the first location; and in response to the first distance exceeding a translational distance threshold, repeating at least the following operations: receiving the location data from the location-determining device, analyzing the location data to determine a first location of the wearable biometric tracking device, determining a first terrain altitude from historical terrain data, wherein the first terrain altitude is an altitude associated with the first location , receiving said data representing altitude from said pressure sensor, and analyzing said data representing altitude to determine whether to calculate and (ii) recalibrating the pressure sensor with a first difference between the altitudes associated with the first location. In some such implementations, the translation distance threshold may be a distance between 50 feet and 200 feet. In some other or additional implementations, the translation distance threshold may be a distance between 500 feet and 1000 feet.

In some other or additional implementations of the biometric tracking device, the memory may further store program instructions for controlling the one or more processors to: detect the wearable biometric tracking device when at least a first distance has been traveled; and in response to the first distance exceeding a translational distance threshold, repeating at least the following operations: receiving the position data from the position determining device, analyzing the position data to determine the wearable a first location of the biometric tracking device, determining a first terrain altitude from historical terrain data, wherein the first terrain altitude is an altitude associated with the first location, receiving from the pressure sensor a said data, and analyzing said data representing altitude to determine whether an altitude should be based on said first topographical altitude and (i) calculated from said data representing altitude and (ii) associated with said first location The first difference between the altitudes to recalibrate the pressure sensor.

In some other or additional implementations of the biometric tracking device, the control logic may be further configured to: analyze the data representing altitude over a period of time to determine when there is a A change in altitude of the wearable biometric tracking device by a first threshold altitude change amount while the location data indicates that the wearable biometric tracking device has not traveled more than a first threshold distance away from the first location , and in response to determining that there is an altitude change that exceeds a first threshold altitude change amount when the location data indicates that the wearable biometric tracking device has not traveled beyond a first threshold distance, performing at least the following operations: from the location determining means receives the location data, analyzes the location data to determine a first location of the wearable biometric tracking device, determines a first terrain altitude from historical terrain data, wherein the first terrain altitude is equal to the an altitude associated with said first location, receiving said data indicative of altitude from said pressure sensor, and analyzing said data indicative of altitude to determine whether the first topographical altitude should be based on said first topographical altitude and (i) from said The data calculation of the altitude and (ii) a first difference between the altitude associated with the first location to recalibrate the pressure sensor.

In some other or additional implementations of the biometric tracking device, the biometric tracking device may further comprise an image acquisition interface. The control logic may be further configured to: obtain a first image associated with the data representing altitude, cause the first image to be analyzed to determine an altitude based on the first image, analyze all data representing altitude said data to determine whether the first image-based altitude should be based on a second distance between said first image-based altitude and (i) calculated from said data representing altitude and (ii) said altitude associated with said first location recalibrating the pressure sensor by taking two differences, and when the control logic determines in (i) that the pressure sensor should be recalibrated, recalibrating the pressure sensor based on the second difference. In some such embodiments, the control logic may be further configured to cause the first image to be analyzed by causing the first image to be compared to a plurality of reference images, each reference image to an image-based and receiving a determination that the first reference image having the first image-based altitude satisfies one or more matching criteria with respect to the first image. In some other or additional implementations, the control logic may be further configured to cause the first image to be analyzed by: performing optical character recognition on the first image, extracting from the first image based on first text-based information, and determining the first image-based altitude from the first text-based information. In some such implementations, the control logic may be further configured to determine the first image-based altitude from the first text-based information by extracting the first image-based altitude from the first text-based information. The altitude of the first image. In some other or additional implementations, the control logic may be further configured to determine the first image-based altitude from the first text-based information by causing the first text-based information comparing a plurality of text records in a database, each text record associated with a text-based altitude, receiving a determination that a first text record satisfies one or more matching criteria with respect to the first text-based information, and Using the text-based altitude of the first text record as the first image-based altitude.

In some embodiments, a wearable biometric tracking device comprising: one or more biometric sensors; a pressure sensor configured to detect the wearable biometric tracking ambient atmospheric pressure around the device, and outputting data indicative of altitude, wherein the data indicative of altitude is based at least in part on the air pressure measured by the pressure sensor; determining location from data received by one or more devices wearing a biometric tracking device, and outputting corresponding location data; and control logic wherein said one or more biometric sensors, said pressure sensor, said location determining device, and The control logic is communicatively coupled, and the control logic is configured to: a) receive the location data from the location determining device, b) analyze the location data to determine a location of the wearable biometric tracking device. A first location, c) determining a first topographic altitude from historical topographic data, wherein the first topographic altitude is the altitude associated with the first location, d) receiving from the pressure sensor all data representing the altitude said data representing altitude, e) analyzing said data representing altitude to determine whether an altitude based on said first topographical altitude should be (i) calculated from said data representing altitude and (ii) associated with said first location and f) when the control logic determines in (e) that the pressure sensor should be recalibrated, recalibrate the pressure sensor based on the first difference Pressure Sensor.

In some embodiments, the pressure sensor is a barometric altimeter or a barometric pressure sensor.

In some implementations, the position determining means comprises one or more systems selected from the group consisting of: a satellite-based global positioning system, a Wi-Fi based positioning system, a cell tower location based system, A near field communication tag lookup system, a radio frequency identification tag lookup system, a camera image based lookup system, and a system configured to interrogate a nearby, external paired device for location information.

In some implementations, the control logic is further configured to recalibrate the pressure sensor in response to a determination in e) that the pressure sensor should be recalibrated to generate corrected data representing altitude based on the first difference .

In some implementations, the control logic is further configured to repeat at least (a) through (e) one or more times.

In some implementations, each repetition of (a) through (e) is separated from the immediately preceding performance of (a) through (e) by a set time interval.

In some embodiments, the set time interval is between 1 second and 24 hours.

In some implementations, the historical terrain data is stored within the wearable biometric tracking device.

In some embodiments, the wearable biometric tracking device further includes wireless communication circuitry communicatively coupled to the control logic, wherein the historical terrain data is obtained from an auxiliary source remote from the biometric tracking device via the The above wireless communication circuit is obtained.

In some implementations, the control logic is further configured to: g) analyze the first position to determine an update time period; and h) repeat at least (a) through (e) according to the update time period.

In some embodiments, (g) comprises: determining that the historical terrain data indicates a second location having a second terrain altitude, determining that the first location and the second location are separated by a first distance, determining the first location The second location is the closest location to the first location, wherein the absolute difference between the first terrain altitude and the second terrain altitude is greater than a first terrain altitude difference threshold, and is based at least in part on the The first distance determines the update time period.

In some implementations, (g) includes determining from the historical terrain data that the first location is within a first threshold distance of a location of a building.

In some implementations, the control logic is further configured to at least cease performing (f) when the first location is within the first threshold distance of the location of the building.

In some embodiments, the memory stores further program instructions for controlling the one or more processors to detect when the wearable biometric tracking device has traveled at least far from the first location a second position at a first distance; and repeating at least (a) through (e) in response to the first distance exceeding a translational distance threshold.

In some implementations, the translation distance threshold is a distance between 50 feet and 200 feet.

In some implementations, the memory stores other program instructions for controlling the one or more processors to: detect when the wearable biometric tracking device has traveled at least a first distance; and respond to The first distance exceeds a translation distance threshold for at least repeating (a) through (e).

In some implementations, the translation distance threshold is a distance between 500 feet and 1000 feet.

In some implementations, the control logic is further configured to: i) analyze the data representing altitude over a period of time to determine when there is an excess of the wearable biometric tracking device during the period of time A change in altitude by a first threshold altitude change amount, and the location data indicates that the wearable biometric tracking device has not traveled more than a first threshold distance away from the first location, and i) in response to (i) Instead, at least (a) to (e) are performed.

In some embodiments, the wearable biometric tracking device further includes an image acquisition interface, wherein the control logic is further configured to: g) acquire a first image associated with the data representing altitude, h) causing said first image to be analyzed to determine a first image-based altitude, i) analyzing said data representing an altitude to determine whether said first image-based altitude should be based on and (ii) a second difference between the altitude associated with the first location to recalibrate the pressure sensor, and i) when the control logic determines in (i) when the pressure sensor should be recalibrated, recalibrate the

In some implementations, the control logic is further configured to perform (h) by causing the first image to be compared to a plurality of reference images, each reference image being associated with an image-based altitude, and receiving a determination that the first reference image having the first image-based altitude satisfies one or more matching criteria with respect to the first image.

In some implementations, the control logic is further configured to perform (h) by: performing optical character recognition on the first image, extracting first text-based information from the first image, and extracting first text-based information from the first image The first text-based information determines the first image-based altitude.

In some implementations, the control logic is further configured to determine the first image-based altitude from the first text-based information by extracting the first image-based altitude from the first text-based information. The altitude of the image.

In some implementations, the control logic is further configured to determine the first image-based altitude from the first text-based information by causing the first text-based information to match the comparing a plurality of text records, each text record associated with a text-based altitude, receiving a determination that a first text record satisfies one or more matching criteria with respect to said first text-based information, and using said first text-based The text-based altitude of a text record is used as the first image-based altitude.

In some implementations, a wearable biometric tracking device includes a pressure sensor configured to detect ambient atmospheric pressure around the wearable biometric tracking device, and output an indication data representing altitude, wherein said data representing altitude is based at least in part on air pressure measured by pressure sensor data representing altitude; a motion sensor configured to detect motion of said wearable biometric tracking device, and outputting corresponding motion data; and control logic, wherein the pressure sensor, the motion sensor, and the control logic are communicatively coupled, and the control logic is configured to: (a) receive the data representing altitude , (b) receiving said motion data, (c) determining when said data representing altitude and said motion data are in combination associated with a first arm motion profile of a plurality of arm motion profiles, and (d) responding The determining in (c) stores data associated with the first arm movement profile.

In some implementations, the wearable biometric tracking device is configured to be worn in a location selected from the group consisting of: a human arm, a human forearm, a human hand, and a human finger.

In some embodiments, the pressure sensor is a barometric altimeter.

In some implementations, the motion sensor comprises one or more sensors selected from the group consisting of accelerometers, gyroscopes, magnetometers, piezoelectric sensors, electromagnetic trackers, and camera-based imaging sensors.

In some embodiments, the plurality of arm movement profiles comprises one or more arm movement profiles approximately representing arm movements of one or more activities, each activity selected from the group consisting of: running, walking , elliptical workouts, resistance training workouts, pull-ups, push-ups, sit-ups, skipping rope, and cardio dance.

In some implementations, the control logic is further configured to: determine a first arm motion profile associated with the first arm motion profile, and determine the first arm motion profile associated with the first arm motion profile The degree of deviation between the profile's associated first reference motion profile.

In some implementations, both the first arm movement profile and the first reference motion profile are associated with one or more gestures associated with common actions selected from the group consisting of: swinging a baseball bat; swinging a softball bat; swinging a tennis racket; swinging a badminton racket; swinging a squash racket; swinging an American squash racket; swinging a golf wood; swinging a golf iron; swinging a golf wedge; swinging a golf putter; swinging golf chipping; swinging a golf hybrid; performing resistance training exercises; performing martial arts movements; performing gymnastics movements; performing yoga exercises; hitting a ball in pool sports such as pool; hitting a ball in pool sports such as snooker ; hitting a ball in sports such as pool; swimming; bowling; shooting with a rifle; shooting with a pistol; stabbing with a knife; brandishing a knife; and parrying with a knife.

In some embodiments, the wearable biometric tracking device further comprises an interface device communicatively coupled to the control logic, wherein the control logic is further configured to: h) determine when the degree of deviation exceeds a deviation and i) in response to a determination in (h) that the degree of deviation exceeds the first threshold amount, causing the interface device to provide an indication that the degree of deviation exceeds the first threshold amount, wherein A) said interface device comprises one or more items selected from the group consisting of: a display, one or more lights, a vibrating motor, and an audio device; and B) said indication comprises one or more items selected from the group consisting of One or more items of the group consisting of: numerical score, vibration, sound, graphic, icon, animation, message indicating fatigue, message warning the wearer to slow down and message warning said wearer to take a break.

In some embodiments, the first arm movement profile is associated with a resistance training curl-type activity, and the motion data and the data representing altitude comprise one instance or two or more substantially consecutive In an example, the motion data and the data indicative of altitude are associated with the first arm movement profile, wherein: the motion data indicates that the forearm wearing the wearable biometric tracking device has changed from a downwardly inclined orientation transitioning to an upwardly inclined orientation, the motion data indicating that the forearm on which the wearable biometric tracking device is worn transitions from the upwardly inclined orientation to during a second time period subsequent to the first time period Oriented at a downward slope, and the data representing altitude indicates that the wearable biometric tracking device experienced an increase in altitude of between 1 ft and 2 ft during the first time period.

In some implementations, for one example, the motion data and the data indicative of altitude are associated with the first arm movement profile.

In some embodiments, the number of instances is selected from the group consisting of: i) 1 to 5 instances, ii) 5 to 8 instances, iii) 8 to 10 instances, iv) 10 to 12 instances , v) 12 to 15 examples and vi) 15 to 20 examples.

In some embodiments, the range of said numbers (i) to (vi) of instances is based on indicating whether the wearer of said wearable biometric tracking device is for the purpose of being selected from the group respectively consisting of Data for resistance training were selected for: I) building strength, II) building strength and muscle mass substantially equally, III) building muscle mass with an emphasis on strength, IV) building muscle mass with an emphasis on endurance, V) Enhance endurance, focus on muscle and VI) mainly enhance endurance.

In some embodiments, the first arm movement profile is associated with a resistance training deadlift-type activity, and the motion data and the data representing altitude comprise one instance or two or more substantially consecutive In an example, the motion data and the data representing altitude are associated with the first arm movement profile, wherein: the data representing altitude indicates that the wearable biometric tracking device experienced during a first time period an increase in altitude between 1.5ft and 2ft, the data representing altitude indicates that the wearable biometric tracking device experienced a decrease in altitude between 1.5ft and 2ft during the second time period, and The motion data indicates that a forearm wearing the wearable biometric tracking device maintained a substantially vertical and downward orientation during substantially all of the first time period and the second time period.

In some embodiments, the first arm movement profile is associated with a resistance training squat or bench press type activity, and the motion data and the data representing altitude include one instance or two or more In substantially consecutive instances, the motion data and the data indicative of altitude are associated with the first arm movement profile, wherein: the data indicative of altitude indicates that the wearable biometric tracking device was at a first time experiencing an increase in altitude between 0.5ft and 2ft during the period, the data representing altitude indicating that the wearable biometric tracking device experienced an increase in altitude between 0.5ft and 2ft during the second time period is lowered, and the motion data indicates that the forearm on which the wearable biometric tracking device is worn maintains a substantially vertical and upward orientation during substantially all of the first time period and the second time period.

In some embodiments, said first arm movement profile is associated with a pre-resistance training raise or side raise type activity, and said data representing altitude in said motion data includes one instance or two or both For more than one substantially consecutive instance, the motion data and the data representing altitude are associated with the first arm movement profile, wherein: the data representing altitude is indicative of the wearable biometric tracking device at experiencing an increase in altitude between 1 ft and 2 ft during a time period, the data representing altitude indicating that the wearable biometric tracking device experienced an increase in altitude between 1 ft and 2 ft during a second time period The motion data indicates that the forearm wearing the wearable biometric tracking device transitions from a substantially vertical and downward orientation at the beginning of the first time period to a substantially vertical orientation at the end of the first time period. a horizontal orientation, and the motion data indicates that the forearm wearing the wearable biometric tracking device transitions from a substantially horizontal orientation at the beginning of the second time period to a substantially horizontal orientation at the end of the second time period. Up vertical and down orientation.

In some embodiments, the first arm movement profile is associated with a resistance training morning-type activity, and the motion data and the data representing altitude comprise one instance or two or more substantially consecutive In an example, the motion data and the data representing altitude are associated with the first arm movement profile, wherein: the data representing altitude indicates that the wearable biometric tracking device experienced during a first time period An increase in altitude between 1.5ft and 2ft, the data representing altitude indicates that the wearable biometric tracking device experienced a decrease in altitude between 1.5ft and 2ft during the second time period, so the motion data indicates that a forearm wearing the wearable biometric tracking device transitions from a substantially horizontal orientation at the beginning of the first time period to a substantially vertical and upward orientation at the end of the first time period, and The motion data indicates that the forearm wearing the wearable biometric tracking device transitions from a substantially vertical and upward orientation at the beginning of the second time period to a substantially horizontal orientation at the end of the second time period orientation.

In some embodiments, the first arm movement profile is associated with elliptical machine activity, and the motion data and the data indicative of altitude are associated with the first arm movement profile when: indicative of altitude said data of altitude exhibits cyclic behavior during a first period of time, with small altitude changes during each cycle commensurate with altitude changes experienced by handles of the elliptical machine during normal use, said motion data exhibiting cyclic behavior behavior having substantially the same frequency as the cyclic behavior of the data representing altitude during the first time period, and the motion data includes acceleration data, and the acceleration data indicates less than otherwise An acceleration magnitude during the first time period of the acceleration magnitude used by the control logic to determine whether the person wearing the wearable biometric tracking device should be attributed to engaging the person's foot The pedometer is incremented by acceleration data from normal walking or running activities off the ground.

In some embodiments, the wearable biometric tracking device further includes a display communicatively coupled to the control logic, wherein: I) the first arm movement profile is associated with watch-watching activity; II) the The motion data and the data representing altitude are associated with the first arm movement profile when the data representing altitude indicates that the wearable biometric tracking device experienced approximately an increase in altitude of 1 ft, and the motion data indicates that a forearm wearing the wearable biometric tracking device transitions from a substantially vertical and downward orientation to a substantially horizontal orientation during the first time period; and III) The control logic is further configured to cause the display to present clock-related data in response to a determination in (c) that the motion data and the data representing altitude are associated with the watch-watching activity first movement profile.

In some implementations, the motion data and the data indicative of altitude are when the motion data additionally indicates that the forearm on which the wearable biometric tracking device is worn was at the first time period immediately following the first time period. Remaining in the substantially horizontal orientation for two time periods is associated with the first arm movement profile, wherein the second time period has a non-zero duration.

In some implementations, the plurality of arm movement profiles includes an arm movement profile that approximates a user at rest, and the control logic is further configured to: p) track the first arm movement profile substantially similar to that of the user a duration of a resting arm movement profile; and q) determining that the duration for which the first arm movement profile is substantially similar to the user's resting arm movement profile is greater than a rest duration threshold.

In some implementations, a wearable biometric tracking device includes: a pressure sensor configured to detect an altitude of the wearable biometric tracking device, and to output corresponding data representing the altitude; and a motion sensor, the motion a sensor configured to detect motion of the wearable biometric tracking device, and to output corresponding motion data; and control logic, wherein the biometric monitoring device, the pressure sensor, and the control logic are communicatively coupled, and the control logic The logic is configured to: a) determine when the data representing an altitude indicates that the wearable biometric tracking device has not experienced an altitude change rate that exceeds a first altitude rate change threshold during a first time period, and b ) at least in response to determining in (a) that the data representing altitude indicates that the wearable biometric tracking device has not experienced an altitude change that exceeds the first altitude change rate threshold during the first time period rate, placing the wearable biometric tracking device in a lower power consumption state during a second period of time subsequent to the first period of time.

In some implementations, the control logic is further configured to: c) determine when the motion data indicates that the wearable biometric tracking device experienced an impact event immediately before the first time period, and d) By except in response to determining in (a) that the data representing altitude indicates that the wearable biometric tracking device has not experienced an altitude change that exceeds the first altitude change rate threshold during the first time period In addition to the rate, placing the wearable biometric tracking device in response to determining in (c) that the motion data indicates that the wearable biometric tracking device experienced an impact event immediately before the first time period Performing b) in the lower power consumption state.

In some implementations, the control logic is further configured to: c) determine when the data representing an altitude indicates that the wearable biometric tracking device experienced more than a second altitude during the second time period an altitude change rate of a rate-of-change threshold, and d) in response to determining in (c) that the data representing altitude indicates that the wearable biometric tracking device experienced more than the second time period during the second time period. An altitude rate of change threshold that causes the wearable biometric tracking device to exit the lower power consumption state.

In some implementations, the control logic is further configured to: e) in response to determining in (c) that the data indicative of altitude indicates that the wearable biometric tracking device experienced during the second time period An altitude change rate exceeding the second altitude change rate threshold causes a display of the wearable biometric tracking device to transition from an off or inactive state to an on state and display a message.

In some implementations, the wearable biometric tracking device includes: one or more biometric sensors; an altitude sensor configured to detect an altitude of the wearable biometric tracking device, and outputting altitude sensor data; and control logic, wherein the one or more biometric sensors, the altitude sensor, and the control logic are communicatively coupled, and the control logic is configured to: a) receive the altitude sensor data, b) analyzing the altitude sensor data to determine whether the altitude sensor data is indicative of flying, and c) at least in part responsive to the altitude sensor data being indicative of flying in (b) Determining, placing the wearable biometric tracking device in a mode associated with air travel.

In some embodiments, the altitude sensor data is indicative of flying when the altitude sensor data indicates an altitude rate of change that exceeds an altitude rate of change threshold.

In some embodiments, the altitude change threshold is greater than 500 feet per minute.

In some implementations, when the altitude sensor data indicates a total altitude increase over an altitude increase time period of the wearable biometric tracking device, the altitude sensor data indicates flying, and at The total altitude increase within the altitude increase time period exceeds a total altitude increase threshold.

In some embodiments, the total altitude increase threshold is at least 1500 feet above sea level, and the altitude increase time period is 180 seconds or less.

In some embodiments, the altitude sensor data is indicative of flying an airplane when: the altitude sensor data indicates an altitude change rate that exceeds an altitude change rate threshold throughout an altitude change time period, and The altitude rate threshold is at least 500 feet per minute.

In some embodiments, the altitude change time period is a time period of at least 60 seconds.

In some implementations, the mode associated with air travel is a mode that places the wearable biometric tracking device in a low power state.

In some implementations, the wearable biometric tracking device further includes wireless communication circuitry communicatively coupled to the control logic and configured to communicate wirelessly with an associated device, wherein an aeronautical The mode associated with travel is a mode that places the communication circuit in an inactive or off state.

In some implementations, the control logic is further configured to: d) further analyze the altitude sensor data to determine whether the altitude sensor data indicates a landing of the aircraft, and e) respond at least in part to (d ), the determination that the altitude sensor data is indicative of landing of the aircraft causes the wearable biometric tracking device to exit the mode associated with air travel.

In some embodiments, the altitude sensor data is indicative of the landing of the aircraft when: f) the altitude sensor data is indicative of all altitude decrease thresholds that exceed a total altitude decrease threshold during the altitude decrease time period. the total altitude of the wearable biometric tracking device decreases; and g) the altitude sensor data following (f) indicates an altitude change rate of less than 50 feet per minute over a period of 300 seconds.

In some embodiments, the altitude reduction time period is 180 seconds or less.

In some implementations, the control logic is further configured to: h) receive biometric data from the one or more biometric sensors; i) analyze the biometric data to determine whether the biometric data is indicative of the the wearer of the wearable biometric tracking device has taken at least a predetermined number of steps; and i) when the control logic further detects (i) after (f), it is determined that the altitude sensor data indicates all Said landing.

In some embodiments, the total altitude decrease threshold is an altitude decrease of at least 1500 feet.

In some implementations, the control logic is further configured to monitor biometric data from the one or more biometric sensors, and after (b) determine when the biometric data is indicative of a biometric monitoring device The wearer has not moved more than a threshold amount of movement relative to the aircraft frame of reference within the first period of time.

In some implementations, the wearable biometric tracking device further includes a user interface, wherein the control logic is further configured to respond, at least in part, to the wearer of the biometric monitoring device during the first time period The determination that the vehicle has not moved relative to the aircraft reference frame beyond the threshold amount of movement causes a notification to be provided to the wearer via the user interface.

In some implementations, the wearable biometric tracking device further includes a light sensor configured to measure an amount of ambient light around the biometric tracking device and output detected light data, wherein the control logic is further configured to configured to: analyze the detected light data to determine whether the detected light data is indicative of a light level typically associated with a sleeping environment in an aircraft, and respond to the wearer of the biometric monitoring device in the The determination that the threshold amount of movement has not been moved relative to the aircraft frame of reference for a first period of time, and in response to the detected light data not indicative of the The determination of the light level causes the notification to be provided to the wearer via the user interface.

In some implementations, the control logic is further configured to: place the wearable biometric tracking device in a sleep tracking mode in response to a motivating factor selected from the group consisting of: (i) the an intentional request provided by said wearer of a wearable biometric tracking device and (ii) an analysis of said biometric data indicating that said wearer of said wearable biometric tracking device may be asleep; when the biometric tracking device is not in the sleep tracking mode and in response to the wearer of the biometric monitoring device having not moved relative to the aircraft reference frame by more than the threshold amount of movement within the first period of time The determination causes the notification to be provided to the wearer via the user interface.

In some implementations, the user interface includes a digital display, and the notification is a message displayed on the digital display.

In some implementations, the user interface includes an audio device, and the notification is an audio notification.

In some implementations, the user interface includes a tactile feedback mechanism and the notification is a tactile notification.

In some implementations, the wearable biometric tracking device further includes an audio sensor configured to output audio data, wherein the control logic is further configured to: receive the audio data; and analyze the audio data to determine whether the audio data is indicative of background noise consistent with engine noise of the aircraft, wherein the control logic is further configured to respond at least in part to the determination in (b) that the altitude sensor data indicates that the aircraft is flying and The audio data is indicative of the determination of background noise consistent with the engine noise of the aircraft, placing the wearable biometric monitoring device in the mode associated with air travel.

In some implementations, the wearable biometric tracking device further includes an acceleration sensor configured to detect acceleration and output acceleration data, wherein the control logic is further configured to: receive the acceleration data; and analyze the acceleration data to determine whether the acceleration data indicates vibrations consistent with vibrations from an aircraft engine, wherein the control logic is further configured to be at least partially responsive to the determination in (b) that the altitude data indicates that the aircraft is flying and The determination that the acceleration data is indicative of vibrations consistent with the vibrations of the aircraft engine places the wearable biometric monitoring device in the mode associated with air travel.

In some implementations, the wearable biometric tracking device further includes a communication interface configured to receive communication data, wherein the control logic is further configured to: receive the communication data; and analyze the communication data to determine whether the communication data indicates communications consistent with communications from an aircraft communication device, wherein the control logic is further configured to respond at least in part to the determination in (b) that the altitude sensor data indicates that the aircraft is flying and also in response to said determination that said communication data indicates a communication consistent with said communication from said aircraft communication device, placing said wearable biometric monitoring device in said mode associated with air travel .

In some implementations, the control logic is further configured to: provide a stair climbing mode that tracks the number of flights of stairs climbed by a wearer of the wearable biometric tracking device, and turn off all stairs in response to (c). Describe the stair climbing mode.

In some implementations, the wearable biometric tracking device further includes a wireless communication interface, wherein the control logic is further configured to: receive a first input signal via the wireless communication interface, and respond to the first input The receipt of a signal causes the wearable biometric tracking device to enter the mode associated with air travel.

In some implementations, the control logic is further configured to cause the wearable biometric tracking device to leave the mode associated with air travel upon receiving a second input signal, wherein the second input signal Indicating an activity selected from the group consisting of: powering on an associated external device, touching the wearable biometric tracking device, selecting a menu option via a user interface of the wearable biometric tracking device, and pressing the button of the wearable biometric monitoring device described above.

In some embodiments, a system includes: a wearable biometric tracking device comprising: one or more biometric sensors, a biometric tracking device communication interface, and biometric tracking device control logic, wherein the one or more a biometric sensor, the biometric tracking device communication interface, and the biometric tracking device control logic are communicatively coupled, and the biometric tracking device control logic is configured to: a) receive a signal associated with an aircraft mode ; b) responsive to (a) placing the wearable biometric tracking device in a mode associated with air travel; and a mobile communication device separate from the wearable biometric tracking device, the mobile communication device comprising : a mobile communication device communication interface, and a mobile communication device control logic, wherein the mobile communication device communication interface and the mobile communication device control logic are communicatively coupled, and the mobile communication device control logic is configured to: via the The mobile communication device communication interface transmits the signal associated with the aircraft mode.

Description of drawings

Various implementations disclosed herein are illustrated by way of example and not limitation in the figures of the drawings, where like reference numerals may refer to like elements.

1 illustrates an example portable monitoring device that enables user interaction via a user interface.

2A illustrates an example portable monitoring device that may be fastened to a user via the use of straps.

2B provides a view of the example portable monitoring device of FIG. 2A showing a skin-facing portion of the device.

Figure 2C provides a cross-sectional view of the portable monitoring device of Figure 2A.

3A provides a cross-sectional view of a sensor protrusion of an example portable monitoring device.

3B depicts a cross-sectional view of a sensor protrusion of an example portable monitoring device; this protrusion is similar to that presented in FIG. 3A except that the light source and photodetector are placed on a flat and/or rigid PCB.

Figure 3C provides another cross-sectional view of an example PPG sensor embodiment.

Figure 4A illustrates an example of a potential PPG light source and photodetector geometry.

4B and 4C illustrate an example of a PPG sensor with a photodetector and two LED light sources.

Figure 5 illustrates an example of an optimized PPG detector with a protrusion with curved sides to avoid discomfort to the user.

6A illustrates an example of a portable monitoring device with a strap; optical sensors and light emitters can be placed on the strap.

6B illustrates an example of a portable biometric monitoring device with a display and wristband. Additionally, an optical PPG (eg, heart rate) detection sensor and/or transmitter may be located on the side of the biometric monitoring device. In one embodiment, each of these may be located in a side mounted button.

7 depicts a user pressing the side of a portable biometric monitoring device to take a heart rate measurement from a side mounted optical heart rate detection sensor. The display of the biometric monitoring device may show whether a heart rate has been detected and/or display the user's heart rate.

8 illustrates the functionality of an example biometric monitoring device smart alert feature.

9 illustrates an example of a portable biometric monitoring device that changes the way it detects a user's heart rate based on the degree of movement experienced by the biometric monitoring device.

10 illustrates an example of a portable biometric monitoring device with a bicycle application on it that can display bicycle speed and/or pedaling cadence, among other metrics.

11A illustrates an example block diagram of a PPG sensor with light source, light detector, ADC, processor, DAC/GPIO, and light source intensity and on/off controls.

11B illustrates an example block diagram of a PPG sensor similar to that of FIG. 11A , additionally using sample and hold circuitry and analog signal conditioning.

11C illustrates an example block diagram of a PPG sensor similar to that of FIG. 11A , additionally using a sample and hold circuit.

11D illustrates an example block diagram of a PPG sensor with multiple switchable light sources and detectors, light source intensity/switch controls, and signal conditioning circuitry.

11E illustrates an example block diagram of a PPG sensor using synchronous detection. To perform this type of PPG detection it has a demodulator.

11F illustrates an example block diagram of a PPG sensor having a differential amplifier in addition to the features of the sensor illustrated in FIG. 11A.

11G illustrates an example block diagram of a PPG sensor having the features of the PPG sensors shown in FIGS. 11A-KKF.

12A illustrates an example of a portable biometric monitoring device with a heart rate or PPG sensor, motion sensor, display, vibration motor, and communication circuitry connected to a processor.

12B illustrates an example of a portable biometric monitoring device with a heart rate or PPG sensor, motion sensor, display, vibration motor, position sensor, altitude sensor, skin conductivity/humidity sensor, and communication circuitry connected to a processor.

12C illustrates an example of a portable biometric monitoring device having physiological sensors, environmental sensors, and position sensors connected to a processor.

13A illustrates an example of measuring heart rate using motion signals and optical PPG signals.

13B illustrates another example of measuring heart rate using motion signals and optical PPG signals.

Figure 14A illustrates an example of a sensor with an analog connection to a sensor processor.

Figure 14B illustrates an example of a sensor with an analog connection to a sensor processor, which in turn has a digital connection to an application processor.

14C illustrates an example of a sensor device having one or more sensors connected to an application processor.

Figure 14D illustrates an example of a sensor device having one or more sensors connected to a sensor processor, which in turn is connected to an application processor.

Figure 15A illustrates an example of a swim detection algorithm using a sequential algorithm flow.

Figure 15B illustrates an example of a swim detection algorithm using a parallel algorithm flow.

Figure 15C illustrates an example of a swim detection algorithm using a mix of sequential and parallel algorithm flows.

Figure 15D illustrates an example of a swim detection algorithm using a mix of sequential and parallel algorithm flows.

16A illustrates an example schematic of a sample and hold circuit and differential/instrumentation amplifier that may be used for PPG sensing.

16B illustrates an example schematic of a circuit for a PPG sensor using a controlled current source to compensate for "bias" current prior to a transimpedance amplifier.

16C illustrates an example schematic of a circuit for a PPG sensor using a sample and hold circuit for current feedback applied to a photodiode (before the transimpedance amplifier).

16D illustrates an example schematic of a circuit for a PPG sensor using a differential/instrumentation amplifier with ambient light cancellation functionality.

16E illustrates an example schematic of a circuit for a PPG sensor that uses a photodiode to compensate the current dynamically generated by a DAC.

16F illustrates an example schematic diagram of a circuit for a PPG sensor that uses a photodiode to compensate current dynamically generated by a controlled voltage source.

16G illustrates an example schematic of a circuit for a PPG sensor including ambient light removal functionality using a "switched capacitor" approach.

16H illustrates an example schematic of a circuit for a PPG sensor that uses a photodiode to compensate the current generated by a constant current source (this can also be done using a constant voltage source and a resistor).

16I illustrates an example schematic of a circuit for a PPG sensor including ambient light removal functionality and differentiation between consecutive samples.

16J illustrates an example schematic of a circuit for ambient light removal and differentiation between consecutive samples.

17 depicts a simplified block diagram of a biometric monitoring device with an altimeter and position determination system.

18 depicts a flow diagram of a technique for recalibrating pressure sensors based on terrain data.

19 depicts a flow diagram of a technique for recalibrating a pressure sensor based on terrain data obtained from a remote device.

Figure 20 shows a plot of hypothetical pressure or altitude versus time.

Figure 21A illustrates aspects of one technique for determining terrain altitude from historical terrain data.

FIG. 21B illustrates aspects of another technique for determining terrain altitude from historical terrain data.

Figure 21C illustrates aspects of yet another technique for determining terrain altitude from historical terrain data.

22 depicts a flow diagram of a technique for recalibrating a pressure sensor based on a change in XY position.

23 depicts a flow diagram for a technique for recalibrating a pressure sensor depending on its proximity to a building.

24 depicts a flowchart of another technique for recalibrating a pressure sensor based on a change in XY position.

25 depicts a flow diagram of a technique for recalibrating a pressure sensor based on slope.

Figure 26 depicts taking a photograph of an altitude information sign and corresponding identification of keywords and altitude data from the sign.

27 depicts a simplified block diagram of a biometric monitoring device with an altimeter and one or more motion sensors.

28 depicts a flow diagram of a technique for using altitude data and motion data from a biometric monitoring device to recognize gestures made by a person wearing the biometric monitoring device.

Figure 29 depicts a biometric monitoring device worn on a person's forearm and a coordinate system for the biometric monitoring device acceleration sensor; reference is made to this coordinate system with respect to Figures 30 through ZU.

30 depicts examples of postures associated with a bench press-type resistance training exercise and hypothetical motion and altitude data that may be used to identify the postures.

31 depicts examples of poses associated with side lift-type resistance training exercises and hypothetical motion and altitude data that may be used to identify the poses.

32 depicts an example of a pose associated with a deadlift-type resistance training exercise and hypothetical motion and altitude data that may be used to identify the pose.

33 depicts examples of poses associated with a curl-type resistance training exercise and hypothetical motion and altitude data that may be used to identify the poses.

34 depicts examples of poses associated with a good morning resistance training workout and hypothetical motion and altitude data that can be used to identify the poses.

35 depicts an example of hypothetical motion and altitude data that may be used to differentiate between postures associated with walking and postures associated with using an elliptical trainer.

36 shows a flowchart detailing an example algorithm used by a biometric tracking device to determine whether to place the biometric tracking device in a mode associated with airplane travel.

37 shows a flowchart detailing an example algorithm used by a biometric tracking device to determine whether to exit a mode associated with airplane travel.

38 shows a flowchart detailing an example algorithm used by a biometric tracking device to determine whether to first place the biometric tracking device in a mode associated with air travel and then determine whether to exit the mode associated with air travel.

39A is an example graph showing equivalent air pressure inside a pressurized compartment of an example aircraft for takeoff compared to unpressurized air pressure at the same altitude.

39B is an example graph illustrating altitude sensor data from various example situations.

40 shows a flowchart detailing an example algorithm used by a biometric tracking device to determine whether to notify a user of user inactivity for a period greater than the inactivity time period.

Detailed ways

The present invention is directed to biometric monitoring devices (which may also be referred to herein and in any references incorporated by reference as "biometric tracking devices," "personal health monitoring devices," "portable monitoring devices," "portable "Biometric Monitoring Device", "Biometric Monitoring Device", etc.), which may generally be described as wearable devices, typically of small size, designed to be worn relatively continuously by a person. When worn, such biometric monitoring devices collect data regarding activities performed by the wearer or the wearer's physiological state. This data may include data representing the surrounding environment around the wearer or the wearer's interaction with the environment, for example, motion data about the wearer's movement, ambient light, ambient noise, air quality, etc., and by measuring the wearer's various Physiological data obtained from various physiological characteristics, such as heart rate, perspiration level, etc.

As mentioned above, biometric monitoring devices are typically small in size to be unobtrusive to the wearer. Fitbit offers several biometric monitoring devices that are all very small and very light, for example, the Fitbit Flex is a wristband with an insertable biometric monitoring device that is approximately 0.5 inches wide by 1.3 inches long by 0.25 inches thick . Biometric monitoring devices are typically designed to be worn for extended periods of time without discomfort and without interfering with normal daily activities.

In some cases, the biometric monitoring device may utilize other devices external to the biometric monitoring device, for example, an external heart rate monitor in the form of an EKG sensor on a chest strap may be available to obtain heart rate data, or a GPS receiver in a smartphone Available to obtain location data. In such cases, the biometric monitoring device may communicate with these external devices using wired or wireless communication connections. The concepts disclosed and discussed herein are applicable to stand-alone biometric monitoring devices as well as biometric monitoring utilizing sensors or functionality provided in external devices (e.g., external sensors, sensors or functionality provided by a smartphone, etc.) device both.

In general, the concepts discussed herein may be implemented in stand-alone biometric monitoring devices as well as biometric monitoring devices utilizing external devices, where appropriate.

It should be understood that although the concepts and discussions contained herein are presented in the context of a biometric monitoring device, the concepts may equally apply in other contexts if appropriate hardware is available. For example, many modern smartphones include motion sensors such as accelerometers, which are often included in biometric monitoring devices, and if appropriate hardware is available in the device, the concepts discussed herein can be implemented in the device. In effect, this can be seen as turning the smartphone into some form of biometric monitoring device (but one that is larger than typical biometric monitoring devices and may not be worn in the same way). Such embodiments are also understood to be within the scope of the present invention.

The functionality discussed herein can be provided using a number of different approaches. For example, in some implementations, a processor may be controlled by computer-executable instructions stored in memory to provide functionality as described herein. In other implementations, this functionality may be provided in the form of a circuit. In yet other implementations, this functionality may be provided by one or more processors controlled by computer-executable instructions stored in memory coupled with one or more specially designed circuits. Various examples of hardware that can be used to implement the concepts outlined herein include, but are not limited to, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and memories storing executable instructions for controlling general-purpose microprocessors. coupled general purpose microprocessor.

Standalone biometric monitoring devices are available in several form factors and can be designed to be worn in a variety of ways. In some embodiments, a biometric monitoring device may be designed to be insertable into one wearable housing or to be insertable into multiple different wearable housings, e.g., a wristband housing, a belt clip housing -clip case), a pendant housing, a housing configured to attach to a piece of exercise equipment such as a bicycle, etc. Such embodiments are described in more detail, for example, in US Patent Application Serial No. 14/029,764, filed September 17, 2013, which is hereby incorporated by reference for this purpose. In other embodiments, a biometric monitoring device may be designed to be worn in only one way, for example, a biometric monitoring device that is non-removably integrated into a wristband may be intended to be worn only on a person's wrist (or possibly ankle) )superior.

Portable biometric monitoring devices according to embodiments and implementations described herein may have a shape and size suitable for coupling to (eg, fastened to, worn, supported, etc.) a user's body or clothing. An example of a portable biometric monitoring device is shown in FIG. 1; the example portable monitoring device may have a user interface, processor, biometric sensors, memory, environmental sensors, and/or wireless transceivers that can communicate with clients and/or servers. device. An example of a wrist-worn portable biometric monitoring device is shown in Figures 2A-2C. The device may have a display, buttons, electronics packaging, and/or an attachment strap. The attachment strap may be fastened to the user through the use of hook and loop (eg, Velcro), snaps, and/or a strap with memory in its shape (eg, through the use of a spring metal strap). In Fig. 2B, the sensor protrusions and recesses for mating with the charger and/or data transmission cable can be seen. In FIG. 2C , a cross-section through the electronics package is shown. Noteworthy are the sensor protrusions, the main PCB, and the display.

The portable biometric monitoring device can collect one or more types of physiological and/or environmental data from embedded sensors and/or external devices, and communicate or relay this information to other devices, including data that can serve as an Internet-accessible device. source device), thus allowing the collected data to be viewed, for example, using a web browser or web-based application. For example, when a user is wearing a biometric monitoring device, the biometric monitoring device may use one or more biometric sensors to count and store the user's step count. The biometric monitoring device may then transmit data representing the user's step count to an account on a web service (e.g., www.fitbit.com), computer, mobile phone, or health kiosk where the data may be stored, processed, and observed by the user . Indeed, the biometric monitoring device may measure or calculate a number of other physiological metrics in addition to or instead of the user's step count. These physiological measures include, but are not limited to, energy expenditure, such as calorie burn, floors climbed and/or descended, heart rate, heart rate variability, heart rate recovery, position and/or heading (e.g., via GPS, GLONASS or similar systems) , ascent, walking speed and/or distance traveled, swim lap count, stroke type and detected count, bike distance and/or speed, blood pressure, blood glucose, skin conduction, skin and/or body temperature, via electromyography Measured muscle state, brain activity measured by EEG, body weight, body fat, calorie intake, nutrient intake from food, medication intake, sleep cycles such as clock time, sleep stages, sleep quality and/or Or duration, pH level, hydration level, respiration rate, and other physiological measures. The biometric monitoring device may also measure or calculate metrics related to the environment around the user, such as barometric pressure, weather conditions (e.g., temperature, humidity, pollen count, air quality, rain/snow conditions, wind speed), light exposure (e.g., ambient light, UV light exposure, time and/or duration spent in darkness), noise exposure, radiation exposure, and magnetic fields. Additionally, a biometric monitoring device or system that collects a data stream from a biometric monitoring device can calculate metrics derived from this data. For example, a device or system may calculate a user's stress and/or relaxation level through a combination of heart rate variability, skin conduction, noise pollution, and sleep quality. In another example, a device or system may determine the efficacy of a medical intervention (eg, a drug) via a combination of drug intake, sleep data, and/or activity data. In yet another example, a biometric monitoring device or system may determine the efficacy of an allergy medication via a combination of pollen data, medication intake, sleep, and/or activity data. These examples are provided for illustration only and are not intended to be limiting or exhaustive. Further examples and implementations of sensor devices can be found in U.S. Patent Application 13/156,304, entitled "Portable Biometric Monitoring Devices and Methods of Operating Same," filed June 8, 2011 and US Patent Application 61/680,230, entitled "Fitbit Tracker," filed August 6, 2012, both of which are hereby incorporated by reference in their entirety.

physiological sensor

Biometric monitoring devices discussed herein may use one, some, or all of the following sensors to acquire physiological data, including but not limited to those outlined in the table below. All combinations and permutations of physiological sensors and/or physiological data are intended to fall within the scope of the present invention. A biometric monitoring device may include, but is not limited to, the types of one, some, or all of the sensors specified below for obtaining corresponding physiological data; indeed, other types of sensors may also or alternatively be used to obtain corresponding physiological data, and such other Sensors of the type are also intended to fall within the scope of the present invention. Furthermore, a biometric monitoring device may derive physiological data from corresponding sensor output data, but is not limited to the number or type of physiological data it may derive from said sensors.

In one example embodiment, a biometric monitoring device may include optical sensors to detect, sense, sample, and/or generate data that may be used to determine information indicative of, for example, a user's stress (or its level), blood pressure, and/or heart rate. (See, eg, FIGS. 2A to 3C and 11A to KKG). In such embodiments, the biometric monitoring device may include an optical sensor having one or more light sources (LEDs, lasers, etc.) to emit or output light to the user's body, and light detectors (photodiodes, phototransistors , etc.) to sample, measure, and/or detect the response or reflection of this light from the user's body and provide an indication for determining the user's tension (or its level), blood pressure, and/or heart rate (e.g., such as by using a photoplethysmogram) the data of the data.

In one example embodiment, a user's heart rate measurement may be triggered by criteria determined by one or more sensors (or processing circuitry connected thereto). For example, a biometric monitoring device may trigger, acquire, and/or obtain heart rate measurements or data when data from a motion sensor indicates periods of stillness or little motion. (See, eg, Figures 9, 12A and 12B).

12A illustrates an example of a portable biometric monitoring device with a heart rate or PPG sensor, motion sensor, display, vibration motor, and communication circuitry connected to a processor.

12B illustrates an example of a portable biometric monitoring device with a heart rate or PPG sensor, motion sensor, display, vibration motor, position sensor, altitude sensor, skin conductivity/humidity sensor, and communication circuitry connected to a processor.

In one embodiment, when a motion sensor indicates user activity or motion (e.g., is inappropriate or not optimal for triggering, capturing, and/or obtaining desired heart rate measurements or data (e.g., to determine the user's resting heart rate) During optimal exercise), biometric monitoring devices and/or sensors used to acquire and/or obtain desired heart rate measurements or data may be placed or maintained in a low power state. Because heart rate measurements taken during exercise may be less reliable and may be corrupted by motion artifacts, it may be desirable to reduce the frequency at which heart rate data samples are collected (and thus reduce power usage) when the biometric monitoring device is in motion.

In another embodiment, the biometric monitoring device may use data indicative of user activity or motion (e.g., from one or more motion sensors) to adjust or modify the triggering, acquisition, and/or characteristics of obtaining desired heart rate measurements or data (eg, to improve robustness to motion artifacts). For example, if the biometric monitoring device receives data indicative of user activity or motion, the biometric monitoring device may adjust or modify the sampling rate and/or resolution mode of the sensors used to acquire heart rate data (e.g. Where a certain threshold is exceeded, the biometric monitoring device may increase the sampling rate and/or increase the sampling resolution mode of the sensor used to obtain heart rate measurements or data). Furthermore, the biometric monitoring device may adjust or modify the sampling rate and/or resolution mode of the motion sensor during such periods of user activity or motion (eg, periods in which the amount of user motion exceeds a certain threshold). In this way, when the biometric monitoring device determines or detects such user activity or motion, the biometric monitoring device may place the motion sensor in a higher sampling rate and/or higher sampling resolution mode, for example, to achieve better resolution of the heart rate signal. Accurate adaptive filtering. (See, eg, Figure 9).

9 illustrates an example of a portable biometric monitoring device that changes the way it detects a user's heart rate based on the degree of movement experienced by the biometric monitoring device. In cases where motion is detected (eg, via use of an accelerometer), the user may be considered "active" by the biometric monitoring device, and high sampling rate heart rate detection may occur to reduce motion artifacts in heart rate measurements. This data can be saved and/or displayed. In cases where the biometric monitoring device determines that the user is not moving (or is relatively sedentary), low sampling rate heart rate detection (which does not consume as much power) may be appropriate for measuring heart rate and thus may be used.

Notably, where the biometric monitoring device uses optical techniques to acquire heart rate measurements or data, such as by using photoplethysmography, motion signals may be used to determine or establish that data acquisition or measurement by the heart rate sensor (e.g., synchronous detection rather than non-amplitude modulation methods) and/or specific methods or techniques for their analysis. (See, eg, Figure 1 IE). In this way, data indicative of a user's motion or activity level may cause the biometric monitoring device to establish or adjust the type or technique of data acquisition or measurement used by one or more optical heart rate sensors.

For example, in one embodiment, when the motion detector circuit detects or determines that the motion of the wearer of the biometric monitoring device is below a threshold (for example, if the biometric monitoring device determines that the user is sedentary or asleep), the biometric A monitoring device (or heart rate measurement technique as disclosed herein) may adjust and/or reduce the sampling rate of optical heart rate sampling. (See, eg, Figure 9). In this way, the biometric monitoring device can control its power consumption. For example, a biometric monitoring device may reduce power consumption by reducing sensor sampling rates, eg, a biometric monitoring device may sample heart rate (via a heart rate sensor) every 10 minutes or every 1 minute and 10 seconds. It is worth noting that, additionally or alternatively, the biometric monitoring device may control power consumption by controlling data processing circuit analysis and/or data analysis techniques based on motion detection. Thus, the movement of the user may affect heart rate data acquisition parameters and/or data analysis or processing thereof.

Motion Artifact Suppression in Heart Rate Sensors

As discussed above, the raw heart rate signal measured by the PPG sensor can be improved by using one or more algorithms to remove motion artifacts. The user's movement (for determining motion artifacts) may be measured using sensors including, but not limited to, accelerometers, gyroscopes, proximity detectors, magnetometers, and the like. The goal of such algorithms is to remove components of the PPG signal attributable to motion (motion artifacts) using motion signals captured from other sensors as a guide. In one embodiment, an adaptive filter can be used to remove motion artifacts in the PPG signal based on a hybrid Kalman filter and a least mean square filter or a recursive least square filter. Heart rate can then be extracted from the cleaned/filtered signal using a peak counting algorithm or a power spectral density estimation algorithm. Alternatively, a Kalman filter or particle filter may be used to remove such motion artifacts.

Another method that can be used to calculate the heart rate frequency is to create a model of the heart rate signal as Y = Y _dc + Σa _k * cosk θ + b _k * sink θ, where k is the order of the harmonic components and θ is the model for the heart rate parameter. This model can then be fitted to the signal using an extended Kalman filter or a particle filter. This model exploits the fact that the signal is not sinusoidal and therefore contains power at both the fundamental harmonic as well as several additional harmonics.

Alternatively, the signal can be modeled as Y=Y _dc + ∑a _k *sin(k*W _motion t+θ)+∑b _k *sin(k*W _HR t+φ), where W _motion comes directly from the accelerometer signal (or another motion sensor signal) estimate.

Ambient light and skin tone

Ambient light and skin tones may make it difficult to extract the user's heart rate from the PPG signal. The effect of ambient light can be reduced by subtracting the value of the received detected light signal when the PPG light source is off from the value of the received detected light signal when the PPG light source is on (assuming the two signals are acquired in close proximity to each other) .

The effect of skin color can be reduced by varying the intensity of the PPG light source, the wavelength of light emitted from the light source, and/or by using the ratio or difference of received signals corresponding to two different wavelengths. Skin color can be determined by using user input (eg, a user typing in their skin color), an image of a person's face, etc., and the skin color can then be used to calibrate the algorithm, light source brightness, light source wavelength, and receiver gain. The effect of skin color (and how tightly the user is wearing the device) on the raw PPG signal can also be measured by sending a signal of known amplitude to the light source and then measuring the signal received from the photodetector. Such signals may be sent for extended periods of time (so that data is captured over multiple expected heartbeats) and then averaged to produce a steady state data set independent of heart rate. This amplitude can then be compared to a set of values stored in a table to determine algorithm calibration, transmitter amplitude, and receiver gain.

Improved heart rate estimation using heuristics

After an initial estimate of heart rate (eg, peak counts estimated by power spectral density) is obtained, it can be used to impose bounds on the allowable rate of heart rate. These limits can be optimized for each user since each user will have a unique heart rate profile. For example, each user's sedentary heart rate can be estimated when they are stationary, and this can be used as a lower bound when the user is walking. Similarly, one-half the walking frequency as calculated from the pedometer may serve as a good lower limit for the expected heart rate.

The heart rate algorithm can be customized for each user and can learn the user's heart rate profile and adapt to the user's behavior and/or characteristics to perform better over time. For example, the algorithm may set boundaries on heart rate or walking rate during a particular physical activity based on historical data from the user. This may provide better results when heart rate data is corrupted by noise and/or motion artifacts.

HR Quality Metrics

In another example embodiment, a signal quality metric of the heart rate/PPG signal may be used to provide a quantification of the accuracy/precision of the generated signal. Depending on the value of this metric, algorithms that determine the user's heart rate (or other PPG-derived metrics such as respiration) may take certain actions, including asking the user to tighten the strap, ignoring certain parts of the collected heart rate data (e.g., with data segments with low quality metrics), and weighting certain parts of the heart rate data (for example, data with higher quality metrics can be given more weight in computing heart rate).

In one embodiment, the signal quality metric may be derived by plotting a scatter diagram with time on the x-axis and frequency of peaks in the PPG signal at that given moment in time on the y-axis. The problem to be overcome using this strategy is that there may be multiple and/or zero peaks at a given moment. The line of best fit captures the linear relationship in this scatterplot. A high quality signal should have a set of peaks that fit well to the line (over a short time span), while a bad signal will have a set of peaks that are not well described by the line. Therefore, the quality of the fit to the line provides a good measure of the quality of the PPG signal itself.

Categorical measures of sedentary, sleep and activity

In yet another example embodiment, the biometric monitoring device may use sensors to calculate heart rate variability when the device determines that the user is sedentary or asleep. Here, the biometric monitoring device may operate the sensor in a higher rate sampling mode (relative to non-sedentary periods or periods of user activity above a predetermined threshold) to calculate heart rate variability. A biometric monitoring device (or an external device) may use changes in heart rate as an indicator of heart health or stress.

Indeed, in some embodiments, the biometric monitoring device may measure and/or determine the user's stress level and/or when the user is sedentary and/or asleep (eg, as detected and/or determined by the biometric monitoring device) or heart health. Some embodiments of the biometric monitoring device of the present invention may use sensor data indicative of changes in heart rate, galvanic skin response, skin temperature, body temperature, and/or heart rate to determine a user's stress level, health status (e.g., risk of fever or cold, onset, or progression), and/or heart health. In this manner, the processing circuitry of the biometric monitoring device may determine and/or track a user's "baseline" stress level over time and/or heart "health" over time. In another embodiment, the device may be in which the user has no motion (or the user's motion is below a predetermined threshold, such as when the user is sitting, lying down, asleep, or in a sleep stage (e.g., deep sleep)) A physiological parameter of the user is measured during one or more cycles of . This data can also be used by the biometric monitoring device as a "data" for stress-related parameters, health-related parameters (e.g., risk or onset of fever or cold), heart health, heart rate variability, galvanic skin response, skin temperature, body temperature, and/or heart rate. Baseline".

sleep monitoring

In some embodiments, the biometric monitoring device may automatically detect or determine that the user is attempting to fall asleep, falling asleep, falling asleep, and/or waking from a sleep cycle. In such embodiments, the biometric monitoring device may use physiological sensors to acquire data, and the data processing circuitry of the biometric monitoring device may render the heart rate, heart rate variability, respiration rate, galvanic skin response collected from the sensors of the biometric monitoring device , motion, skin temperature and/or body temperature data to detect or determine whether the user is attempting to fall asleep, falling asleep, falling asleep and/or waking from a sleep cycle. In response, the biometric monitoring device may, for example, acquire physiological data (of the type and in the manner described herein) and/or determine a physiological condition of the user (of the type and in the manner described herein) Way). For example, a decrease or cessation of user motion combined with a decrease in change in the user's heart rate and/or heart rate variability may indicate that the user has fallen asleep. Changes in heart rate and subsequent changes in galvanic skin response may then be used by the biometric monitoring device to determine transitions in the user's sleep state between two or more sleep stages (e.g., transitions to lighter and/or deeper sleep stage). The user's motion and/or elevated heart rate and/or changes in heart rate variability may be used by the biometric monitoring device to determine that the user has woken up.

Real-time, windowed, or batch processing can be used to determine transitions between wake, sleep, and sleep stages. For example, a reduction in heart rate may be measured in a time window where the heart rate increases at the beginning of the window and decreases in the middle (and/or end) of the window. Wake and sleep stages can be classified by hidden Markov models using motion signals (eg, reduced exercise intensity), heart rate, changes in heart rate, skin temperature, galvanic skin response, and/or changes in ambient light levels. Transition points can be determined by a change point algorithm (eg, Bayesian change point analysis). Wake versus sleep may be determined by observing periods in which the user's heart rate decreases by at least a certain threshold over a predetermined duration, but within predetermined margins of the user's resting heart rate (which, for example, is observed as the user's minimum heart rate while sleeping) transitions between. Similarly, transitions between sleep and wakefulness may be determined by observing the user's heart rate increase above a predetermined threshold above the user's resting heart rate.

In some embodiments, a biometric monitoring device may be a component of a system for monitoring sleep, wherein the system includes an auxiliary device configured to communicate with the biometric monitoring device and adapted to be placed near the sleeper (e.g., Alarm clock). In some implementations, the secondary device has a shape and a mechanical and/or magnetic interface to receive a biometric monitoring device for safekeeping, communication, and/or charging. However, the secondary device may also be generic to a biometric monitoring device, such as a smartphone that is not specifically designed to physically interface with a biometric monitoring device. Communication between the biometric monitoring device and the auxiliary device may be provided via a wired communication interface or via a wireless communication interface and protocols such as Bluetooth (including, for example, Bluetooth 4.0 and Bluetooth low energy protocols), RFID, NFC, or WLAN. Assistive devices may include sensors to aid in sleep monitoring or environmental monitoring, such as sensors that measure ambient light, noise, and/or sound (e.g., to detect snoring), temperature, humidity, and air quality (pollen, dust, CO2, etc.) . In one embodiment, the secondary device may communicate with an external service or server (eg, a personal computer), such as www.fitbit.com. The communication with the auxiliary device can be implemented via wired (e.g., Ethernet, USB) or wireless (e.g., WLAN, Bluetooth, RFID, NFC, cellular) circuitry and protocols used to transfer data to and/or from the auxiliary device. device communication. The auxiliary device can also act as a means to transfer data from external services such as www.fitbit.com or other services (e.g., data such as news, social network updates, email, calendar notifications, etc.) or servers (e.g., personal , tablet computer) to the biometric monitoring device and/or a repeater that transmits data from the biometric monitoring device to said external service or server. Calculation of user sleep data may be performed on one or both devices or on an external service (eg, cloud server) using data from one or both devices.

The secondary device may be equipped with a display to display data obtained by the secondary device or data transmitted to it by the biometric monitoring device, external service, or a combination of data from the biometric monitoring device, secondary device, and/or external service. For example, the secondary device may display data indicative of the user's heart rate, total steps taken for the day, activity and/or sleep goal achievement, weather for the day (measured by the secondary device or reported by an external service for a location), and the like. In another example, the secondary device may display data related to the ranking of the user relative to other users, such as total weekly steps. In yet another embodiment, the biometric monitoring device may be equipped with a display to display data obtained by the biometric monitoring device, an auxiliary device, an external service, or a combination of the three sources. In embodiments where the first device is equipped with a wake-up alarm (eg, vibrating motor, speaker), the secondary device can act as a backup alarm (eg, using an audio speaker). The secondary device may also have an interface (eg, a display and buttons or a touch screen) to create, delete, modify or activate an alarm on the first and/or secondary device.

Sensor-based standby mode

In another embodiment, the biometric monitoring device may automatically detect or determine whether it is attached to, mounted on, and/or worn by the user. In response to detecting or determining that the biometric monitoring device is not attached to, mounted on, and/or worn by the user, the biometric monitoring device (or selected portions thereof) may implement or be placed in a low power mode of operation, for example, The optical heart rate sensor and/or circuitry can be placed in a lower power or sleep mode. For example, in one embodiment, a biometric monitoring device may include one or more photodetectors (photodiodes, phototransistors, etc.). If, at a given light intensity setting (e.g., relative to light emitted by a light source that is part of the biometric monitoring device), one or more light detectors provides a low return signal, the biometric monitoring device may interpret the data as Pointing device is not being worn. Upon such a determination, the device may reduce its power consumption, for example, by "disabling" or adjusting the operating conditions of the stress and/or heart rate detection sensors and/or circuits, among other device circuits or displays (e.g., by Reduce the duty cycle of the light source and/or detector or disable the light source and/or detector, turn off the device display, and/or disable or attenuate associated circuitry or portions thereof). Additionally, the biometric monitoring device may periodically determine (e.g., once per second) whether the operating conditions of the stress and/or heart rate detection sensors and/or associated circuitry should be restored to normal operating conditions (e.g., light sources, detectors, and / or associated circuitry should return to normal operating mode for heart rate detection). In another embodiment, the biometric monitoring device may detect a triggerable event (e.g., upon detection of motion of the device (e.g., based on data from one or more motion sensors) and/or upon detection of a triggerable event via the user interface. Immediately following user input (eg, a touch, bump, or toggle interaction with the biometric monitoring device) the stress and/or operating condition of the heart rate detection sensor and/or associated circuitry is restored. In some related embodiments, for power saving purposes, the biometric monitoring device may reduce its default rate of heart rate measurement collection to, for example, one measurement per minute when the user is inactive, and the user may have the ability, for example, by pushing a button Instead, the device is placed in a mode of operation that produces measurements on demand or at a faster rate (eg, once per second).

optical sensor

In one embodiment, optical sensors (sources and/or detectors) may be placed on the interior or skin side of the biometric monitoring device (i.e., the side of the biometric monitoring device that touches, touches, and/or faces the user's skin (lower side). referred to herein as the "skin side")). (See, eg, Figures 2A through 3C). In another embodiment, the optical sensor may be disposed on one or more sides of the device, including the skin side and one or more sides of the device facing or exposed to the surrounding environment (environmental side). (See, eg, Figures 6A through 7).

6A illustrates an example of a portable monitoring device with a strap; optical sensors and light emitters can be placed on the strap.

6B illustrates an example of a portable biometric monitoring device with a display and wristband. Additionally, an optical PPG (eg, heart rate) detection sensor and/or transmitter may be located on the side of the biometric monitoring device. In one embodiment, each of these may be located in a side mounted button.

7 depicts a user pressing the side of a portable biometric monitoring device to take a heart rate measurement from a side mounted optical heart rate detection sensor. The display of the biometric monitoring device may show whether a heart rate has been detected and/or display the user's heart rate.

Notably, data from such optical sensors may represent physiological data and/or environmental data. Indeed, in one embodiment, the optical sensor provides, acquires and/or detects information from multiple sides of the biometric monitoring device, regardless of whether the sensor is disposed on one or more of the multiple sides. For example, an optical sensor may obtain data related to the ambient light conditions of the environment.

Where the optical sensor is positioned or arranged on the skin side of the biometric monitoring device, in operation a light source in the biometric monitoring device may shine light on the user's skin and in response the light in the biometric monitoring device detects The detector can sample, acquire and/or detect corresponding reflected and/or emitted light from the skin (and from inside the body). One or more light sources and light detectors may be arranged in an array or pattern that enhances or optimizes signal-to-noise ratio and/or to reduce or minimize power consumption of the light sources and light detectors. These optical sensors may sample, acquire, and/or detect physiological data, which may then be processed or analyzed (e.g., by resident processing circuitry) to obtain representations such as the user's heart rate, respiration, heart rate variation, oxygen saturation ( SpO ₂ ), blood volume, blood sugar, skin moisture, and/or skin pigmentation levels.

The light source may emit light having one or more wavelengths specific or specific to the type of physiological data to be collected. Similarly, the optical detector may sample, measure and/or detect one or more wavelengths that are also specific or specific to the type of physiological data to be collected and/or the physiological parameter (of the user) to be evaluated or determined. For example, in one embodiment, a light source emitting light having a wavelength in the green spectrum (e.g., an LED emitting light having a wavelength corresponding to the green spectrum) and positioned to sample, measure, and/or detect and This light corresponds to a responsive or reflected photodiode that can provide data that can be used to determine or detect heart rate. In contrast, a light source emitting light having a wavelength in the red spectrum (e.g., an LED emitting light having a wavelength corresponding to the red spectrum) and a light source emitting light having a wavelength in the infrared spectrum (e.g., an LED emitting An LED with light corresponding to a wavelength of the IR spectrum) and a photodiode positioned to sample, measure and/or detect the response or reflection of this light can provide data that can be used to determine or detect _Sp02 .

Indeed, in some embodiments, the color or wavelength of light emitted by a light source such as an LED (or group of LEDs) can be modified, adjusted, and/or controlled according to predetermined types of physiological data being acquired or operating conditions . Here, the wavelength of light emitted by the light source can be adjusted and/or controlled to optimize and/or enhance the "quality" of the physiological data obtained and/or sampled by the detector. For example, when the user's skin temperature or ambient temperature is low, the color of the light emitted by the LED can be switched from infrared to green in order to enhance the signal corresponding to heart activity. (See, eg, Figure 1 ID).

In some embodiments, the biometric monitoring device may include a window (eg, for temporary inspection, an opaque window) in the housing to facilitate light emission between the optical sensor and the user. Here, the window may permit, for example, one or more LEDs to emit light (e.g., of a selected wavelength) onto the user's skin and permit a response or reflection of the light to pass back through the window to be transmitted by, for example, one or more LEDs. photodiodes for sampling, measurement and/or detection. In one embodiment, the circuitry related to emitting and receiving light may be housed inside the device housing and between layers of plastic or glass (e.g., sprayed with infrared ink) or infrared lenses or filters (which allow infrared light to pass but not Below or behind the passage of light in the human visual spectrum). In this way, the light transmission of the window is not visible to the human eye.

Biometric monitoring devices may use light pipes or other light transmissive structures to facilitate emission of light from the light source to the user's body and skin. (See, eg, Figures 4A through 5). In this regard, in some embodiments, light may be directed from the light source to the user's skin via such light pipes or other light transmissive structures. Scattered light from the user's body can be directed back to the optical circuitry in the biometric monitoring device via the same or similar structure. Indeed, the light-transmitting structure may use materials and/or optical designs that promote low light loss (for example, the light-transmitting structure may include lenses that facilitate light collection, and portions of the light-transmitting structure may be coated with reflective material or adjacent to reflective materials to facilitate internal reflection of light within the light-transmissive structure), thereby improving the signal-to-noise ratio of the photodetector and/or facilitating reduced power consumption of the light source and/or photodetector. In some embodiments, a light pipe or other light transmissive structure may comprise a material that selectively emits light of one or more specific or predetermined wavelengths more efficiently than light of other wavelengths, thereby serving as a bandpass filter. Such bandpass filters can be tuned to improve the signal for certain types of physiological data. For example, in one embodiment, in-mold labeling or "IML" light-transmissive structures may be implemented where the light-transmissive structures use materials with predetermined or desired optical properties to create specific bandpass characteristics such that, for example, Infrared light has a higher passing efficiency than light with other wavelengths (for example, light with wavelengths in the human visible spectrum). In another embodiment, a biometric monitoring device may use a light transmissive structure having an optically opaque portion comprising certain optical properties and an optically transparent portion comprising optical properties different from the optically opaque portion. Such light transmissive structures may be provided via a double injection or two step molding process, where optically opaque and optically transparent materials are injected into the mold separately. A biometric monitoring device implementing such a light-transmissive structure may contain different light transmission characteristics for different wavelengths depending on the direction of light travel through the light-transmissive structure. For example, in one embodiment, an optically opaque material may reflect a specific range of wavelengths in order to more efficiently transmit light from the user's body back to the light detector (which may have a different wavelength relative to the wavelength of the emitted light).

In another embodiment, reflective structures may be placed in the field of view of the light emitters and/or light detectors. For example, sides with holes that direct light from the light emitter to the user's skin and/or from the user's skin to the light detector (or through light-transmitting structures that do so) may be covered in a reflective material ( For example, chrome plating) to facilitate light transmission. The reflective material can increase the efficiency of light transmission from the light source to the skin and then from the skin back to the detector. Reflective coated holes may be filled with optical epoxy or other transparent material to prevent liquid from entering the device body while still allowing light to be transmitted with low transmission loss.

In another embodiment implementing light-transmissive structures (eg, structures created or formed via IML), such light-transmissive structures may include a mask composed of an opaque material that confines one, some, or all light sources and and/or the aperture of the detector. In this way, the light transmissive structure may selectively "bound" a preferred volume of the user's body into which light is emitted or from which light is detected. It is worth noting that other mask configurations may be used or implemented in conjunction with the concepts described and/or illustrated herein; all such masking to, for example, improve the photoplethysmographic signal and implemented in conjunction with the concepts described and/or illustrated herein configurations are intended to fall within the scope of the present invention.

In another embodiment, the light emitters and/or detectors may be configured to emit light through an aperture or series of apertures in the exterior of the device. The hole, or series of holes, may be filled with light transmissive epoxy, such as optical epoxy. The epoxy can form a light guide that allows light to be emitted from the light emitter to the skin and from the skin back to the light detector. This technology also has the advantage that the epoxy creates a watertight seal that prevents water, sweat, or other liquids from entering the body of the device through holes on the exterior of the device that allow light emitters and detectors to emit light to biometric monitoring Light is received outside the device body and from outside the biometric monitoring device body. Epoxies with high thermal conductivity can be used to help prevent light sources (eg, LEDs) from overheating.

In any of the light-transmissive structures described herein, the exposed surface of the optic (light-transmissive structure) or device body may comprise a hard paint, hard dip-coat, or optical coating (e.g., anti-reflection, anti-scratch , anti-fog and/or wavelength band blocking (eg UV blocking) coatings). Such properties or materials may improve the operation, accuracy and/or durability of the biometric monitoring device.

Figure 4A illustrates an example of a potential PPG light source and photodetector geometry. In this embodiment, two light sources are placed on either side of the photodetector. These three devices are located in protrusions on the back of the wristband-type biometric monitoring device with its side facing the user's skin.

4B and 4C illustrate an example of a PPG sensor with a photodetector and two LED light sources. These components are placed in a biometric monitoring device with protrusions on the back side. The light pipe optically connects the LED and photodetector to the surface of the user's skin. Under the skin, light from the light source is scattered from the blood in the body, some of which may be scattered or reflected back into the photodetector.

5 illustrates an example of a biometric monitoring device with an optimized PPG detector having a protrusion with curved sides to avoid discomfort to the user. Additionally, the surface of the light pipe that optically couples the photodetectors and LEDs to the wearer's skin is contoured to maximize light flux coupling between the LEDs and photodetectors and the light pipe. The end of the light pipe facing the user's skin is also corrugated. This profile can focus or defocus the light to optimize the PPG signal. For example, the profile can focus emitted light to a certain depth and location consistent with areas where blood flow is likely to occur. The vertices of these foci can overlap or be very close together so that the photodetector receives the largest possible amount of scattered light.

In some embodiments, the biometric monitoring device may include a concave or convex shape (such as a lens) on the skin side of the device to focus light towards a specific volume at a specific depth in the skin and increase the collection of light from that point. to the efficiency of the photodetector. (See, eg, Figures 4A through 5). In cases where such biometric monitoring devices also use light pipes to selectively and controllably route light, the ends of the light-guiding rods are shaped to have a degree of cylindricity (e.g., the ends of the light-guiding rods can be It may be advantageous to be a cylindrical surface (or part thereof) bounded by a cylinder axis nominally parallel to the skin side (eg instead of using an axially symmetric lens). For example, in a wrist-worn biometric monitoring device, such cylindrical lenses may be oriented such that the cylinder axis is nominally parallel to the wearer's forearm, which may have the effect of restricting access from directions parallel to the person's forearm. The amount of light of such lenses and the effect of increasing the amount of light entering such lenses from a direction perpendicular to the person's forearm, because the environment Light is more likely to reach the sensor detection area from directions unobscured by the straps of the biometric monitoring device (i.e., along the axis of the user's forearm). The signal-to-noise ratio is also improved by reducing the "stray" light detected or collected by the photodetector. In this way, the signal sampled, measured and/or detected by the photodetector consists of less stray light and more user skin/body response to the emitted light (signal or data representing the response to the emitted light )composition.

In another embodiment, the light transmissive epoxy can be molded into a concave or convex shape to also provide the sensor with beneficial optical properties. For example, during application of a light-transmissive epoxy, the top of the light-transmissive structure formed from the epoxy can be shaped into a concave surface to enable more efficient coupling of light into the light-transmissive structure.

In one embodiment, components of the optical sensor may be positioned on the skin side of the device and arranged or positioned to reduce or minimize interference between (i) the light source and/or associated detector and (ii) the user's skin. distance. See, eg, FIG. 3A, which provides a cross-sectional view of a sensor protrusion of an example portable monitoring device. In FIG. 3A, two light sources (eg, LEDs) are placed on either side of the photodetector to enable PPG sensing. A light blocking material is placed between the light source and the photodetector to prevent any light from the light source from reaching the photodetector without first exiting the body of the biometric monitoring device. A flexible transparent layer can be placed on the lower surface of the sensor protrusion to form a seal. This transparent layer can serve other functions, such as preventing liquid from entering the device where the light source or photodetector is placed. This transparent layer can be formed via in-mold labelling, or "IML." The light source and photodetector can be placed on the flexible PCB.

Such configurations can improve the coupling efficiency of light flux between components of the optical sensor and the user's body. For example, in one embodiment, the light source and/or associated detector may be mounted on a flexible or bendable substrate that is flexible, allowing the skin side of the biometric monitoring device to (It may be made of compliant material) conforms (eg, without additional processing) or can be shaped (or conformable) to conform to the body part to which the biometric monitoring device is coupled or attached during normal operation (eg, The shape of the user's wrist, arm, ankle, and/or leg) so that the light source and/or associated detector are close to the user's skin (i.e., there is little gap between the skin side of the device and the adjacent surface of the user's skin) . See eg Figure 6A. In one embodiment, the light source and/or associated detector may be mounted on a Flat Flex Cable or "FFC" or flexible PCB. In this embodiment, a flexible or bendable substrate (eg, FFC or flex PCB) may be connected to a second substrate (eg, PCB) within the device on which other components (eg, data processing circuitry) are disposed. Optical components of different heights can be mounted to different "fingers" of the flexible substrate and pressed or fastened to the housing surface so that the optical components are flush with the housing surface. In one embodiment, the second substrate may be a relatively inflexible or inflexible substrate, fixed within the device, on which other circuits and components (passive and/or active) are disposed.

3B depicts a cross-sectional view of a sensor protrusion of an example portable monitoring device; this protrusion is similar to that presented in FIG. 3A except that the light source and photodetector are placed on a flat and/or rigid PCB.

Figure 3C provides another cross-sectional view of an example PPG sensor embodiment. Notably, there are no protrusions in this PPG sensor. Additionally, liquid gaskets and/or pressure sensitive adhesives are used to prevent liquids from entering the body of the biometric monitoring device.

Some embodiments of the biometric monitoring device may be adapted to be worn or carried on the user's body. In some embodiments that include an optical heart rate monitor, the device may be a wrist-worn or arm-mounted ornament such as a watch or bracelet. (See, eg, Figures 2A through 7). In one embodiment, the optics of the optical heart rate monitor may be located on the interior or skin side of the biometric monitoring device, such as facing the top of the wrist when the biometric monitoring device is worn on the wrist (i.e., the optical heart rate monitor may be adjacent on and facing the wrist). (See, eg, Figures 2A through 3C).

In another embodiment, an optical heart rate monitor may be located on one or more exterior or ambient side surfaces of the biometric monitoring device. (See, eg, Figures 6B and 7). In such embodiments, the user may touch the optical window (behind which the optics of the optical heart rate monitor is located) with a finger of the opposite hand to initiate a heart rate measurement (and/or other metrics related to heart rate, such as heart rate variability) and and/or collect data that can be used to determine the user's heart rate (and/or other metrics related to heart rate). (See, eg, Figure 6B). In one embodiment, the biometric monitoring device may trigger or initiate a measurement by detecting a (sudden) drop in incident light on the photodiode, for example, when a user's finger is placed on the optical window. Additionally or alternatively, heart rate measurements (or other such metrics) may be triggered by infrared-based proximity detectors and/or capacitive touch/proximity detectors (which may be separate from the other detectors). Such infrared-based proximity detectors and/or capacitive touch/proximity detectors may be disposed in or on the optical window and/or functionally, electrically and/or physically coupled to the optical window to detect or determine For example the presence of a user's finger.

In yet another embodiment, the biometric monitoring device may include a button that, when pressed, triggers or initiates a heart rate measurement (and/or other metrics related to heart rate). The button may be placed in close proximity to the optical window to facilitate the user's pressing of the button when a finger is placed on the optical window. (See, eg, Figure 7). In one embodiment, the optical window can be embedded in the push button. Thus, when the user presses the button, it can trigger a measurement of the finger pressing the button. Indeed, the button can be given a shape and/or a resistance to pressing that enhances or optimizes the pressure distribution of the button against the finger to provide a high signal-to-noise ratio during measurement or data acquisition. In other embodiments (not illustrated), the biometric monitoring device may be in the form of a clip, slippery object, pendant, anklet, strap, etc. adapted to be worn on the body, clipped or mounted to a piece of clothing, stored in clothing ( For example, in a pocket) or stored in an ornament (for example, a handbag).

In one particular embodiment, the biometric monitoring device may include protrusions on the skin side or inside of the device. (See, Figures 2A to 6A). When coupled to a user, the protrusions can engage the skin with greater force than the surrounding device body. In this embodiment, the optical window or light transmissive structure (both of which are discussed in detail above) may form part of or be incorporated in the protrusion. The light emitters and/or detectors of the optical sensor may be positioned near the window or the light-transmitting structure or arranged in a protrusion. (See, eg, Figures 2B and 6A). Thus, when attached to the user's body, the raised window portion of the biometric monitoring device can engage the user's skin with greater force than the surrounding device body, thereby providing a stronger physical coupling between the user's skin and the optical window . That is, the protrusions can induce permanent contact between the biometric monitoring device and the user's skin, which can reduce the amount of stray light measured by the photodetector, reduce relative motion between the biometric monitoring device and the user, and and/or provide improved localized pressure on the user's skin; all of which may improve the quality of the cardiac signal of interest. Notably, the protrusions may contain other sensors that benefit from close proximity and/or firm contact with the user's skin. These sensors may be included in addition to or instead of heart rate sensors and include, for example, skin temperature sensors (e.g., non-contact sensors utilizing optical windows or thermistors bonded to the exterior surface of the protrusion by thermal epoxy). sensors such as thermopiles), pulse oximeters, blood pressure sensors, EMG or galvanic skin response (GSR) sensors.

Additionally or alternatively, a portion of the skin side of the biometric monitoring device may contain a friction enhancing mechanism or material. For example, the skin side of a biometric monitoring device may include a plurality of raised or recessed regions or portions (eg, small bumps, ridges, grooves, and/or pits). Additionally, a friction enhancing material (eg, a gel-like material such as silicone or other elastic material) may be disposed on the skin side. In fact, the back of the device, made of gel, also provides friction while also improving user comfort and preventing stray light from entering. As noted above, friction enhancing mechanisms or materials may be used alone or in combination with a biometric monitoring device with protrusions as described herein. In this regard, a biometric monitoring device may include a plurality of raised or recessed region portions (eg, small bumps, ridges, grooves, and/or dimples) in or on a raised portion of the device. Indeed, such raised or recessed region portions may be incorporated/embedded in or onto the raised window portion. Additionally or alternatively, the raised portion may consist of or be coated with a friction enhancing material (eg, a gel-like material such as silicone). It is worth noting that the use of protrusions and/or friction can improve the ability to respond to certain sensors by reducing motion of the biometric monitoring device (and thus the sensor) relative to the user's skin during operation (especially when the user is exercising). Measurement accuracy of data acquisition of parameters (eg, heart rate, heart rate variability, galvanic skin response, skin temperature, skin coloration, heat flux, blood pressure, blood glucose, etc.).

Some or all of the internal or skin-side housing of the biometric monitoring device may also be composed of a metallic material (eg, steel, stainless steel, aluminum, magnesium, or titanium). Such configurations provide structural rigidity. (See, eg, Figure 2B). In such embodiments, the device body can be designed to be hypoallergenic through the use of hypoallergenic "nickel-free" stainless steel. Notably, it may be advantageous to use (at least in some locations) some type of metal that is at least slightly ferrous (eg, a grade of stainless steel that is ferrous). In such embodiments, the biometric monitoring device (where it includes a rechargeable energy source (e.g., a rechargeable battery)) can be interconnected with a charger via a connector using a The magnets of the material secure themselves to the biometric monitoring device. Additionally, biometric monitoring devices can use this magnetic feature to engage a base or docking station to facilitate data transfer. Additionally, such housings may provide enhanced electromagnetic shielding which will enhance the integrity and reliability of the optical heart rate sensor and heart rate data acquisition process/operation. Additionally, a skin temperature sensor may be physically coupled and thermally coupled to the metal body, eg, by thermal epoxy, to sense the user's temperature. In embodiments that include a protrusion, the sensor may be positioned near or in the protrusion to provide secure contact and localized thermal coupling to the user's skin.

In a preferred embodiment, one or more components of the optical sensor (in one embodiment, it may be located in a protrusion, and/or in another embodiment, it may be seated or placed in contact with the surface of the biometric monitoring device Flush) is attached, secured, contained and/or secured to the biometric monitoring device via a liquid-tight seal (ie, a method/mechanism that prevents liquids from entering the body of the biometric monitoring device). For example, in one embodiment, a back of the device made of metal (such as, but not limited to, stainless steel, aluminum, magnesium, or titanium) or rigid plastic may provide sufficient rigidity to maintain the structural integrity of the device while accommodating a watertight seal for the sensor package. Sealed structure. (See, eg, Figures 2B through 3C).

In a preferred embodiment, the package or module of the optical sensor can be attached to the device by a pressure sensitive adhesive and a liquid gasket. See, eg, Figure 3C, which provides another cross-sectional view of a PPG sensor embodiment. Notably, there are no protrusions in this PPG sensor. Additionally, liquid gaskets and/or pressure sensitive adhesives are used to prevent liquids from entering the body of the device. For example, if a stronger or more durable connection is desired between the optical sensor package/module and the device body, screws, rivets, etc. may also be used. It is worth noting that this embodiment can also use waterproof adhesives, hydrophobic membranes such as Gore-Tex, o-rings, sealants, grease or epoxy to fasten the optical sensor package/module Or attached to the body of the biometric monitoring device.

As discussed above, biometric monitoring devices may include materials that include high reflectivity properties, such as polished stainless steel, reflective paint, and polished plastic, disposed on the skin side or inside. In this way, light scattered from the skin side of the device may be reflected back to the skin in order to improve the signal-to-noise ratio of the optical heart rate sensor, for example. In effect, this effectively increases the input optical signal compared to a non-reflective (or less reflective) back of the device body. Notably, in one embodiment, the color of the skin side or inside of the biometric monitoring device may be selected to provide certain optical properties (e.g., reflect specific or predetermined wavelengths of light) in order to improve information about certain physiological data. type of signal. For example, where the skin side or inside of the biometric monitor is green, the measurement of heart rate may be enhanced due to the preferential emission of light corresponding to wavelengths of the green spectrum. Where the skin side or inner side of the biometric monitor is red, the measurement of _SpO2 may be enhanced due to the preference for emission of light corresponding to wavelengths of the red spectrum. In one embodiment, the color of the skin side or inside of the biometric monitor may be modified, adjusted and/or controlled according to a predetermined type of physiological data acquired.

11A depicts an example schematic block diagram of an optical heart rate sensor, where light is emitted from a light source towards the user's skin, and the reflection of this light from the user's skin/inside the body is sensed by a light detector, the signal from which is then passed through the analog /digital converter (ADC) digitization. The intensity of the light source can be modified (eg, via a light source intensity control module) to maintain a desired reflected signal intensity. For example, the light source intensity can be reduced to avoid saturation of the output signal from the photodetector. As another example, the light source intensity may be increased to maintain the output signal from the photodetector within a desired range of output values. Notably, active control of the system can be achieved via linear or nonlinear control methods (e.g., proportional-integral-derivative (PID) control, fixed-step control, predictive control, neural networks, hysteresis, etc.), And information derived from other sensors in the device (eg motion, galvanic skin response, etc.) may also be used. FIG. 11A is provided for illustration and not to limit implementation of such systems to, for example, an ADC integrated within an MCU or use of an MCU in this regard. Other possible implementations include using one or more internal or external ADCs, FPGAs, ASICs, etc.

In another embodiment, a system with an optical heart rate sensor may incorporate the use of a sample and hold circuit (or equivalent) to maintain the output of the photodetector when the light source is turned off or attenuated to save power. In embodiments where relative changes in the output of the photodetectors are critical (eg, heart rate measurements), the sample and hold circuitry may not necessarily maintain an accurate replica of the output of the photodetectors. In these cases, the sample and hold can be reduced to, for example, a diode (eg, a Schottky diode) and a capacitor. The output of the sample and hold circuit can be presented to an analog signal conditioning circuit (e.g., a Sallen-Key bandpass filter, level shifter, and/or gain circuit) to condition and amplify the signal in the frequency band of interest. signal (eg, 0.1 Hz to 10 Hz for cardiac or respiratory function), which can then be digitized by an ADC. See, eg, Figure 1 IB.

In operation, circuit topologies such as those already described herein (eg, sample and hold circuits) remove the DC and low frequency components of the signal and help resolve the AC components associated with heart rate and/or respiration. Embodiments may also include analog signal conditioning circuitry for variable gain settings, which may be controlled to provide a suitable signal (eg, not saturate). Performance characteristics (eg, ramp rate and/or gain bandwidth product) and power consumption of the light source, photodetector, and/or sample-and-hold can be significantly higher than analog signal conditioning circuits to enable fast duty cycling of the light source. In some embodiments, the power provided to the light source and photodetector can be controlled separately from the power provided to the analog signal conditioning circuit to provide additional power savings. Alternatively or in addition, the circuitry may use functionality such as enabling, disabling, and/or shutting down to achieve power savings. In another embodiment, the output of the photodetector and/or sample and hold circuit may be sampled by an ADC in addition to or instead of an analog signal conditioning circuit to control the light intensity of a light source or measure a physiological condition of interest. parameter (for example, when the analog signal conditioning circuit has not stabilized after changing the light intensity setting). Notably, since the physiological signal of interest is typically small relative to the inherent resolution of the ADC, in some embodiments, the reference voltage and/or gain of the ADC can be adjusted to enhance signal quality and/or the ADC can be processed. sampling. In yet another embodiment, the device may only digitize the output of the sample and hold circuit by, for example, oversampling, adjusting the reference voltage and/or gain of the ADC, or using a high resolution ADC. See, eg, Figure 11C.

PPG DC offset removal technology

In another embodiment, the sensor device may incorporate a differential amplifier to amplify the relative change in the output of the photodetector. See, eg, Figure 1 IF. In some embodiments, a digitally averaged or digitally low pass filtered signal may be subtracted from the output of the photodetector. This modified signal can then be amplified before it is digitized by an ADC. In another embodiment, an analog average or an analog low pass filtered signal may be subtracted from the output of the photodetector by, for example, using a sample and hold circuit and an analog signal conditioning circuit. The power supplied to the light source, photodetector, and differential amplifier can be controlled independently of the power supplied to the analog signal conditioning circuitry to improve power savings.

In another example, a signal (voltage or current, depending on the particular sensor implementation) can be subtracted from the raw PPG signal to remove any bias in the raw PPG signal, and thus increase parameters, such as heart rate variation) information of the PPG signal gain or amplify the PPG signal. This signal can be set in the factory to a default value, set to a value based on the user's specific skin reflectivity, absorption, and/or color, and/or can depend on feedback from an ambient light sensor or on the PPG signal itself analysis changes. For example, if it is determined that the PPG signal has a large DC offset, then a constant voltage can be subtracted from the PPG signal to remove the DC offset and achieve a larger gain, thus improving the PPG signal quality. In this example, the DC offset may result from ambient light (eg, from the sun or from indoor lighting) or light reflected from the PPG light source reaching the photodetector from the PPG light source.

In another embodiment, a differential amplifier may be used to measure the difference between the current and previous samples rather than the magnitude of each signal. Because the magnitude of each sample is typically much larger than the difference between each sample, a larger gain can be applied to each measurement, thus improving the PPG signal quality. The signals can then be integrated to obtain the original time domain signal.

In another embodiment, the photodetector module may incorporate a transimpedance amplifier stage with variable gain. Such configurations can avoid or minimize saturation due to bright ambient light and/or bright light emitted from the light source. For example, the gain of the transimpedance amplifier can be automatically reduced by a variable resistor and/or a set of multiplexing resistors in the degeneration path of the transimpedance amplifier. In some embodiments, the device can incorporate little optical shielding from ambient light by amplitude modulating the intensity of the light source and then demodulating the output of the light detector (eg, synchronous detection). See eg Figure 11E. In other aspects, if the ambient light is of sufficient brightness to obtain a heart rate signal, the light source may be reduced in brightness and/or turned off completely.

In yet another embodiment, the foregoing processing techniques may be used in combination to optically measure physiological parameters of the user. See, eg, Figure 11G. This topology can allow the system to operate in low power measurement states and circuit topologies (where applicable) and adapt to higher power measurement states and circuit topologies as needed. For example, the system could use an analog signal conditioning circuit to measure a physiological parameter of interest (e.g., heart rate) to reduce power consumption when the user is immobile or sedentary, but switch directly to the light detector output when the user is active Oversampling style sampling for .

In embodiments where the biometric monitoring device includes a heart rate monitor, processing the signal to obtain a heart rate measurement may include filtering and/or signal conditioning, such as bandpass filtering (eg, Butterworth filtering). To counteract large transients that may occur in the signal and/or improve the convergence of the filtering, non-linear methods such as neural networks or ramp rate limiting may be used. Data from sensors on the device (eg, motion, galvanic skin response, skin temperature, etc.) can be used to adjust the signal conditioning method used. Under certain operating conditions, the user's heart rate may be measured by counting the number of signal peaks within a time window or by exploiting the signal's fundamental frequency or second harmonic (eg, via a Fast Fourier Transform (FFT)). In other cases, such as heart rate data acquired while the user is exercising, an FFT can be performed on the extracted signal and spectral peaks, which can then be followed by a multi-object tracker (which starts, continues, merges, and deletes tracking) to be processed. In some embodiments, a similar set of operations can be performed on the motion signal, and the output can be used for activity discrimination (e.g., sedentary, walking, running, sleeping, lying down, sitting, cycling, typing, elliptical training, weight training), which is used to assist the multi-object tracker. For example, it may be determined that the user is stationary and has begun to move. This information can be used to preferentially bias the continuation portion of the trace towards increasing frequencies. Similarly, the activity discriminator may determine that the user has stopped running or is running more slowly, and this information may be used to preferentially bias the continuation portion of the trace towards decreasing frequency. Tracking can be achieved with single-scan or multi-scan multi-object tracker topologies, such as joint probabilistic data association tracker, multiple hypothesis tracking, closest neighbor, etc. Estimation and prediction in the tracker can be done via Kalman filter, spline regression, particle filter, interactive multi-model filter, etc. The track selector module can use the output track from the multispectral tracker and estimate the user's heart rate. The estimate may take the form of a maximum likelihood trajectory, a weighted sum of the probability of the trajectory against which it is heart rate, or the like. Furthermore, the activity discriminator may influence the selection and/or fusion to obtain heart rate estimates. For example, if the user is sleeping, sitting, lying down, or sedentary, the prior probabilities may be biased towards heart rates in the 40 to 80 bpm range; Elevated heart rate in the range of 90 to 180 bpm. The impact of the activity discriminator may be based on the user's velocity. When the user is not moving, the estimate can move to the fundamental frequency of the signal (or be derived from it at all). Trajectories corresponding to the user's heart rate may be selected based on criteria indicative of a change in activity; for example, if the user begins walking from a stationary position, a trajectory illustrating a shift toward higher frequencies may be preferentially selected.

Acquisition of a good heart rate signal may be indicated to the user via a display on the biometric monitoring device or another device in wired or wireless communication with the biometric monitoring device (eg, a Bluetooth low energy equipped mobile phone). In some embodiments, the biometric monitoring device may include a signal strength indicator represented by a pulse of an LED that may be viewed by the user. The pulse can be timed or correlated to coincide with the user's heartbeat. The intensity, pulse rate and/or color of the LEDs can be modified or adjusted to suggest signal strength. For example, a brighter LED intensity may indicate a stronger signal or in an RGB LED configuration, a green LED may indicate a stronger signal.

In some embodiments, the strength of the heart rate signal may be determined by the energy (eg, sum of squares) of the signal in a frequency band, eg, 0.5 Hz to 4 Hz. In other embodiments, the biometric monitoring device may have strain gauges, pressure sensors, force sensors, or other contact-indicating sensors that may be incorporated or built into the housing and/or the strap (when the biometric monitoring device is attached to the strap ( such as a watch, bracelet and/or armband) or in those embodiments mounted with a strap (which can then be fastened to the user)). A signal quality metric (eg, heart rate signal quality) may be calculated based on data from these contact sensors alone or in combination with data from the heart rate signal.

In another embodiment, the biometric monitoring device may monitor heart rate optically via an array of photodetectors, such as a grid of photodiodes or CCD cameras. Movement of the optical device relative to the skin may be tracked via feature tracking of the skin and/or adaptive motion correction using accelerometers and gyroscopes. The detector array can be in contact with the skin or offset from the skin by a small distance. The detector array and its associated optics can be actively controlled (eg, via motors) to maintain a steady image of the target and acquire heart rate signals. This optomechanical stabilization may be achieved using information from motion sensors (eg, gyroscopes) or image features. In one embodiment, a biometric monitoring device may use a coherent or incoherent light source that illuminates the skin and an array of photodetectors, where each photodetector is compared to the intensity between adjacent detectors, thereby obtaining a so-called Speckle patterns, which can be tracked using various image tracking techniques, such as optical flow, template matching, edge tracking, etc.) to implement relative motion cancellation. In this embodiment, the light source used for motion tracking may be different than the light source used for optical heart rate monitoring.

In another embodiment, a biometric monitoring device may consist of a plurality of photodetectors and photoemitters distributed along the surface of the device that touches the user's skin (i.e., the skin side of the biometric monitoring device). ). (See, eg, Figures 2A through 6A). For example, in the example of a bracelet, there may be multiple photodetectors and light emitters placed at various points along the inner circumference of the strap. (See, eg, Figure 6A). A heart rate signal quality metric associated with each location may be calculated to determine an optimal location or set of optimal locations for estimating the user's heart rate. Subsequently, some of the sites may be disabled or switched off, for example to reduce power consumption. The device may periodically check heart rate signal quality at some or all of the sites to enhance, monitor and/or optimize signal and/or power efficiency.

In another embodiment, a biometric monitoring device may include a heart rate monitoring system that includes multiple sensors such as optical, acoustic, pressure, electrical (e.g., ECG or EKG), and motion, and fuses data from both of these sensors. or both to provide heart rate estimates and/or mitigate motion-induced noise.

In addition to or instead of heart rate monitoring (or other biometric monitoring), in some embodiments, the biometric monitoring device may include an optical sensor to track or detect ultraviolet light exposure based on light conditions, all Time and duration of outdoor light exposure, type of light source and duration and intensity of light source (fluorescent light exposure, incandescent light exposure, halogen, etc.), exposure to televisions (based on light type and flicker rate), user exposure to indoor Or outdoors, time of day and location. In one embodiment, the UV light detection sensor may consist of a reverse biased LED emitter driven as a photodetector. For example, the photocurrent produced by this detector can be characterized by measuring the time it takes for the capacitance of the LED (or alternatively, the parallel capacitor) to discharge.

All of the optical sensors discussed herein may be used in conjunction with other sensors to improve the detection of the data described above or to enhance the detection of other types of physiological or environmental data.

Where the biometric monitoring device includes audio or passive acoustic sensors, the device may contain one or more passive acoustic sensors that detect sound and pressure and may include, but are not limited to, microphones, piezoelectric membranes, and the like. Acoustic sensors may be positioned on one or more sides of the device, including the side that touches or faces the skin (skin side) and the side that faces the environment (environmental side).

Skin-side acoustic or audio sensors can detect any type of sound emanating through the body, and such sensors can be arranged in an array or pattern that optimizes both the signal-to-noise ratio and power consumption of such sensors. These sensors can detect breathing (e.g., by listening to the lungs), breathing sounds (e.g., breathing, snoring) and problems (e.g., sleep apnea, etc.), heart rate (listening to the heartbeat), the user's voice (by the sound of).

The biometric monitoring device of the present invention may also include a galvanic skin response (GSR) circuit to measure the response of the user's skin to emotional and physical stimuli or physiological changes (eg, transitions in sleep stages). In some embodiments, the biometric monitoring device may be a wrist or arm mounted device that incorporates a strap made of conductive rubber or fabric so that the galvanic skin response electrodes can be hidden in the strap. Because the galvanic skin response circuit may be subject to changing temperature and environmental conditions, it may also include circuitry for automatic calibration, such as two or more switchable reference resistors in parallel or in series with the human skin/electrode path , which allows real-time measurements of known resistors to characterize the response of galvanic skin response circuits. The reference resistor can be switched on and off in the measurement path so that it can be measured independently and/or simultaneously with the electrical resistance of the human skin.

Circuitry for performing PPG

The PPG circuit can be optimized for the best quality signal regardless of a variety of environmental conditions, including but not limited to motion, ambient light, and skin tone. The following circuits and techniques can be used to perform this optimization (see Figures 16A to 16J);

-Sample and hold circuits and differential/instrumentation amplifiers that can be used for PPG sensing. The output signal is the amplified difference between the current and previous samples of the reference given voltage.

- Controlled current source to compensate "bias" current before the transimpedance amplifier. This allows larger gains to be applied at the transimpedance amplifier stage.

- A sample and hold circuit for current feedback applied to the photodiode (before the transimpedance amplifier). This can be used for ambient light removal, or "bias" current removal, or as a pseudo-differential amplifier (dual rails may be required).

-Differential/instrumentation amplifier with ambient light cancellation.

- A photodiode that compensates the current dynamically generated by the DAC.

- A photodiode that compensates the current generated dynamically by a controlled voltage source.

- Ambient light removal using the "switched capacitor" method.

- A photodiode that compensates the current generated by a constant current source (it can also be done with a constant voltage source and a resistor).

- Ambient light removal and differentiation between consecutive samples.

- Ambient light removal and differentiation between consecutive samples.

16A illustrates an example schematic of a sample and hold circuit and differential/instrumentation amplifier that may be used for PPG sensing. The output signal in such a circuit may be the amplified difference between the current sample and the previous sample of a reference given voltage.

16B illustrates an example schematic of a circuit for a PPG sensor that uses a controlled current source to compensate for the "bias" current prior to the transimpedance amplifier. This allows larger gains to be applied at the transimpedance amplifier stage.

16C illustrates an example schematic of a circuit for a PPG sensor using a sample and hold circuit for current feedback applied to a photodiode (before the transimpedance amplifier). This circuit can be used for ambient light removal, or "bias" current removal, or as a pseudo-differential amplifier.

16D illustrates an example schematic of a circuit for a PPG sensor using a differential/instrumentation amplifier with ambient light cancellation functionality.

16E illustrates an example schematic of a circuit for a PPG sensor that uses a photodiode to compensate the current dynamically generated by a DAC.

16F illustrates an example schematic diagram of a circuit for a PPG sensor that uses a photodiode to compensate current dynamically generated by a controlled voltage source.

16G illustrates an example schematic of a circuit for a PPG sensor including ambient light removal functionality using a "switched capacitor" approach.

16H illustrates an example schematic of a circuit for a PPG sensor that uses a photodiode to compensate the current generated by a constant current source (this can also be done using a constant voltage source and a resistor).

16I illustrates an example schematic of a circuit for a PPG sensor including ambient light removal functionality and differentiation between consecutive samples.

16J illustrates an example schematic of a circuit for ambient light removal and differentiation between consecutive samples.

Various circuits and concepts related to heart rate measurement using a PPG sensor are discussed in greater detail in U.S. Provisional Patent Application No. 61/946,439, filed February 28, 2014, which was previously published in "Related Application Incorporated by reference in the "Cross-Reference" section of , and with respect to the content of heart rate measurements with PPG sensors and circuits, methods, and systems for performing such measurements, for example, to compensate for sensor saturation, ambient light, and skin tone Again incorporated herein by reference.

biometric feedback

Some embodiments of a biometric monitoring device may provide feedback to a user based on one or more biometric signals. In one embodiment, the PPG signal may be presented to the user as a real-time or near real-time waveform on the display of the biometric monitoring device (or on the display of a secondary device in communication with the biometric monitoring device). This waveform provides feedback similar to that displayed on an ECG or EKG machine. In addition to providing the user with an indication of the PPG signal that can be used to estimate various cardiac metrics (e.g., heart rate), the waveform can also provide feedback that can enable the user to optimize the position and pressure at which they wear the biometric monitoring device . For example, the user may see that the waveform has a low amplitude. In response to this, the user may attempt to move the location of the biometric monitoring device to a different location that gives a higher amplitude signal. In some implementations, based on such indications, the biometric monitoring device may provide instructions to the user to move or adjust the fit of the biometric monitoring device in order to improve signal quality.

In another embodiment, feedback on the quality of the PPG signal may be provided to the user via methods other than displaying the waveform. If the signal quality (eg, signal-to-noise ratio) exceeds a certain threshold, the biometric monitoring device may sound an audible alarm (eg, beep). The biometric monitoring device may provide visual cues (e.g., via use of a display) to the user to change the position of the sensors and/or increase the pressure on the wearing device (e.g., by tightening the wrist strap if the device is worn on the wrist) .

Biometric feedback may be provided for sensors other than PPG sensors. For example, if the device uses ECG, EMG, or is connected to a device that does any of these, it can provide feedback to the user about the waveforms from those sensors. If the signal-to-noise ratio of these sensors is low or the signal quality is compromised for other reasons, the user can be indicated how they can improve the signal. For example, if heart rate cannot be detected from the ECG sensor, the device may provide a visual message to the user instructing them to wet or wet the ECG electrodes to improve the signal.

environmental sensor

Some embodiments of the biometric monitoring device of the present invention may use one, some or all of the following environmental sensors to, for example, acquire environmental data, including the environmental data summarized in the table below. Such biometric monitoring devices are not limited to the number or types of sensors specified below, but other sensors that acquire the environmental data outlined in the table below may be used. All combinations and permutations of environmental sensors and/or environmental data are intended to fall within the scope of the present invention. Furthermore, a device may derive environmental data from corresponding sensor output data, but is not limited to the types of environmental data it may derive from said sensors.

Notably, embodiments of the biometric monitoring device of the present invention may use one or more or all of the environmental sensors described herein and one or more or all of the physiological sensors described herein. Indeed, the biometric monitoring device of the present invention may use any sensor now known or later developed to acquire any or all of the environmental and physiological data described herein, all of which are intended to fall within the scope of the present invention .

In one embodiment, the biometric monitoring device may include, for example, an altimeter sensor disposed or located inside the housing of the device. (See, eg, Figures 12B and 12C; Figure 12C illustrates an example of a portable biometric monitoring device having physiological sensors, environmental sensors, and a position sensor connected to a processor). In this case, the device housing may have vents that allow the interior of the device to measure, detect, sample and/or experience any changes in external pressure. In one embodiment, the vents may prevent water from entering the device while facilitating the measurement, detection and/or sampling of changes in pressure via the altimeter sensor. For example, the exterior surface of a biometric monitoring device may include a vent type configuration or architecture (e.g., Gore ^™ vents) that allows ambient air to move in and out of the device's housing (which allows an altimeter sensor to measure, detect, and/or sample changes in pressure), but reduce, prevent and/or minimize the flow of water and other liquids into the housing of the device.

In one embodiment, the altimeter sensor may be filled with a gel that allows the sensor to experience pressure changes outside of the gel. The gel can act as a relatively impermeable, incompressible, yet flexible membrane that transmits external pressure changes to the altimeter while physically separating the altimeter (and other internal components) from the external environment. The use of a gel-filled altimeter can give the device a higher level of environmental protection with or without the use of environmentally sealed vents. The device may have a higher survivability rate with the gel-filled altimeter in locations including, but not limited to: locations with high humidity, washing machines, dishwashers, clothes dryers, steam rooms, or saunas Cabinets, showers, sinks, bathtubs, and any location where the appliance may be exposed to moisture, exposed to liquid, or submerged in liquid.

Sensor Integration/Signal Processing

Some embodiments of the biometric monitoring device of the present invention may use data from two or more sensors to calculate corresponding physiological or environmental data as seen in the table below (e.g., data from two or more sensors may be used in combination sensor data to determine metrics such as those listed below). A biometric monitoring device may include, but is not limited to, the number, type, or combination of sensors specified below. Furthermore, such biometric monitoring devices may derive included data from corresponding sensor combinations, but are not limited to the number or types of data computable from corresponding sensor combinations.

In some embodiments, the biometric monitoring device may also include a near field communication (NFC) receiver/transmitter to detect the proximity of another device, such as a mobile phone. When a biometric monitoring device is brought into or comes into detectable proximity to a second device, it can trigger the initiation of new functionality on the second device (e.g., launching an "app" on a mobile phone and using Physiological data from said device is radio-synchronized to a second device). (See, eg, Figure 10). Indeed, the biometric monitoring device of the present invention may implement U.S. Provisional Patent Application 61/606,559 (“Near Field Communication System, and Method of Operating Same”), filed March 5, 2012. , Inventor: James Park (James Park, the contents of which are incorporated herein by reference for this purpose) in any of the circuits and techniques described and/or illustrated.

10 illustrates an example of a portable biometric monitoring device with a bicycle application on it that can display bicycle speed and/or pedaling cadence, among other metrics. The application may be launched whenever a biometric monitoring device approaches a passive or active NFC tag. This NFC tag can be attached to the user's handle bar.

In another embodiment, a biometric monitoring device may include a location sensor (eg, GPS circuitry) and a heart rate sensor (eg, photoplethysmography circuitry) to generate GPS or location-related data and heart-rate-related data, respectively. (See, eg, Figures 12B and 12C). The biometric monitoring device can then fuse, process and/or combine data from these two sensors/circuits to determine, correlate and/or "mapping" geographic areas. In this manner, the biometric monitoring device may identify geographic regions that increase or decrease measurable user metrics including, but not limited to, heart rate, stress, activity level, amount of sleep, and/or calorie intake.

Additionally or alternatively, some embodiments of the biometric monitoring device may use GPS-related data and photoplethysmographic-related data (notably, each of which may be considered a data stream) to monitor activity based on activity levels (e.g., as via The user's acceleration, velocity, position, and/or distance traveled (as determined by GPS measurements and/or determined from GPS-related data) determine or correlate the user's heart rate. (See, eg, Figures 12B and 12C). Here, in one embodiment, heart rate as a function of speed can be "plotted" for the user, or the data can be broken down into different levels, including but not limited to sleep, rest, sedentary, moderately active, active , and highly active.

Indeed, some embodiments of the biometric monitoring device may also correlate GPS-related data with a database of predetermined geographic locations with activities associated therewith for a set of predetermined conditions. For example, activity determination and corresponding physiological classification (eg, heart rate classification) may include correlating the user's GPS coordinates (corresponding to locations of exercise equipment, health clubs, and/or gyms) with physiological data. In these situations, the user's heart rate may be automatically measured and displayed, eg, during a gym workout. Notably, many physiological classifications can be based on GPS-related data, including position, acceleration, altitude, distance, and/or velocity. Such databases include geographic data, and physiological data may be compiled, formed and/or stored on a biometric monitoring device and/or an external computing device. Indeed, in one embodiment, users can create their own location databases or add to or modify location databases to better categorize their activities.

In another embodiment, a user may wear multiple biometric monitoring devices (having any of the features described herein) simultaneously. The biometric monitoring devices of this embodiment may communicate with each other or with remote devices using wired or wireless circuitry to calculate, for example, biometric or physiological qualities or quantities that may otherwise be difficult or inaccurate to calculate, such as pulse transit time. The use of multiple sensors may also improve the accuracy and/or precision of biometric measurements over that of a single sensor. For example, having biometric tracking devices on the waist, wrists, and ankles can improve detection of a user's steps (compared to the case of a single device in only one of those locations). Signal processing may be performed on the biometric tracking device in a distributed or centralized manner to provide improved measurements over the case of a single device. This signal processing can also be performed remotely and communicated back to the biometric tracking device after processing.

In another embodiment, heart rate or other biometric data may be correlated to the user's food log (a log of the user's ingested food, its nutritional content and its portions). Food log entries may be automatically entered into the food log or may be made by the user himself via an auxiliary or remote device, such as a smartphone, in communication with the biometric monitoring device (or with the biometric monitoring device, or some other device in communication with the biometric monitoring device, such as server) interaction. Information regarding the biometric response of the user's body to one or more food inputs may be presented to the user. For example, if a user drinks coffee, their heart rate may rise due to caffeine. In another example, if a user eats a larger portion of food late at night, it may take longer than usual to fall asleep. Any combination of food inputs and corresponding outcomes in biometrics can be incorporated into such feedback systems.

Fusion of food intake data with biometric data may also enable some embodiments of the biometric monitoring device to estimate the user's glucose level. This may be especially useful for users with diabetes. Through algorithms that relate glucose levels to the user's activities (eg, walking, running, calorie burn) and nutrient intake, the biometric monitoring device may be able to advise the user when they may have abnormal blood sugar levels.

Handle task delegation

Embodiments of a biometric monitoring device may include one or more processors. For example, a stand-alone application processor may be used to store and execute applications that utilize sensor data acquired and processed by one or more sensor processors (processors that process data from physiological, environmental and/or activity sensors). Where there are multiple sensors, there may also be multiple sensor processors. The application processor can also have sensors directly connected to it. The sensor and application processor can exist as separate discrete chips or within the same packaged chip (multi-core). A device may have a single application processor, or an application processor and a sensor processor, or multiple application processors and sensor processors.

In one embodiment, the sensor processor may be placed on a daughterboard consisting of all analog components. This board may have some of the electronics typically found on a main PCB, such as but not limited to transimpedance amplifiers, filter circuits, level shifters, sample and hold circuits, and a microcontroller unit. Such a configuration may allow the daughterboard to connect to the main PCB via the use of digital connections rather than analog connections (in addition to any necessary power or ground connections). Digital connections can have various advantages over analog daughterboard-to-main PCB connections, including but not limited to reduced noise and reduced number of necessary cables. The daughterboard can be connected to the motherboard through the use of a flex cable or set of wires.

Multiple applications can be stored on the application processor. An application program may consist of executable code and data for the application program, but is not limited to these. Data may consist of graphics or other information needed to execute the application, and it may be an output of information generated by the application. Both the executable code and data for the application can reside on the application processor (or memory incorporated therein), or the data for the application can be stored and retrieved from external memory. External memory may include, but is not limited to, NAND flash memory, NOR flash memory, flash memory on another processor, other solid state storage devices, mechanical or optical disks, RAM, and the like.

Executable code for applications may also be stored in external memory. When an application processor receives a request to execute an application, the application processor can retrieve executable code and/or data from the external storage device and execute the executable code and/or data. The executable code may be stored temporarily or permanently on the application processor's memory or storage device. This allows the application to execute more quickly on the next execution request because the retrieval step is eliminated. When execution of an application is requested, the application processor may retrieve all or a portion of the executable of the application. In the latter case, only the portion of the executable code that is needed at the time is retrieved. This allows execution of applications that are larger than the application processor's memory or storage.

The application processor may also have memory protection features to prevent applications from overwriting, corrupting, interrupting, blocking, or otherwise interfering with other applications, sensor systems, the application processor, or other components of the system.

Applications can be loaded to the application processor and/or any external storage via a variety of wired, wireless, optical or capacitive mechanisms including but not limited to USB, Wi-Fi, Bluetooth, Bluetooth Low Energy, NFC, RFID, Zigbee on the device.

Applications can also be cryptographically signed with an electronic signature. The application handler can restrict the execution of the application to those with the correct signature.

System Integration in Biometric Monitoring Devices

In some embodiments of the biometric monitoring device, or some sensors or electronic systems in the biometric monitoring device may be integrated with each other or may share components or resources. For example, a photodetector for an optical heart rate sensor such as may be used for heart rate as discussed in U.S. Provisional Patent Application No. 61/946,439, filed February 28, 2014 and previously incorporated herein by reference. sensor) can also act as a photodetector for determining ambient light levels, which can be used, for example, to correct for the effect of ambient light on heart rate sensor readings. For example, if the light source for such a heart rate detector is turned off, the light measured by the photodetector may indicate the amount of ambient light present.

In some implementations of the biometric monitoring device, the biometric monitoring device may be configured with or communicate with an on-board optical sensor, such as a component in an optical heart rate monitor. For example, the photodetector of an optical heart rate sensor (or, if present, an ambient light sensor) may also act as a receiver for an optical transmit channel such as infrared communication.

In some embodiments of the biometric monitoring device, a hybrid antenna may be included that combines a radio frequency antenna (such as a Bluetooth antenna or a GPS antenna) with an inductive loop (such as may be used in Near Field Communication (NFC) tags or inductive charging systems). In such embodiments, the functionality of two different systems can be provided in one integrated system, saving packaging volume. In such hybrid antennas, an inductive loop may be placed in close proximity to the radiator of the inverted-F antenna. The inductive loop may be inductively coupled to the radiator, allowing the inductive loop to act as a planar element of the antenna for radio frequency purposes, thus forming eg a flat inverted-F antenna. At the same time, the inductive loop can also serve its normal functions, such as supplying current to the NFC chip via inductive coupling with the electromagnetic field generated by the NFC reader. Examples of such hybrid antenna systems are discussed in more detail in U.S. Provisional Patent Application No. 61/948,470, filed March 5, 2014, previously published in "Cross-Reference to Related Applications" section is incorporated herein by reference and again hereby is hereby incorporated by reference with respect to what is indicated at the hybrid antenna structure. Of course, such hybrid antennas may also be used in other electronic devices than biometric monitoring devices, and such non-biometric monitoring device use of hybrid antennas is encompassed within the scope of the present invention.

Method of wearing the device

Some embodiments of a biometric monitoring device may include a housing sized and shaped to facilitate securing the biometric monitoring device to a user's body during normal operation, wherein the device does not measurably or significantly affect the user when coupled to the user. activity. The biometric monitoring device can be worn in different ways depending on the particular sensor package integrated into the biometric monitoring device and the data the user will want to acquire.

A user may wear some embodiments of the biometric monitoring device of the present invention on his wrist or ankle (or arm or leg) by using a strap (which is flexible and thus easily adaptable to the user). The strap may have an adjustable circumference, thus allowing it to be fitted to the user. The straps can be constructed from a material that shrinks when exposed to heat, thus allowing the user to create a custom fit. The strap is detachable from the "electronics" portion of the biometric monitoring device and replaceable if necessary.

In some embodiments, a biometric monitoring device may consist of two main components: a body (containing the "electronics") and a strap (facilitating attachment of the device to the user). The body may comprise a housing (eg made of plastic or a plastic-like material) and an extension protrusion (eg made of metal or metal-like material) protruding from the body. (See, eg, Figures 2C to 3C). The straps (eg made of thermoplastic urethane) can be attached to the body eg mechanically or adhesively. The strap may extend a portion from the circumference of the user's wrist. The distal end of the urethane strap can be connected with a Velcro or hook and loop elastic fabric strap (wrapped in a D-ring on one side and then attached back to itself). In this embodiment, the closure mechanism may allow infinite strap length adjustment by the user (unlike indexed holes and mechanical snap closures). The Velcro or elastic fabric may be attached to the strap in a manner that allows it to be replaced (eg, if it is worn before the end of the useful life of the device or is otherwise undesirably worn). In one embodiment, the Velcro or fabric can be attached to the strap by screws or rivets and/or glue, adhesive and/or snaps.

Embodiments of the biometric monitoring device of the present invention may also be integrated into and worn in necklaces, chest straps, bras, adhesive patches, glasses, ear cords, or toe bands. Such biometric monitoring devices can be built in in such a way that the sensor package/portion of the biometric monitoring device is removable and can be worn in any number of ways including but not limited to those listed above.

In another embodiment, an embodiment of the biometric monitoring device of the present invention may be worn to clip to a piece of clothing or to be stored in clothing (eg, pocket) or accessory (eg, handbag, backpack, purse). Because such biometric monitoring devices may not be close to the user's skin, in embodiments that include heart rate measurement, the device may be manually placed into a particular mode by the user (e.g., by pressing a button, covering a capacitive touch sensor with a fingertip) , etc., possibly with a heart rate sensor embedded in a button/sensor) in a discrete, "on-demand" context, or automatically (once the user places the device on the skin (e.g., applies a finger to an optical heart rate sensor)) Measurement.

User interface with device

Some embodiments of a biometric monitoring device may include functionality for one or more methods to allow interaction with the device locally or remotely.

In some embodiments, the biometric monitoring device may communicate data visually via a digital display. The physical embodiment of this display can use any one or more display technologies, including but not limited to one or more of the following: LED, LCD, AMOLED, electronic ink, clear display technology, graphics display, and other display technologies , such as TN, HTN, STN, FSTN, TFT, IPS and OLET. This display can show data obtained or stored locally on the device or can display data obtained remotely from other devices or Internet services. A biometric monitoring device may use a sensor (eg, an ambient light sensor "ALS") to control or adjust the amount of screen backlight (if a backlight is used). For example, in dim lighting situations, the display can be dimmed to save battery life, while in bright lighting situations, the display brightness can be increased to make it easier to read by the user.

In another embodiment, a biometric monitoring device may use single or multi-color LEDs to indicate the status of the device. Statuses that a biometric monitoring device may indicate using LEDs may include, but are not limited to, biometric status such as heart rate, or application status such as an incoming message or a goal reached. These states may be indicated by the color of the LED, the LED on or off (or at intermediate intensity), the pulsing of the LED (and/or its rate), and/or the pattern of light intensity from completely off to full brightness. In one embodiment, the LEDs can have their intensity and/or color modulated with the phase and frequency of the user's heart rate.

In some embodiments, using an electronic ink display may allow the display to remain on without draining the battery of the non-reflective display. This "always on" functionality can provide a pleasant user experience in the case of, for example, a watch application where the user can simply glance at the biometric monitoring device to see the time. The e-ink display always shows content regardless of the device's battery life, allowing the user to see the time (as it would on a traditional watch).

Some implementations of the biometric monitoring device may use lights such as LEDs to display the user's heart rate (by modulating the amplitude of the light emitted at the frequency of the user's heart rate). The device may delineate heart rate zones (eg, aerobic, anaerobic, etc.) via the color of the LEDs (eg, green, red) or a series of LEDs (eg, a progress bar) that light up as the heart rate changes. The biometric monitoring device may be integrated or incorporated into, or in communication with, another device or structure such as glass or goggles to display this information to the user.

Some embodiments of the biometric monitoring device may also communicate information to the user via physical movement of the device. One such embodiment of a method of physically moving the device is to use a vibration induced motor. The device can use this method alone or in combination with a number of other motion-inducing techniques.

In some implementations, the biometric monitoring device may communicate information to the user via audio feedback. For example, speakers in a biometric monitoring device may communicate information through the use of audio tones, speech, singing, or other sounds.

In various embodiments of the biometric monitoring device, these three information communication methods (visual, motor, and auditory) may be used alone or in any combination with each other or another communication method that conveys any one or more of the following information to use:

The user needs to wake up at a specific time

The user should wake up when they are in a certain sleep stage

The user should fall asleep at a certain time

• The user should wake up when they are in a certain sleep stage and within a preselected window of time delimited by the earliest and latest time the user wants to wake up.

· Email received

• The user has been inactive for a certain period of time. Notably, this can be integrated with other applications such as meeting calendars or sleep tracking applications to schedule, streamline or adjust the behavior of inactivity reminders.

· The user has been active for a certain period of time

· User has an appointment or calendar event

A user has reached a certain activity metric

· The user has walked a certain distance

The user has reached a certain mileage

· The user has reached a certain speed

· The user has accumulated to a certain altitude

· The user has walked a certain number of steps

The user has recently taken a heart rate measurement

The user's heart rate has reached a certain level

· The user has a normal, active or resting heart rate that is a specific value or in a specific range

The user's heart rate has entered or exited a target range or training zone

The user has new heart rate "zone" goals to achieve, in this case heart rate zones trained for activities such as running, biking, swimming, etc.

· The user has swum a lap or completed a certain number of laps in the pool

· The external device has information that needs to be conveyed to the user, such as an incoming phone call or any of the above alerts

• The user has reached a certain fatigue goal or limit. In one embodiment, fatigue may be determined via a combination of heart rate, galvanic skin response, motion sensor, and/or respiration data

These examples are provided for illustration and not intended to limit the scope of information that may be conveyed (eg, to a user) by such embodiments of a biometric monitoring device. Note that the data used to determine whether the reminder condition is met can be obtained from the first device and/or one or more auxiliary devices. The biometric monitoring device itself may determine whether criteria or conditions for the alert have been met. Alternatively, a computing device (eg, a server and/or mobile phone) in communication with the biometric monitoring device may determine when a reminder should occur. Other information that a biometric monitoring device may communicate to a user may be envisioned by one of ordinary skill in the art in view of this disclosure. For example, a biometric monitoring device may communicate with a user when a goal has been met. Criteria for meeting this goal may be based on physiological, contextual, and environmental sensors on the first device and/or other sensor data from one or more auxiliary devices. The goal may be set by a user or may be set by the biometric monitoring device itself and/or another computing device (eg, a server) in communication with the biometric monitoring device. In an example embodiment, the biometric monitoring device may vibrate when the biometric goal is met.

Some embodiments of the biometric monitoring device of the present invention may be equipped with wireless and/or wired communication circuitry to display data on a secondary device in real time. For example, such biometric monitoring devices may be able to communicate with mobile phones via Bluetooth low energy in order to give the user real-time feedback on heart rate, heart rate changes and/or stress. Such biometric monitoring devices may train or empower the user to "moment" to breathe in a particular way to relieve tension, such as by taking slow, deep breaths. Stress can be quantified or assessed via changes in heart rate, heart rate variability, skin temperature, motor activity data, and/or galvanic skin response.

Some embodiments of a biometric monitoring device may receive input from a user via one or more local or remote input methods. One such embodiment of local user input may use a sensor or set of sensors to translate the user's movements into commands to the device. Such motions may include, but may not be limited to, touching, turning the wrist, flexing one or more muscles, and swinging the arm. Another method of user input may be through the use of buttons such as, but not limited to, capacitive touch buttons, capacitive on-screen buttons, and mechanical buttons. In one embodiment, the user interface buttons may be made of metal. In embodiments where the screen uses capacitive touch detection, it can always be sampled and ready to respond to any gesture or input without an intervening event, such as pushing a physical button. Such biometric monitoring devices may also take input through the use of audio commands. All of these input methods can be integrated locally into the biometric monitoring device or into a remote device that can communicate with such biometric monitoring device via a wired or wireless connection. Additionally, the user may also be able to manipulate the biometric monitoring device via a remote device. In one embodiment, this remote device may have Internet connectivity.

Alarm system

In some embodiments, the biometric monitoring device of the present invention can act as a wrist-mounted vibrating alarm to quietly wake the user from sleep. Such biometric monitoring devices may track the user's sleep quality, wake-up cycles via one or a combination of heart rate, heart rate variability, galvanic skin response, motion sensing (e.g., accelerometer, gyroscope, magnetometer), and skin temperature. , sleep latency, sleep efficiency, sleep stages (eg, deep sleep versus REM), and/or other sleep-related metrics. The user can specify the desired alarm time or time window (for example, set the alarm to go off at 7 am and 8 am). Such embodiments may use one or more of the sleep metrics to determine the optimal time within the alarm window to wake up the user. In one embodiment, when the vibrating alarm is active, the user can cause it to be dismissed by tapping or touching the device (detected, for example, via motion sensors, pressure/force sensors, and/or capacitive touch sensors in the device) or turn off. In one embodiment, the device may attempt to wake the user at the optimal moment in the sleep cycle by initiating a small vibration at a particular user sleep stage or at a time prior to the alarm setting. It may gradually increase the intensity or pronounceability of the vibration as the user progresses towards wakefulness or towards an alarm setting. (See, eg, Figure 8).

8 illustrates the functionality of an example portable biometric monitoring device smart alert feature. A biometric monitoring device may be capable of detecting or may communicate with a device that can detect a user's sleep stage or state (eg, light or deep sleep). The user can set a window of time (eg, 6:15 am to 6:45 am) that they will wish to wake up. Smart alarms can be triggered by the user falling into a light sleep state during the alarm window.

The biometric monitoring device may be configured to allow the user to select or create an alert vibration pattern of his choice. The user may be able to "snooze" or delay the alarm event. In one embodiment, the user may be able to set an amount of delay for the "snooze" feature: the delay is the amount of time before the alarm will sound again. It may also be possible to set the number of times the snooze feature may be activated per alarm period. For example, the user may select a snooze delay of 5 minutes and a maximum number of consecutive snoozes of 3. So it can press snooze 3 times to delay the alarm for 5 minutes each time it presses snooze to delay the alarm. In such embodiments, the snooze function will not turn off the alarm if the user tries to press snooze a fourth time.

Some biometric monitoring devices may have information about a user's calendar and/or schedule. The user's calendar information can be entered directly into the biometric monitoring device or it can be downloaded from a different device such as a smartphone. This information can be used to automatically set alarms or alarm characteristics. For example, if the user has a meeting at 9 am, the biometric monitoring device may automatically wake the user at 7:30 am to allow the user sufficient time to prepare for and/or arrive at the meeting. The biometric monitoring device may determine the amount of time the user needs to prepare for the meeting based on the user's current location, the location of the meeting, and the amount of time it will take to get from the user's current location to the meeting location. Alternatively, historical data about the time it takes the user to arrive at the meeting location and/or prepare to leave for the meeting (e.g., the time it takes them to wake up in the morning, take a shower, eat breakfast, etc.) can be used to determine when to wake the user. Similar functionality can be used for calendar events other than meetings, such as meal times, sleep times, nap times, and exercise times.

In some embodiments, the biometric monitoring device may use information about when the user wants to fall asleep to determine when an alarm should sound to wake the user. This information supplements the calendar information described in this article. A user may have a goal of an approximate number of hours of sleep that they would like to have each night or each week. The biometric monitoring device can set a morning alarm at an appropriate time for the user to meet these sleep goals. In addition to the amount of sleep the user desires each night, other sleep goals that the user can set may include, but are not limited to, the amount of deep sleep, REM sleep, and light sleep the user experiences while sleeping, all of which can be measured by biometrics. A monitoring device is used to determine when to set the alarm in the morning. Additionally, users may be reminded at night when they should go to bed to meet their sleep goals. Additionally, users may be reminded during the day when they should take a nap to meet their sleep goals. The time to remind the user that they should take a nap may be determined by factors that optimize the user's sleep quality during the nap, subsequent naps, or nighttime sleep. For example, if a user naps early in the morning, the user may have a hard time falling asleep at night. The user may also be advised to eat certain foods or beverages or avoid certain foods or beverages to optimize their sleep quality. For example, a user may be discouraged from drinking alcohol close to their bedtime because alcohol may reduce the quality of their sleep. Users may also be advised to perform certain activities or avoid certain activities to optimize their sleep quality. For example, users might be encouraged to exercise in the afternoon to improve their sleep quality. Users may be discouraged from exercising or watching TV near their bedtime to improve their sleep quality.

User interface with auxiliary devices

In some embodiments, the biometric monitoring device can transmit data and/or commands to and/or receive data and/or commands from the secondary electronic device. The secondary electronic device may communicate directly or indirectly with the biometric monitoring device. Direct communication herein means that data is transmitted between a first device and a secondary device without any intermediary device. For example, two devices may communicate with each other via a wireless connection (such as Bluetooth) or a wired connection (such as USB). Indirect communication refers to the transmission of data between a first device and a secondary device by means of one or more intermediate third devices that relay the data. Third devices may include, but are not limited to, wireless repeaters (eg, WiFi repeaters), computing devices such as smartphones, laptops, desktop or tablet computers, cell phone towers, computer servers, and other networked electronics. For example, a biometric device may send data to a smartphone, which forwards the data via a cellular network data connection to a server connected to the cellular network via the Internet.

In some embodiments, the secondary device serving as a user interface to the biometric monitoring device may consist of a smartphone. An application on the smartphone may facilitate and/or enable the smartphone to act as a user interface to the biometric monitoring device. The biometric monitoring device can send biometric and other data to the smartphone in real time or with some delay. The smartphone may send one or more commands to the biometric monitoring device, eg, instructing it to send biometric and other data to the smartphone, in real time or with some delay. For example, if the user enters a mode to track a run in an app, the smartphone can send commands to the biometric device instructing it to send data in real time. Thus, users can track their run on their app without any delay as they progress.

Such smartphones may have one or more applications to enable users to view data from their biometric devices. The application may open to a "dashboard" page by default when the user launches or opens the application. On this page, a summary of data totals such as total steps, floors climbed, miles traveled, calories burned, calories burned and water consumed can be shown. Other relevant information can also be displayed, such as the last time the app received data from the biometric monitoring device, metrics about the previous night's sleep (e.g., when the user fell asleep, woke up, and how long they slept), and whether the user was able to eat during the day How many calories to maintain their calorie goals (for example, to achieve a calorie deficit goal for weight loss). A user may be able to select which of these and other metrics are displayed on the dashboard screen. A user may be able to see these and other metrics from previous days on the dashboard. It may be possible to access previous days by pressing a button or icon on the touch screen. Alternatively, gestures such as swiping left or right may enable the user to navigate current and previous metrics.

The smartphone application may also have another page that provides a summary of user activity. Activities may include, but are not limited to, walking, running, biking, cooking, sitting, working, swimming, errands, lifting weights, commuting, and yoga. Metrics related to these activities can be presented on this page. For example, a bar graph may show how many steps a user took during different parts of the day (eg, how many steps are taken every 5 minutes or every 1 hour). In another example, the amount of time the user spent performing a certain activity and how many calories were burned during this time period may be displayed. Similar to the dashboard page, the application may provide navigation functionality to allow the user to view these and other metrics for the past few days. Other time periods such as hours, minutes, weeks, months or years may also be selected by the user to enable them to view trends and metrics of their activity over shorter or larger time spans.

The smartphone application may also have an interface to log what the user has eaten or will eat. This interface may have a keyword search feature to allow users to quickly find foods they wish to type into their diary. As an alternative to, or in addition to, searching for foods, a user may be able to navigate through a menu or series of menus to find foods to log. For example, a user may select the following series of categories: Breakfast/Cereals/Health/Oatmeal to arrive at the food they wish to log (eg, apple-flavored oatmeal). At any of these menus, the user may be able to perform a keyword search. For example, a user may search for "oatmeal" after having selected the category "breakfast" to search for the keyword "oatmeal" within the category of breakfast foods. After having selected the food they would like to log, the user may be able to modify or enter the portion size and nutritional content. After at least one food has been logged, the app can display a summary of the foods logged for a certain period of time (e.g., day) and the nutritional content (individual and overall calorie content, vitamin content, sugar content, etc.) of the food. content, etc.).

The smartphone application may also have a page that displays measurements about the user's body (eg, the user's weight, body fat percentage, BMI, and waist size). It may display one or more curves showing trends of one or more of these metrics over a certain period of time (eg, two weeks). A user may be able to select a value for this time period and view a previous time period (eg, last month).

The smartphone application may also have a page that allows the user to enter how much water the user has consumed. Whenever the user drinks some water, he can key in the amount in the unit of his choice (eg, ounces, cups, etc.). The application may display the total amount of all water that the user has logged over a certain period of time (eg, a day). The application allows the user to view previously logged water entries and daily totals for previous days and the current day.

The smartphone application may also have a page displaying the user's online friends. This "friends" page may enable users to add or request new friends (eg, by searching their names or by their email addresses). This page can also display a leaderboard of users and their friends. Users and their friends can be ranked based on one or more metrics. For example, users and their friends can be ranked using their total step count over the past seven days.

The smartphone application may also have a page showing metrics about the user's sleep the previous night and/or previous nights. This page may also enable the user to log when they have slept in the past by specifying when they went to bed and when they woke up. Users may also be able to have subjective metrics about their sleep (eg, poor night's rest, good night's rest, excellent night's rest, etc.). A user may be able to view these metrics for the past today or a period of time (eg, two weeks). For example, the sleep page may by default display a bar graph of the amount of sleep the user has had each night for the last two weeks. The user may also be able to view a bar graph of the amount of sleep time for each night of the user for the most recent month.

The user may also be able to access the full capabilities of the smartphone applications described herein via alternative or additional interfaces (e.g., be able to enter a food log, view a dashboard, etc.). In one embodiment, this alternative interface may consist of a web page hosted by a server in indirect communication with the biometric monitoring device. The web pages can be accessed via any Internet-connected device using a program such as a web browser.

Wireless Connectivity and Data Transmission

Some embodiments of the biometric monitoring devices of the present invention may include wireless communication means to transmit and receive information from the Internet and/or other devices. Wireless communication may consist of one or more interfaces such as Bluetooth, ANT, WLAN, powerline networking, and cellular networks. These are provided as examples and should not be understood as excluding other existing wireless communication methods or protocols or wireless communication technologies or protocols that have not yet been invented.

Wireless connections may be bi-directional. The biometric monitoring device can transmit, communicate and/or push its data to other devices, such as smartphones, computers, etc. and/or the Internet, such as web servers and the like. The biometric monitoring device may also receive, request and/or pull data from other devices and/or the Internet.

Biometric monitoring devices may act as repeaters providing communications for other devices to each other or to the Internet. For example, a biometric monitoring device may be connected to the Internet via WLAN and equipped with an ANT radio. The ANT device can communicate with the biometric monitoring device to transmit its data to the Internet (and vice versa) via the biometric monitoring device's WLAN. As another example, a biometric monitoring device may be equipped with Bluetooth. If a Bluetooth-enabled smartphone comes within range of the biometric monitoring device, the biometric monitoring device can transmit data to or receive data from the Internet via the smartphone's cellular network. Data from another device may also be transmitted to the biometric monitoring device and stored (or vice versa) or transmitted at a later time.

Embodiments of the biometric monitoring device of the present invention may also include functionality for streaming or transmitting web content for display on the biometric monitoring device. A typical example of this content follows:

1. History of heart rate and/or other data measured by the device but stored remotely

2. Historical curves of user activity and/or food consumed and/or sleep data measured by other devices and/or stored remotely (e.g., for example at a website like fitbit.com)

3. Historical curves of other user tracking data stored remotely. Examples include heart rate, blood pressure, arterial stiffness, blood sugar levels, cholesterol, duration of TV watching, duration of video game playing, mood, etc.

4. Training and/or dieting data based on one or more of the user's heart rate, current weight, weight goals, food intake, activity, sleep, and other data.

5. User's progress towards heart rate, weight, activity, sleep and/or other goals.

6. Summary statistics, graphs, badges and/or metrics describing the aforementioned data (e.g., "rank")

7. Comparison between the aforementioned data of the user and similar data of his "friends" with similar devices and/or tracking methods

8. Social content, such as Twitter feeds, instant messaging and/or Facebook updates

9. Other online content such as newspaper articles, horoscopes, weather reports, RSS feeds, comics, crosswords, classified ads, stock reports, and websites

10. Email messages and calendar scheduler

Content may be delivered to the biometric monitoring device according to different contexts. For example, in the morning, news and weather reports may be displayed along with the user's sleep data from the previous night. In the evening, a daily overview of the day's activities can be displayed.

Various embodiments of biometric monitoring devices as disclosed herein may also include NFC, RFID, or other short-range wireless communication circuitry that may be used to initiate functionality in other devices. For example, a biometric monitoring device may be equipped with an NFC antenna such that an application automatically launches on a mobile phone when the user brings it into close proximity.

These examples are provided for illustration and are not intended to limit the scope of data that may be transmitted, received or displayed by a device or any intermediate processing that may occur during such transmission and display. Many other examples of data that may be streamed or transmitted via a biometric monitoring device may be envisioned by those skilled in the art in view of this disclosure/application.

Charging and data transmission

Some embodiments of a biometric monitoring device may use a wired connection to charge an internal rechargeable battery and/or transmit data to a host device such as a laptop or mobile phone. In one embodiment similar to those discussed earlier in this disclosure, the biometric monitoring device may use magnets to help the user align the biometric monitoring device to the mount or cable. The magnetic fields of the magnets in the base or cable and the magnets in the device itself can be strategically oriented so as to force the biometric monitoring device to self-align with the base or cable (or more specifically, the connector on the cable), and A force is provided to hold the biometric monitoring device in the mount or to the cable. The magnets can also be used as conductive contacts for charging or data transmission purposes. In another embodiment, permanent magnets may be used only on the base or cable side and not on the biometric monitoring device itself. This may improve the performance of the biometric monitoring device if the biometric monitoring device uses a magnetometer. If magnets are present in a biometric monitoring device, the strong magnetic fields of nearby permanent magnets can make it significantly more difficult for a magnetometer to accurately measure the Earth's magnetic field. In such embodiments, the biometric monitoring device may utilize a ferrous material instead of a magnet, and the magnet on the base or cable side may be attached to the ferrous material.

In another embodiment, a biometric monitoring device may contain one or more electromagnets in the body of the biometric monitoring device. The charger or base for charging and data transmission may also contain electromagnets and/or permanent magnets. The biometric monitoring device can only switch on its electromagnet when it is in proximity to the charger or base. The biometric monitoring device may detect proximity to the base or charger by using a magnetometer to look for the magnetic field signature of a permanent magnet in the charger or base. Alternatively, the biometric monitoring device may be detected by measuring the Received Signal Strength Indication (RSSI) of a wireless signal from the charger or dock or, in some embodiments, by recognizing an NFC or RFID tag associated with the charger or dock The proximity of the charger. When the device does not need to be charged, synced, or has completed syncing or charging, the electromagnet can be reversed, creating a force that repels the device from the charging cable or base. In some embodiments, the charger or base may include an electromagnet and may be configured (eg, a processor in the charger or base may be configured via program instructions) to turn on when a biometric monitoring device is connected for charging. The electromagnet (the electromagnet can generally be kept on so that a biometric monitoring device placed on the charger is attracted against the charger by the electromagnet, or the electromagnet can be kept off until the charger determines that the biometric monitoring device has been placed Placed on the charger, e.g. by completing the charging circuit, recognizing an NFC tag in the biometric monitoring device, etc., and then switching on to attract the biometric monitoring device against the charger. After charging is complete (or data transfer is complete, if charging Once the device is actually a data transfer cradle or a combined charger/data transfer cradle), the electromagnet can be turned off (temporarily or until placement of the biometric monitoring device on the charger is detected again), and the biometric The biometric monitoring device may cease to be attracted against the charger. In such embodiments, it may be desirable to orient the interface between the biometric monitoring device and the charger so that in the absence of the magnetic force generated by the electromagnet, the biometric The monitoring device will be dropped from the charger or otherwise displaced from the charging position to a significantly different position (to visually indicate to the user that charging or data transfer is complete).

Sensor use in data transfer

In some implementations, a biometric monitoring device can include a communication interface that can switch between two or more protocols with different data transmission rates and different power consumption rates. This switching may be driven by data obtained from various sensors of the biometric monitoring device. For example, if Bluetooth is used, the communication interface may use Bluetooth Basic Rate/Enhanced Data Rate (BR/EDR) and Bluetooth Low Energy (BLE) in response to determinations made based on data from sensors of the biometric monitoring device. switch between protocols. For example, a lower power, slower BLE protocol may be used when sensor data from an accelerometer in a biometric monitoring device indicates that the wearer is asleep or otherwise sedentary. In contrast, the higher power, faster BR/EDR protocol may be used when sensor data from the accelerometer in the biometric monitoring device indicates that the wearer is moving around. This adaptive data transmission technique and functionality is further discussed in U.S. Provisional Patent Application No. 61/948,468, filed March 5, 2014, previously published in "Cross-Reference to Related Applications" section is incorporated herein by reference and again hereby is hereby incorporated by reference for what is indicated at Adaptive Data Transfer Rates in Biometric Monitoring Devices.

Such communication interfaces may also act as a form of sensor for the biometric monitoring device. For example, a wireless communication interface may allow a biometric monitoring device to determine the number and type of devices within range of the wireless communication interface. This data can be used to determine whether the biometric monitoring device is in a particular context, eg, indoors, in a car, etc., and to alter its behavior in various ways in response to this determination. For example, as discussed in US Provisional Patent Application No. 61/948,468 (incorporated by reference above), such contexts can be used to drive the selection of a particular wireless communication protocol for wireless communication.

Configurable application functionality

In some embodiments, biometric monitoring devices of the present invention may include a watch-like form factor and/or a bracelet, armband, or anklet form factor and be programmed with "apps" that provide certain functionality and/or display certain information. ". This can be done in a variety of ways including, but not limited to, pressing a button, using a capacitive touch sensor, performing a gesture detected by an accelerometer, moving to a specific location or area detected by a GPS or motion sensor, compressing the body of the biometric monitoring device (thus Generate a pressure signal inside the device that can be detected by an altimeter inside the biometric monitoring device), or place the biometric monitoring device in close proximity to an NFC tag associated with an application or group of applications to activate or deactivate applications. Certain environmental or physiological conditions (including, but not limited to, detection of a high heart rate, detection of water using a humidity sensor (to launch a swimming app, for example), a certain time of day (to launch a sleep tracking app, for example at night), plane changes in the pressure and motion characteristics of departure or landing to activate and deactivate the "airplane" mode application) to automatically trigger the activation or deactivation of the application. It is also possible to activate or deactivate the application by satisfying multiple conditions at the same time. For example, If the accelerometer detects that the user is running and the user presses a button, the biometric monitoring device can launch a pedometer application, an altimeter data collection application, and/or a display. Another application where the accelerometer detects swimming and the user presses the same button In one case, it can start the swim lap counting application.

In some embodiments, the biometric monitoring device may have a swim tracking mode that can be activated by launching the swim application. In this mode, the biometric monitoring device's motion sensor and/or magnetometer can be used to detect strokes, classify stroke types, detect laps, and other related metrics such as stroke efficiency, lap time, speed, distance, and calories combustion. The change in direction indicated by the magnetometer can be used to detect a variety of one-way turn methods. In a preferred embodiment, data from motion sensors and/or pressure sensors may be used to detect swipes.

In another embodiment, the biometric monitoring device can be monitored by moving the biometric monitoring device close to a bicycle located on the bicycle, on the cradle of the bicycle, or in a location associated with the bicycle, including but not limited to a bicycle frame or a bicycle storage facility. NFC or RFID tags to activate the ride-hailing app. (See, eg, Figure 10). The launched application may use a different algorithm than is typically used to determine metrics including, but not limited to, calories burned, distance traveled, and altitude gained. The application may also be launched upon detection of wireless bicycle sensors, including but not limited to wheel sensors, GPS, cadence sensors, or power meters. The biometric monitoring device may then display and/or record data from the wireless bicycle sensor or the bicycle sensor.

Additional applications include, but are not limited to, programmable or customizable watch faces, stopwatches, music player controllers (e.g., mp3 players, remote controls), text messaging and/or email displays or notifiers, navigational compasses, bicycle Computer monitors (when communicating with separate or integrated GPS units, wheel sensors, or power meters), weightlifting trackers, sit-up trackers, pull-up trackers, resistance training modalities/fitness trackers, golf swing analyzers, Tennis (or other racquet sports) swing/serving analyzer, tennis game swing detector, baseball swing analyzer, pitch analyzer (e.g., soccer, baseball), organized sports activity intensity tracker (e.g., football, baseball, basketball, tennis, rugby), disc toss analyzers, food bite detectors, typing analyzers, tilt sensors, sleep quality trackers, alarm clocks, stress gauges, tension/relaxation biofeedback games (e.g., potentially combined Mobile phones that provide auditory and/or visual cues to train the user to breathe during relaxation exercises), tooth brushing trackers, eating rate trackers (e.g., count or track the rate and duration of an implement entering the mouth for food intake), driving Motor vehicle intoxication or suitability indicators (e.g., via heart rate, heart rate variability, galvanic skin response, gait analysis, puzzle solving, etc.), allergy trackers (e.g., using galvanic skin response, heart rate, skin temperature, hay fever (possibly in conjunction with external seasonal allergen tracking from e.g. pollen tracking databases, or local environmental sensors in biometric monitoring devices or by users), fever trackers (e.g., to measure risk, onset or progression of fever, cold or other illnesses, possibly combined with seasonal data, disease databases, user location and/or user-provided feedback regarding the user’s assessment of the spread of a particular disease (e.g., flu), and may in response point out or suggest moderation in work or activity), video games, caffeine impact trackers (e.g., monitoring such as heart rate, heart rate variability, galvanic skin response, skin temperature, blood pressure, tension, sleep, and/or in short- or long-term responses to the intake or abstinence of coffee, tea, energy drinks, and/or other caffeinated beverages activity), drug impact trackers (e.g., similar to the previously mentioned caffeine tracker but with respect to other interventions, whether they are medical or lifestyle drugs such as alcohol, tobacco, etc.), endurance exercise training (e.g., recommending or pointing out Intensity, duration, or profile of running/biking/swimming fitness, or suggested moderation or delay of fitness, based on a user-specified goal, such as a marathon, triathlon, or using data from, for example, historical exercise activity (e.g., distance run, stride length ), heart rate, heart rate variability, health/illness/stress/fever status (customized goals), weight and/or body composition, blood sugar, food intake, or calorie balance tracker (e.g., notifying the user of how many calories they can consume to maintain or Realize a weight), pedometer, and nail biting detector. In some cases, applications may rely solely on the processing power and sensors of the present invention. In other cases, the application may incorporate or only display information from an external device or group of external devices (including but not limited to heart rate straps, GPS distance trackers, body composition scales, blood pressure monitors, blood glucose monitoring) devices, watches, smart watches, mobile communication devices such as smartphones or tablets, or servers).

In one embodiment, the biometric monitoring device can control a music player on the secondary device. Aspects of the music player that may be controlled include, but are not limited to, volume, track and/or playlist selection, skipping forward or backward, fast forward or rewind of tracks, speed of tracks, and music player Equalizer. The music player can be controlled automatically via user input or based on physiological, environmental or contextual data. For example, a user may be able to select and play a track on their smartphone by selecting the track through a user interface on the biometric monitoring device. In another example, the biometric monitoring device may automatically select the appropriate track based on the user's activity level (calculated from the biometric monitoring device sensor data). This can be used to help motivate the user to maintain a certain activity level. For example, if the user has been running and wants to keep their heart rate in a certain range, the biometric monitoring device may play an upbeat or higher tempo track (if the user's heart rate is below their target range).

Automatic features triggered by user activity

Sleep stage triggers functionality

Sleep stages, such as heart rate, heart rate variability, body temperature, body movement, ambient light intensity, ambient noise level, etc., can be monitored via various biometric signals and methods disclosed herein. Such biometrics may be measured using optical sensors, motion sensors (accelerometers, gyroscope sensors, etc.), microphones, and thermometers, among other sensors discussed herein, for example.

The biometric monitoring device may also have a communication module including but not limited to Wi-Fi (802.xx), Bluetooth (Classic, Low Power) or NFC. Once the sleep stage is estimated, the sleep stage can be transmitted to a cloud system, a home server or a master control unit wirelessly connected to a communication-enabled electrical device (via Wi-Fi, Bluetooth or NFC). Alternatively, the biometric monitoring device may communicate directly with the communication-enabled electrical device. Such electrical devices with communication functions may include, for example, kitchen electrical devices, such as microwave ovens, coffee grinders/makers, ovens, and the like.

Once the sleep stage indicates close to the time the user wakes up, the biometric monitoring device may issue a trigger to the electrical device that the user has indicated should operate automatically. For example, a coffee grinder and maker can be caused to start making coffee, and an oven can be caused to start heating bread. It can also cause the microwave to start cooking oatmeal or eggs and the electric kettle to start boiling water. This automatic signal can trigger breakfast cooking as long as the parts are properly prepared.

reminder detection

Reminders, such as low reminders, can be correlated to drowsiness of the person, can also be detected from the biometrics listed above, and can be used to trigger an electrical device such as a coffee maker to start brewing automatically coffee.

hydration

Portable biometric monitoring devices combined with activity level trackers can submit the user's activity level directly to cloud systems, home servers, master control units or electrical equipment. This can trigger actions on electrical devices, especially those related to hydration, such as starting ice making in a refrigerator, or lowering the operating temperature of a water purifier.

power saving

Many electrical devices typically operate at idle at low power levels that consume power. Using the aggregated information of the user's biometric signals, the communication-enabled electrical device can be caused to enter an ultra-low power mode. For example, a water cooler at home could shut itself off to an ultra-low power mode when the user is asleep or out at work, and could start cooling/heating the water as soon as the user's activity in the home is anticipated.

Location-based and event-based restaurant recommendation system

Aggregation of real-time biometric signals and location information can be used to generate educated guesses about one or more users' needs at a given time (eg, ionic drinks). Combining this speculative need with historical user data regarding the user's activity level, activity type, activity time and duration, and user-logged food intake data, an app recommendable on the smartphone and/or smartwatch will satisfy The user's lifestyle and restaurants currently in demand.

For example, a user who has just completed a six-mile circuit run may start the application. The application may know that the person has maintained a high activity level for the past hour, and thus determine that the person may be dehydrated. From historical user data, the app may also know, for example, that the user's meal has too many vegetables and is low in sugar. Through an optimization algorithm that takes into account the user's current location, price range, and other factors mentioned above, the application can recommend, for example, restaurants that serve smoothies.

swim tracking

In some embodiments of the biometric tracking device, the biometric tracking may include a swimming algorithm that may utilize data from one or more motion sensors, altitude sensors (e.g., barometric pressure sensors), orientation sensors (e.g., magnetometers), Data from location services sensors (eg, GPS, wireless triangulation) and/or temperature sensors. The sensors may be embedded mounted into a single device such as a wrist. In other embodiments, additional sensor devices may be attached to the swimmer's forehead, back of the head, goggles, back, hips, shoulders, thighs, legs and/or feet.

The three potential functional components for the swimming exercise analysis are as follows:

• Swipe count detection - Provides stroke counts per pass, where a pass is defined as a one-way pass from one end of the pool to the opposite end.

Stroke Type Classification—describes the user's stroke type (e.g., crawl, breast, back, butterfly, side, kicking without stroke, body streamline, etc.), and can be Any one or combination of:

a. Classification of each swipe taken by the user

b. Classification of major stroke types used per complete trip.

c. Classification of the type of stroke used for each fractional lap (e.g. 1/2 freestyle, 1/2 breaststroke)

• One-way counting—counts the one-way the user passes through. One way to determine one way is by detecting when a user turns around in the pool.

A turn is defined as a 180-degree change in heading. When a turn is detected, the start and end of a one-way can be inferred. Pausing (without movement for some period of time) at a point in the pool (usually at one end or the other) before commencing swimming again is also considered a turn, as long as the subsequent direction of progress is opposite to the direction of progress prior to the pause.

In some embodiments, these functional components can be combined in numerous ways.

Algorithm structure

The three functional components of swim workout analysis may be performed sequentially, in parallel, or in a mixed order (a combination of some sequential blocks and some parallel blocks).

Sequential approach (see Figure 15A)

In one embodiment, the raw and/or pre-processed sensor signals may first be analyzed by a swipe detector algorithm. A swipe detector algorithm may use temporal peaks (local maxima and/or local minima) in motion sensors (eg, accelerometers, gyroscopes) as an indication that a swipe has been taken. Then, one or more heuristic rules may also be applied to remove peaks that do not indicate swipes. For example, the magnitude of a peak, the temporal distance of two adjacent peaks, the peak-to-peak amplitude, and/or the morphological characteristics of the peaks (eg, sharpness) may indicate that certain peaks do not represent strokes. When a sensor provides data in more than one dimension (such as a 3-axis accelerometer, or a 3-axis motion sensor + altimeter (total 4-axis data)), the timing and relative magnitude of the peaks in all axes can be considered to determine the Whether the peak on one or more is caused by swiping.

If a single peak representing a stroke or a cluster of peaks from multiple axes of data representing a stroke is observed, features can be extracted from the data segment obtained between the detection of the previous peak and the detection of the current peak. Features include, but are not limited to, maximum and minimum values, number of ripples in a segment, power measured in various metrics (eg, L1 power and L2 power, standard deviation, mean), etc. The extracted features may then be subjected to a machine learning system, where the system coefficients are calculated offline (supervised learning) or adapted as the user uses the biometric monitoring device (unsupervised learning). The machine learning system can then return a swipe classification for each detected swipe.

The turn detector algorithm may search for sudden changes in motion by computing derivatives, moving averages, and/or using high pass filtering of signals from sensors including but not limited to those listed in this disclosure. Principal component analysis (PCA) may also and/or alternatively be performed on the signal. If one principal component is different from the next, then a turn can be determined to occur. All or part of the coefficients of a transform such as a Fast Fourier Transform (FFT) can also be used as features. Parametric models such as autoregressive (AR) models can also be used. The time-varying model parameters may then be estimated using linear predictive analysis (LPA), least mean square filtering (LMS), recursive least square filtering (RLS), and/or Kalman filtering. The estimated model parameters are then compared to determine if there are sudden changes in their values.

In one embodiment, a swimmer's skill level and/or swimming style (eg, speed) may be inferred from sensor data and then used for turn detection. For example, advanced swimmers typically have more vigorous strokes (ie, large peak accelerometer magnitudes) and take fewer strokes to complete a lap. Therefore, metrics that estimate a swimmer's skill level or characteristics can be used in a turn detection algorithm. These metrics may include, but are not limited to, average or integrated motion signals in the motion signal, in particular, advanced swimmer's arm movement, estimated forward velocity, and detected patterns. The swimmer's skill level or other characteristics may also be determined via user input. For example, a user may input that they are an advanced, intermediate or beginner swimmer.

One or many (combined) features from these analyzes can be used to detect whether a given data sample and/or neighboring data samples have turning properties. To obtain the optimal combination of features and decision boundaries, machine learning techniques such as logistic regression, decision trees, neural networks, etc. can be utilized.

In some embodiments, if a turn is detected, swim data is incremented naturally from a previous turn that can be summarized, such as number of strokes, type of stroke for each stroke and for a single pass, split times, etc. If a turn is not detected, the swipe counter and type may be updated. Unless the user stops swimming, the algorithm may return to stroke count detection.

Parallel approach (see Figure 15B)

In a parallel approach, some or all of the three functional components may be executed in parallel. For example, swipe type detection and turn detection may be performed jointly, while swipe count detection is run independently.

In such embodiments, two functional components may be implemented in a single algorithm that detects both swipe type and turn: swipe type and turn detection. For example, classifiers for stroke type (e.g., detection of movement analysis for freestyle, breaststroke, backstroke, butterfly) and turn type (e.g., tumble turn, flip turn, two-handed touch) can be Returns the detected type of swipe or the detected type of turn. During detection, temporal as well as spectral features can be extracted. Moving windows can first be applied to multiple data axes. Statistics for this window segment can then be calculated, ie maximum and minimum values, number of ripples in the segment, power measured in various metrics (eg L1 and L2 powers, standard deviation, mean). Independent Component Analysis (ICA) and/or Principal Component Analysis (PCA) may also be applied to discover any hidden signals that better characterize turn and swipe types. Temporal features can then be computed from this (potentially improved) signal representation. For temporal features, various non-parametric filtering schemes, low-pass filtering, band-pass filtering, high-pass filtering can be applied to enhance the desired signal characteristics.

Spectral analysis such as FFT, wavelet transform, Hilbert transform, etc. may also be applied to this windowed segment. Whole or partial transform coefficients can be selected as features. Parametric models such as AR, moving average (MA) or ARMA (autoregressive and moving average) models can be used and such models can be discovered via autocorrelation and/or partial autocorrelation or LPA, LMS, RLS or Kalman filters parameters. All or part of the estimated coefficients can be used as features.

Moving average windows of different lengths can be run in parallel and provide the features listed above, and can also use all or some of them as features.

Machine learned coefficients (supervised learning) can then be applied to these extracted features. Can be trained and then used one or more machine learning techniques, ie multiple layers of binomial linear discriminant analysis (eg, logistic regression), multinomial logistic regression, neural nets, decision trees/forests, or support vector machines.

As the window of interest moves, features can be extracted, and these newly extracted features will return the type of swipe or the detected turn via the machine learning system.

The swipe detector algorithm can run in parallel independent of swipe type and turn detection. Temporal peaks of raw or pre-filtered sensor signals can be detected and selected by heuristic rules.

In the generalization phase of the algorithm (in which phase swimming-related metrics can be determined, displayed and/or stored), post-processing can be applied to the sequence stroke type and turn detection. If the turn is confirmed with some confidence, the swim metric data from the previous turn may be summarized along with the detected stroke count. If the turnaround is not confirmed, then the moving average window can continue. Until the user stops swimming, the algorithm may continue to update swimming metrics about the user's workout, including but not limited to total number of turns, total number of laps, total number of strokes, average number of strokes per lap, number of strokes in the last lap , the change of the number of swipes per one-way, etc.

Hybrid approach (see Figures 15C and 15D)

In a hybrid approach, swipe type and swipe count detection may be run in parallel, followed by turn detection.

Swipe type detection can return the swipe type via machine-learned coefficients. The first moving window takes a segment of the sensor signal. Features can then be extracted, either the entirety or a subset of the moving window features enumerated herein. Machine learning coefficients, trained offline, can then be applied to the features to determine which type of swipe produced a given segment of the sensor signal.

Swipe count detection may run concurrently with swipe type detection.

Once the swipe type and count are detected, turn detection can be performed with the entire set of features or a subset of features enumerated.

If a turn is detected, the completion of the lap may be recorded in the user's swim summary metric. A post process can be applied to the detected stroke types to determine the most prominent stroke type for the lap completed. The algorithm can then move to the stroke type and count detection phase unless the user stops swimming. If a turn is not detected, the algorithm may continue to update the stroke type and count for the current lap until a turn is detected.

blood sugar levels and heart rate

Biometric monitoring devices that continuously measure biometric signals can provide meaningful information about the premorbidity, progression, and recovery of a disease. Such biometric monitoring devices may have sensors and run algorithms accordingly to measure and calculate biometric signals such as heart rate, heart rate variability, steps taken, calories burned, distance traveled, weight and body fat, Activity intensity, activity duration and frequency, etc. In addition to the measured biometric signals, food intake records provided by the user may be used.

In one embodiment, the biometric monitoring device may observe heart rate and its changes over time, especially before and after food intake events. Heart rate is known to be affected by blood sugar levels, and high blood sugar levels are known to be a pre-diabetic condition. Thus, a mathematical model describing the relationship between elapsed time (after food intake) and blood glucose levels can be found via statistical regression, where data are collected from normal, prediabetic, and diabetic individuals to provide a corresponding mathematical model . Through the mathematical model, it is possible to predict whether an individual with a particular heart rate pattern is healthy, pre-diabetic or diabetic.

Knowing the many heart failures associated with pre-diabetic or diabetic conditions, it is possible to further inform users of biometric monitoring devices of possible heart failure of such risks, such as coronary heart disease, cerebrovascular disease, and peripheral heart disease, based on the user's biometric data. Vascular disease etc.

Recommended exercise guidelines (e.g., guidelines provided by the American Heart Association (http://www.heart.org/)) can also be used to consider the user's activity intensity, type, duration, and frequency when developing the mathematical model to as an independent variable controlling the "probability" of onset of the disease. Many guidelines on nutrition and weight management are also available in academics and for the general public to prevent cardiovascular disease and diabetes. Such guidelines can be incorporated into the mathematical model along with user data accumulated over time, such as the composition of food consumed by the user and body weight and body obesity trends.

If the user has set his family member as his friend on a social networking site that stores and displays biometric data, then the likelihood of the family member acquiring a disease can also be analyzed and the user informed of the result.

In addition to informing the user of the potential development of disease, a recommended lifestyle that includes an exercise regime and recipes with healthier ingredients and delivery methods can be provided to the user.

Unification of grocery shopping, cooking and food records

Grocery Store Organization and Recipe Recognition System

Receipts from grocery shopping can be rich in information, especially regarding an individual's eating habits. For example, presented here is a novel system that combines information from a grocery store receipt with an individual's biometric data as collected by a biometric monitoring device. The system can collect and analyze data (information) about the individual, and can then recommend options that can change the individual's lifestyle in order to improve their state of health. Implementation of this system may involve cloud computing, hardware platform development for sensing and interfacing, and mobile/website development.

In one embodiment, when a user checks out at a grocery store, a list of grocery stores (as obtained from a receipt or, for example, from an email receipt or invoice) may be automatically transmitted to a remote database (e.g., a cloud server), so The remote database may also store the user's biometric data. When a user arrives home and organizes items in their refrigerator and/or pantry, an app on their smartphone/watch can recommend which items in the pantry or refrigerator to discard based on historical data about the food items (e.g., if Food items are expired or may go bad). Reminders indicating that the food product has expired or should be consumed shortly to avoid spoilage can be automatically sent to the user independent of such interaction. For example, these reminders could be sent to the user whenever a certain threshold has been met (eg, within two days of milk being expired). Reminders may also be sent to the user by means other than by smart phone/watch. For example, via a web interface, via e-mail, via a link on a laptop computer, tablet computer, desktop computer, or any other electronic device that communicates directly or indirectly with a computer that maintains and/or analyzes a food database. The reminder presents the reminder to the user.

Using the updated list of food items and based on the user's historical food consumption data, the application can recommend recipes to the user. In one embodiment, priority may be given to recipes that use items that should be eaten first (eg, before they expire, spoil, or become stale compared to other ingredients). The application may also analyze the user's activity data in order to recommend the best recipes that are nutritionally balanced, properly portioned, and tailored to the user's activity. For example, if the user lifts weights in the morning, a high protein meal may be recommended. In another example, if the user is not very active, the size of the recipe may be reduced to reduce the number of calories the final meal contains.

It should be noted that these policies can be applied to multiple users sharing the same food and/or meal. For example, a combined food database may be created for a family such that if one member of the family gets eggs from the grocery store and another member of the family gets milk, both eggs and milk will be represented in the food database. Similarly, nutritional preferences (eg, vegetarian, allergies to certain foods, etc.), activity, basal metabolic rate, and total calorie burn can be used to form recommendations on what foods/recipes to prepare and/or buy.

Biometric signals including, but not limited to, heart rate and heart rate variability can provide indications of preconditions for disease. This information can be used to recommend that the user purchase, consume and/or prepare specific foods in order to reduce their risk of developing a disease with which they have a pre-existing condition. For example, if a user has a pre-existing condition of heart problems, it may recommend that they buy more vegetables, consume less fatty foods, and prepare food in a way that requires less oil (e.g., not deep frying) .

Controlling "Smart Appliances"

In another embodiment, various home appliances may all have Wi-Fi functions, and may communicate with the server. Since the application (which may be connected to the appliance via, for example, the cloud or the Internet) may know which food items the refrigerator contains, the application may communicate with the refrigerator to lower or raise the refrigerator's temperature depending on the food items. For example, if many food items are more sensitive to cold, such as vegetables, then the refrigerator may be instructed to increase the temperature. The application may also communicate directly with the refrigerator via Bluetooth, BTLE or NFC.

food records

The application may also provide items recorded as food for the user based on a grocery shopping list (which may, for example, be a list maintained within the application) and food recipes recommended by the application. In the case of pre-cooked meals (e.g., frozen meals) or produce that does not require any further processing before eating, the user can simply enter the size of their meal (or in the case of the user eating the entire meal, the user may not Meal size needs to be entered) and the food record will then be completed. Since grocery store listings or recipes provide the exact brands and labels of certain foods, more accurate nutritional information can be logged into the user's account.

When a user logs a food item that is being cooked by following a recipe suggested by the application, the application can calculate nutritional information from the ingredients and cooking procedure. This can provide a more accurate estimate of calorie intake than simple organization of the end product/meal, as many recipes exist to prepare specific types of food, e.g. pasta meatballs can be made from beer, turkey, pork, etc. , and the meatballs may contain varying degrees of carbohydrates.

Motion metering using sensor devices

In some embodiments, sensors may be mounted on a racket, such as a tennis racket, to help measure the different strokes of a player. This applies to most, if not all, racquet sports, including (but not limited to) tennis, racquetball, squash, table tennis, badminton, lacrosse, etc., and balls such as baseball, softball, cricket, etc. stick play sport. Different aspects of golf can also be measured using similar techniques. Such devices may be mounted on the bottom of the racquet, on the handle, or on a shock absorber, usually on the string. This device may have various sensors such as accelerometers, gyroscopes, magnetometers, strain sensors and/or microphones. Data from these sensors can be stored locally or transmitted wirelessly to a host system on a smartphone or other wireless receiver.

In some embodiments of the biometric monitoring device, a wrist-mounted biometric monitoring device including accelerometers, gyroscopes, magnetometers, microphones, etc. may perform similar analysis of the user's game play or movement. This biometric monitoring device may take the form of a watch or other strap worn on the user's wrist. A racket or bat mounted sensor that measures or detects the moment of impact between the bat or racket and the ball and wirelessly transmits this data to a wrist mounted biometric monitoring device can be used to accurately measure the time of impact with the ball to improve the accuracy of these algorithms.

Wrist and racquet/bat mounted devices can help measure different aspects of a user's game, including (but not limited to) stroke type (forehand, counter, serve, oblique), number of forehands, number of counterattacks , ball rotation direction, top spin, serve percentage, angular velocity of racket head, recoil, hitting energy, hitting consistency, etc. A microphone or strain sensor can be used to supplement the accelerometer to identify the moment the ball hits the racket/bat. In cricket and baseball, such devices can measure recoil, the angular velocity of the bat at impact, the number of hits on the offside against the leg side (baseball). Also measures the number of swings and fumbles and the number of defensive versus offensive swipes. Such devices may also have wireless transmitters to transmit this statistics in real time to the scoreboard or to individual devices held by the spectators.

A wrist or racket mounted device may have a small number of buttons (eg, two) that a player may use to indicate when a tennis ball is won or when an unforced error has occurred. This will allow the algorithm to calculate the score for the winner and for an unforced error as a forehand versus a counterattack. The algorithm can also track the number of directly scored serves versus double faults in tennis. If two players use such a system, the system can also automatically track the score.

ECG based on bicycle handle bar

In some embodiments of the biometric monitoring device, the user's heart rate may be monitored using electrodes in contact with the left hand as well as electrodes in contact with the right hand (eg, ECG heart rate measurement). Because bicycling requires the user to touch either side of the handle bars with their hands, this particular activity lends itself well to using ECG technology to track the user's heart rate. By embedding electrodes in the handlebar or the handlebar grip or the cord, the user's heart rate is measured whenever the user holds the handlebar. For bicycles with grips (as opposed to using handlebar straps), the electrodes can be incorporated into special grips that can be used to replace existing grips (eg, factory installed grips, which are typically non-conductive). The left and right grips can be electrically connected to electronics that measure ECG signals, for example, using wires. Where the handle bar itself is conductive, the handle bar may be used to electrically connect one of the grips to the electronics that measure the ECG signal. Electronics to measure the ECG signal may be incorporated into one or both of the grips. Alternatively, the electronics to measure the ECG signal may be located in a separate housing. In one embodiment, this separate housing can be mounted on a bicycle handle bar or stem. It may have the functions and sensors that a typical cycle computer has (eg speed sensor, cadence sensor, GPS sensor). It may also have atypical sensors such as wind speed sensors, GSR sensors and accelerometer sensors (potentially also incorporated into the handle bar). This embodiment can use the techniques described in this disclosure to calculate activity metrics, including but not limited to, calories burned, and transmit these metrics to secondary and tertiary devices (eg, smartphones and servers).

Electrodes for the ECG may be incorporated into parts or accessories of the bicycle other than the handlebar straps and handlebar grips, such as gloves, brake caps, brake levers, or the handlebar itself. These electrodes or additional electrodes can be used to measure GSR, body fat and hydration in addition to or instead of heart rate. In one example, the user's heart rate may be measured using conductive filaments (used as ECG electrodes) sewn into a grip strap mounted on the handle bar. The grip cord strap electrodes can be connected to a central cycle computer unit that contains electronics to measure GSR, hydration, and/or heart rate. The biometric monitoring device can display this information on a display. If the user's hydration or heart rate exceeds a certain threshold, the user may be reminded to drink more, drink less, increase intensity, or decrease intensity. In cases where the cycling computer measures only one or both of GSR, hydration, or heart rate, algorithms can be used to estimate metrics that cannot be measured directly. For example, if the biometric monitoring device could only measure heart rate and exercise duration, a combination of heart rate and exercise duration could be used to estimate hydration and alert the user when they should drink. Similarly, heart rate and exercise duration can be used to remind the user when they should eat or drink something other than water (eg, a sports drink).

Indirect measure estimation

Cycling computers typically measure a variety of metrics including, but not limited to, speed, pace, power, and wind speed. Where the portable monitoring device does not measure these metrics or communicate with a device that may be able to supply these metrics, these and other metrics may be inferred using sensors that the portable biometric monitoring device has. In one embodiment, the portable biometric monitoring device measures heart rate. It can use this measurement to infer/estimate the amount of power the user is putting out. Other metrics such as the user's age, height, and weight may help inform motivation measurements. Additional sensor data such as GPS-measured speed, altitude gain/decrease, bike posture (to measure the incline or slope of a slope), and accelerometer signals can be used to further inform power estimates. In one embodiment, the user's power output may be calculated using an approximately linear relationship between heart rate and power output.

In one embodiment, the calibration phase may occur where the user takes data from a portable biometric monitoring device as well as a secondary device that may be used as a baseline during calibration but not used at a later time (eg, a power meter). This may allow a relationship between sensor data measured by the portable monitoring device and sensor data measured by the secondary device to be determined. This relationship may then be used when there is no secondary device to calculate estimates for data provided explicitly by the secondary device but not by the biometric monitoring device.

Activity-Based Automatic Scheduling

In one embodiment, daily travel requirements (to work, from get off work, between meetings) may be scheduled for the user based on information in the user's calendar (or email or text message), with the goal of meeting daily activity goals or long-term activities Target. The user's historical data can be used to help plan both meeting goals and also required transit times. This feature can be combined with friends or colleagues. The scheduling can be done so that the user can meet a friend on his walk to work, or a colleague on that road for a meeting (but the user may need to set a meeting point). If there is real-time communication between the user's biometric monitoring device and the user's friend, the user may be directed to walk a longer route if data from the friend's biometric monitoring device indicates that their friend is running late.

In another embodiment, walking/running/fitness routes may be suggested to the user based (in whole or in part) on the user's proximity to the user. Data for such recommendations may also or additionally be based on GPS information from other users. If there is real-time communication, the user may be directed to a preferred busy or quiet route. Knowing heart rates and basic fitness information about other users may allow the system to suggest a route to match the user's fitness level and desired level of exercise/effort. This information can again be used to plan/direct longer term activity/health goals to the user.

Location/Context Sensing and Applications

Embodiments of the biometric monitoring device disclosed herein have sensors that can determine or estimate the location or context of the biometric monitoring device (eg, in a bus, at home, in a car) by one or more methods. Dedicated position sensors may be used, such as GPS, GLONASS or other GNSS (Global Navigation Satellite System) sensors. Alternatively, less accurate sensors may be used to extrapolate, estimate, or guess the location. In some embodiments where knowledge of the user's location is difficult, user input may assist in determining the user's location and/or context. For example, if the sensor data makes it difficult to determine whether the user is in a car or a bus, the biometric monitoring device or a portable electronic device in communication with the biometric monitoring device or a cloud server in communication with the biometric monitoring device can present the user with Ask, which asks the user if they took the bus or car today. Similar queries can be made for locations other than the context of the vehicle. For example, if sensor data indicates that the user has done vigorous exercise, but there is no location data indicating that the user went to the gym, the user may be asked if they went to the gym today.

Vehicle transport. detection

In some embodiments, a sensor of a biometric monitoring device and/or a portable electronic device in communication with the biometric monitoring device and/or a cloud server in communication with the biometric monitoring device may be used to determine what type of vehicle the user is or has been in (if any). It should be noted that in the following embodiments, sensors in one or more biometric monitoring device communications and/or portable electronic devices may be used to sense coherent signals. It should also be noted that while certain network protocols such as WiFi or Bluetooth may be used in the following description, one or more alternative protocols such as RFID, NFC or cellular telephony may also be used.

In one embodiment, the detection of a Bluetooth device associated with the vehicle may be used to infer that the user is in the vehicle. For example, a user may have a car with a Bluetooth multimedia system. When the user is close enough to their car for a long enough period of time, the sensor device can recognize the multimedia system's Bluetooth identification and assume the user is in the car. Data from other sensors may be used to corroborate the user's hypothesis of being in the vehicle. Examples where data or signals from other sensors may be used to confirm that the user is in the car include GPS speed measurements above 30 mph and accelerometer signals which are characteristic of being in the car. Information inherent in the Bluetooth ID can be used to determine whether it is the vehicle's Wi-Fi router or the type of vehicle. For example, the Bluetooth ID of a router in a car may be "Audi In-Car Multimedia". The keywords "audi" or "car" can be used to guess that the router is associated with the vehicle type "car". Alternatively, a database of Bluetooth IDs and their associated vehicles may be used.

In one embodiment, a database of Bluetooth IDs and their associated vehicles may be created or updated by the user of the biometric monitoring device or by portable communication device data. This can be done with and/or without the aid of user input. In one embodiment, if the biometric monitoring device can determine whether it is in a vehicle, the type of vehicle, or a specific vehicle without using a Bluetooth ID and it encounters a Bluetooth ID that is moving with the vehicle, then it can compare the Bluetooth ID and The information is sent to the central database to be cataloged as the Bluetooth ID corresponding to the vehicle. Alternatively, if the user entered information about a vehicle they were or were in at a previous point in time and there was a Bluetooth ID encountered during or near the time the user indicated they had been in that vehicle, then the Bluetooth ID and vehicle information are sent to a central database and associated with each other.

In another embodiment, the detection of Wi-Fi devices associated with a vehicle may be used to infer which vehicle or type of vehicle the user is in. Some trains, buses, planes, cars, and other vehicles have Wi-Fi routers in them. The router's SSID can be detected and used to infer or assist in inferring that the user is in a particular vehicle or type of vehicle.

In one embodiment, a database of SSIDs and their associated vehicles may be created or updated with the user of the biometric monitoring device or through portable communication device data. This can be done with and/or without the aid of user input. In one embodiment, if the biometric monitoring device can determine whether it is in a vehicle, the type of vehicle, or a specific vehicle without using an SSID and it encounters an SSID that is moving with the vehicle, the biometric monitoring device can compare the SSID and The information is sent to the central database to be cataloged into the SSID corresponding to the vehicle. Alternatively, if the user entered information about a vehicle they were or were in at a previous point in time and there was an SSID encountered during or near the time the user indicated they had been in the vehicle, then the SSID and vehicle information are sent to a central database and associated with each other.

In another embodiment of the biometric monitoring device, location sensors may be used to determine the user's trajectory. This trajectory can then be compared to a database of routes for different modes of travel. Modes of transportation may include, but are not limited to, walking, running, biking, driving, bus, train, streetcar, subway, and/or motorcycle. If the user's trajectory corresponds well to a route for a particular mode of travel, it may be assumed that the user used that mode of travel during the period of time it took to traverse that route. It should be noted that the speed at which a route or section of a route is completed may improve guesswork on traffic patterns. For example, both a bus and a car may be able to take the same route, but an additional stop by the bus at a bus stop may allow the device to determine that the user took the bus instead of the car. Similarly, the distinction between biking and driving a route may be aided by the typical difference in speed between the two. This speed difference can also depend on the time of day. For example, some routes may be slower due to cars during rush hour.

In another embodiment, the biometric monitoring device may be capable of detecting that the user is in or near the vehicle based on measurements of the vehicle's magnetic field. In some embodiments, magnetic field signatures of locations commonly associated with vehicles (eg, train stations, subway stations, bus stops, garages) can also be used to infer that the user is currently, will be, or has been in the vehicle. The magnetic field signature can be time-invariant or time-varying.

If it is determined that the user was actually in the vehicle for a period of time, other metrics about the user may be modified to reflect the status. Where the biometric monitoring device and/or portable electronic device may measure activity metrics such as steps taken, distance walked or run, altitude climbed, and/or calories burned, it may be based on information about the vehicle Progressive information to modify these metrics. If any steps taken or altitude climbed were incorrectly recorded while the user was in the vehicle, they may be removed from the record about the user's measurements. Metrics derived from incorrectly recorded steps taken or altitude climbed, such as distance traveled and calories burned, may also be removed from the record of metrics about the user. Where it can be determined in real-time or near real-time whether the user is in the vehicle, sensors that detect metrics measured when they should not be in the vehicle (such as steps taken or stairs climbed) can be turned off, or can be turned off Algorithms for measuring these metrics, thus preventing incorrectly recorded metrics (and saving power). It should be noted that metrics about vehicle usage (e.g., type of vehicle taken, time taken, which route was taken, and how long the journey took) can be recorded and later used to present this data to the user and/or Other activity and physiological measures about the user are corrected.

Location Sensing Using Bluetooth

The biometric monitoring device may also use methods similar to those described above to determine when a user is approaching a static location. In one embodiment, a Bluetooth ID from a computer (eg, a tablet) at a restaurant or store can be used to determine the user's location. In another embodiment, a semi-permanent Bluetooth ID from a portable communication device (eg, a smartphone) may be used to determine the user's location. In the case of a semi-stationary Bluetooth ID source, multiple Bluetooth IDs may be required to achieve an acceptable level of confidence in the user's location. For example, a database of Bluetooth IDs of a user's co-workers may be created. If the user is within range of several of these Bluetooth IDs during typical business hours, the user may be assumed to be at work. Detection of other Bluetooth IDs can also be used to record when two users run into each other. For example, it may be determined that a user went out for a run with another user by analyzing pedometer data and Bluetooth ID. Similar such concepts are discussed in further detail in US Provisional Patent Application No. 61/948,468, filed March 5, 2014, and previously incorporated by reference with respect to such concepts.

Uncertainty Measures for Position-Based GPS

When fusing sensor signals with GPS signals to estimate informative biometrics (eg, steps, life steps, speed, or trajectory of a journey), the quality of the GPS signals is often very informative. However, GPS signal quality is known to be time-varying, and one of the factors affecting signal quality is the surrounding environment.

The location information can be used to estimate GPS signal quality. The server may store a map of area types, where the area type is predetermined by the number and type of objects degrading the GPS signal. The types may be, for example: large built-up areas, small built-up areas, open areas, waterside areas and forested areas. These area types can be queried for their first few position estimates (which are expected to be coarse and incorrect) when the GPS sensor is on. From a rough GPS estimate of position, possible area types can be returned, and these area types can then be taken into account when calculating GPS signal quality and reliability.

For example, if the user is in or near an urban canyon (area surrounded by tall buildings) (eg, downtown San Francisco), low certainty may be associated with any GNSS position measurements. This certainty can later be used by algorithms that attempt to determine the user's trajectory, speed, and/or elevation based at least in part on GPS data.

In one embodiment, a database of locations and GPS signal quality may be automatically created using data from one or more GNSS sensors. This comparison is performed automatically by comparing the GNSS trajectory to the street map and seeing when the GNSS sensor exhibits a characteristic that the user is traveling down the street (eg, with a speed of 10 mph or more) but his trajectory is not on the road. A database of GPS certainty based on approximate locations can also be inferred from maps showing the presence of tall buildings, canyons, or dense forests.

Position sensing using vehicle GNSS and/or dead reckoning

Many vehicles have integrated GNSS navigation systems. Users of vehicles that do not have integrated GNSS navigation systems often purchase GNSS navigation systems for their cars, which are typically not permanently installed within the driver's field of view. In one embodiment, the portable biometric monitoring device may be capable of communicating with the vehicle's GNSS system. In cases where the portable biometric monitoring device is also used to track location, it may receive location information from the vehicle GNSS. It may enable a biometric monitoring device to switch off its own GNSS sensor (if it has one), thus reducing its power consumption.

In addition to GNSS position detection, a vehicle may be able to transmit data about its steering wheel orientation and/or its orientation relative to the Earth's magnetic field, in addition to its speed as measured using tire size and tire rotational speed. This information can be used to perform dead reckoning to determine trajectory and/or position in situations where the vehicle does not have a GNSS system or the vehicle's GNSS system is unable to obtain reliable position measurements. Dead reckoning position information can complement GNSS sensor data from biometric monitoring devices. For example, a biometric monitoring device may reduce the frequency at which it samples GNSS data and fill in the gaps between GNSS position data with positions determined by dead reckoning.

Fusion with step counter data for satellite-based position determination

In some embodiments of a biometric monitoring device, data from various sensors can be fused together to provide new insights into the activities of the wearer of the biometric monitoring device. For example, data from an altimeter in a biometric monitoring device may be combined with step count data obtained by performing peak detection analysis on accelerometer data from the biometric monitoring device's accelerometer to determine the When the wearer (for example) climbs stairs or walks uphill (as opposed to riding a lift or escalator or walking across flat ground)

In another example of sensor data fusion, data from step counters such as those discussed above can be combined with distance measurements derived from GPS data to provide a refined estimate of the total distance traveled within a given window. For example, a Kalman filter can be used to combine GPS-based distance or velocity data with step counter-based distance or velocity (using the number of steps taken multiplied by the span) in order to obtain a fine-grained distance estimate, which The distance estimate may be more accurate than GPS-based distance or speed measurements alone or step counter-based distance or speed measurements. In another implementation, the GPS-based distance measurements may be filtered using a smoothing constant as a function of step rate as measured by, for example, an accelerometer. Such embodiments are further discussed in U.S. Provisional Patent Application No. 61/973,614, filed April 1, 2014, which was previously incorporated herein by reference in the "Cross-Reference to Related Applications" section, and said application is hereby incorporated by reference again with regard to aligning the refinement of distance or speed estimates using data from satellite-based positioning systems and step counting sensors.

Biometrics and Environment/Exercise Performance Correlation

Some embodiments of the portable monitoring devices described herein can detect a variety of data, including biometric data, environmental data, and activity data. All of this data can be analyzed or presented to a user to facilitate analysis of correlations between two or more types of data. In one embodiment, the user's heart rate may be correlated to car speed, biking speed, running speed, swimming speed, or walking speed. For example, the user may be presented with a graph plotting cycling speed on the X-axis and heart rate on the Y-axis. In another example, the user's heart rate may be correlated to the music the user is listening to. The biometric monitoring device may receive data about what music the user has listened to through a wireless connection (eg, Bluetooth) to the car radio. In another embodiment, the biometric monitoring device can also act as a music player itself, and thus can record when which song is played.

Weightlifting Assist

It can be difficult to properly complete a weightlifting routine without the assistance of a personal trainer or collaborator. The portable biometric monitoring device can communicate to the user how long they should lift each weight, how quickly they should lift the weight, how quickly they should lower the weight, and the number of repetitions to perform for each lift. Assists the user in completing a weightlifting routine. The biometric monitoring device may use one or more EMG sensors or strain sensors to measure the user's muscle contractions. It can also be measured by measuring vibration of one or more body parts (for example, using an accelerometer), sweat of one or more body parts (for example, using a GSR sensor), rotation (for example, using a gyroscope), and/or one or more Temperature sensors on multiple body parts to infer the user's muscle contractions. Alternatively, sensors can be placed on the weightlifting equipment itself to determine when the user is lifting, as well as how fast they are lifting or lowering, how long they are lifting, and the number of repetitions they have performed.

In one embodiment, if the biometric monitoring device or the weightlifting device detects that the user is approaching their failure limit (when the user can no longer support the weight), the weightlifting device can automatically lift the weight or prevent the weight from lowering . In another embodiment, a robotic arm in communication with a biometric monitoring device or weight lifting equipment may automatically lift a weight or prevent a weight from lowering. This may allow the user to push themselves to their limit without the need for a partner/witness (to lift the weight in case of failure) and without the risk of injury from dropping the weight.

Blood Glucose Level Monitoring Aid

In some embodiments, a portable biometric monitoring device may be configured to assist users (eg, diabetics) who need to monitor their blood glucose levels. In one embodiment, the portable biometric monitoring device may indirectly infer the user's blood glucose level or a metric related to the user's blood glucose level. Sensors other than those typically used to monitor blood glucose monitoring (using continuous or discrete finger prick type sensors) may be used in addition to, instead of, or as an adjunct to typical blood glucose monitoring methods. For example, a biometric monitoring device may alert a user that they should check their blood sugar levels based on data measured from sensors on the biometric monitoring device. If a user has performed a certain type of activity for a certain amount of time, their blood sugar levels may have decreased, and therefore, the biometric monitoring device may display an alert, generate an audible alert, or vibrate to alert the user that their blood sugar may be low And they should use a typical blood glucose measuring device (eg, a finger prick type blood glucose monitor) to check blood glucose. A biometric monitoring device may allow a user to input blood glucose levels measured from a blood glucose meter. Alternatively, the blood glucose measurements may be automatically transmitted to the biometric monitoring device and/or a third device (eg, a smartphone or server) in direct or indirect communication with the biometric monitoring device. This blood glucose measurement can be used to inform an algorithm used by the biometric monitoring device to determine when the next blood glucose level reminder should be delivered to the user. A user may also be able to input what food they have eaten, are eating, or plan to eat into the biometric monitoring device or a device in direct or indirect communication with the biometric monitoring device. This information can also be used to determine when users should be reminded to check their blood sugar levels. Other metrics and sensor data described herein (eg, heart rate data) may also be used alone or in combination to determine when the user should be reminded to check their blood sugar.

In addition to alerting when blood sugar levels should be checked, the biometric monitoring device can also display an estimate of the current blood sugar level. In another embodiment, data from a biometric monitoring device may be used by a secondary device (e.g., a smartphone or server) to estimate the user's blood glucose level and/or present this data to the user (e.g., via , displaying said data on a web page, and/or by transmitting said data via radio).

Biometric monitoring devices can also be used to correlate exercise, diet, and other factors with blood glucose levels. This may assist users in understanding the positive or negative effects of these factors on their blood sugar levels. Can be measured by the user using a different device (e.g., a finger prick monitor or a continuous glucose monitor), by the biometric monitoring device itself, and/or by inferring blood glucose levels or using other sensors' metrics related to blood glucose levels Activity-related blood sugar levels. In some embodiments of the biometric monitoring device, the user may wear the continuous glucose monitoring device and the biometric monitoring device. The two devices can automatically upload data about activity and blood glucose levels to a third computing device (eg, a server). The server can then analyze the data and/or present the data to the user, making the user more aware of the relationship between their activity and blood glucose levels. The server may also receive input regarding the user's diet (eg, the user may input what foods they eat) and correlate the diet with blood sugar levels. Biometric monitoring devices can assist users with diabetes by helping users understand how diet, exercise, and other factors (eg, stress) affect their blood sugar levels.

UV Exposure Detection

In one embodiment, the biometric monitoring device may be capable of monitoring an individual's exposure to UV radiation. UVA and UVB can be measured by one or more sensors. For example, a photodiode with a bandpass filter that passes only UVA can detect UVA exposure, and a photodiode with a bandpass filter that passes only UVB can detect UVB exposure. A camera or a reflectometer (light emitter and light detector that determine how efficiently light is reflected off the skin) may also be used to measure the user's skin pigmentation. Using the UVA, UVB and skin pigmentation data, the biometric monitoring device can provide the user with information about the amount of UV exposure he has been subjected to. Biometric monitoring devices may also provide estimates or alerts regarding overexposure to UV, likelihood of sunburn, and increased likelihood of skin cancer risk.

Screen power saving using user presence sensor

A portable biometric monitoring device may have one or more displays to present information to a user. In one embodiment, a sensor on the biometric monitoring device may determine that the user is using the biometric monitoring device and/or wearing the biometric monitoring device to determine the status of the display. For example, a biometric monitoring device with a PPG sensor may use the PPG sensor as a proximity sensor to determine when a user is wearing the biometric monitoring device. If the user is wearing a biometric monitoring device, the state of the screen (eg, a color LCD screen) can change from its typical state of being off to "on" or "standby".

Power savings relative to satellite-based position determination systems

In some implementations, certain systems included in a biometric monitoring device may consume relatively large amounts of power compared to other systems in the biometric monitoring device. Due to the small space constraints of many biometric monitoring devices, this can severely impact the overall battery charge life of the biometric monitoring device. For example, in some biometric monitoring devices, a satellite-based position determination system may be included. Whenever a satellite-based position determination system is used to obtain a position fix using data from the GPS satellite constellation, it uses power drawn from the battery of the biometric monitoring device. The biometric monitoring device may be configured to alter the frequency with which the satellite-based position determination system obtains a fix based on data from one or more sensors of the biometric monitoring device. This adaptive positioning frequency functionality can help save power while still allowing satellite based position determination systems to provide positioning at useful intervals (when appropriate).

For example, if the biometric monitoring device has an ambient light sensor, data from the ambient light sensor may be used to determine whether lighting conditions indicate that the biometric monitoring device may be indoors rather than outdoors. If indoors, the biometric monitoring device may cause the location frequency to be set to a level lower than that which may be used when lighting conditions appear to indicate that the biometric monitoring device is outdoors. This has the effect of reducing the number of fixes attempted by the biometric monitoring device when it is indoors, and therefore less likely to obtain a good fix using satellite-based position determination systems.

In another example, the positioning frequency of the satellite-based position determination system is determined if the motion sensor of the biometric monitoring device indicates that the wearer of the biometric monitoring device is substantially immobile, such as sleeping or generally immobile greater than a few feet per minute. It may be set to a lower level than if the motion sensor indicates that the wearer of the biometric monitoring device is in motion (eg, walking or running from one location to another, eg, moving more than a few feet).

In yet another example, the biometric monitoring device may be configured to determine whether the biometric monitoring device is actually being worn by a person, and if not, the biometric monitoring device may set the location frequency to a higher frequency than the biometric monitoring device is actually being worn. The situation is low level. The biometric monitoring device may be stationary, for example, when motion data collected from a motion sensor of the biometric monitoring device indicates that the biometric monitoring device is substantially stationary (e.g., is not stationary even when the biometric monitoring device experiences small movements indicating that the wearer is sleeping or sedentary). Such determinations as to whether the biometric monitoring device is being worn are made while the biometric monitoring device is being worn or when, for example, data from a heart rate sensor indicates that no heart rate is detected. For an optical heart rate sensor, if there is little change in the amount of light detected by the light detection sensor when the light source is turned on and off, this may indicate the fact that the heart rate sensor is not pressing against the person's skin, and inferring the biometric monitoring device Not being worn. This adaptive satellite-based position determination system positioning frequency concept is discussed in more detail in U.S. Provisional Patent Application Serial No. 61/955,045, filed March 18, 2014, which was previously published in "Reference to Related Application Incorporated herein by reference in the "Cross-References" section of the Proposal and again hereby is hereby incorporated by reference for what was noted at Power Conservation in the Context of a Satellite-Based Position Determination System.

It should be appreciated that, in addition to including the features discussed in more detail below, a biometric monitoring device may also include one or more of the features or functions discussed above or in the various applications incorporated by reference into the discussion above. sex. Such implementations are understood to be within the scope of the present invention.

While the above discussion has focused on a variety of different systems and functionality that may be included in a biometric monitoring device, the discussion that follows below focuses in more detail on some specific embodiments (some of which may also have been discussed above).

Altimeter use in biometric monitoring devices

A number of different techniques or systems in which altimeters or altimeter measurement devices may be used are discussed below. Such implementations may be practiced in the context of biometric monitoring devices, although some implementations may be practiced in other devices.

In some implementations, a barometric altimeter in a biometric monitoring device (or other electronic device) may be recalibrated based on data obtained from another sensor (eg, from a GPS or other position determination system or from, eg, a camera). In other or additional implementations, an altimeter in a biometric monitoring device may be used in conjunction with other sensors of the biometric monitoring device to aid in identifying particular gestures or movements that may indicate particular activity. In yet other or additional implementations, an altimeter in a biometric monitoring device (or other device) may be used in conjunction with an "airplane mode" in which the biometric monitoring device may be placed in order to save power and/or provide reduced EM emissions. These embodiments are discussed in more detail below.

Automatic altimeter recalibration

In some implementations, the biometric monitoring device may include a barometric altimeter as well as other biometric sensors (eg, accelerometers, heart rate sensors, etc.) and/or environmental sensors (eg, ambient light sensors, ambient air quality sensors, etc.). A barometric altimeter is actually a pressure sensor that measures ambient atmospheric pressure. As the altitude of the pressure sensor changes, the pressure detected by the pressure sensor will increase as the altitude of the pressure sensor decreases and will decrease as the altitude of the pressure sensor increases. Pressure sensors provided by some manufacturers are designed so that they output a signal indicative of pressure, and the device using this sensor must then convert the signal into an altitude quantity (the relationship between pressure and altitude is not linear, so a pure some form of processing other than linear scaling). Some other pressure sensors are designed with a pre-output circuit or processor that converts the pressure sensor signal into a signal indicative of altitude.

17 depicts a simplified block diagram of an example biometric monitoring device with an altimeter and position determination system. As can be seen, the biometric monitoring device of FIG. 17 includes one or more processors 1704 and memory 1706 communicatively connected to each other to form a controller 1702 . Controller 1702 is communicatively coupled with altimeter or pressure sensor 1708 and with position determination system 1710 . Other arrangements of such components may also be used, for example, as discussed further below, the location determination system 1710 may be located in another device external to the biometric monitoring device, and its functionality may be stored by the biometric monitoring device using, for example, a wireless communication interface. Pick.

Altimeters based on pressure sensors can be quite sensitive, which can be used to detect small magnitude changes in altitude, such as about +/- 20cm. However, pressure sensors can also be highly susceptible to changes in pressure not caused by changes in altitude. For example, if the low pressure wavefront moves into the area, the ambient pressure can decrease without any increase in altitude on the pressure sensor side. Other sources of pressure variation also exist, including wind. Pressure sensors can also be susceptible to drift over time, which can affect their accuracy.

Some biometric monitoring devices may include a pressure sensor that acts as an altimeter, such as the Fiibii Ultra, Fitbit One, and Fitbit Force, all of which include a barometric altimeter/pressure sensor. Barometric altimeters may also be used in other devices, for example some GPS devices may also incorporate altimeters, since altimeters generally provide more accurate altitude measurements than those available with satellite-based position determination systems.

In some implementations, a device (eg, a biometric monitoring device or other device) can be equipped with a pressure sensor that acts as an altimeter and a position determination system. Such a device may use a position determination system to periodically measure an XY position, and then use the XY position data in conjunction with a historical terrain dataset to determine a terrain altitude based on data in the historical terrain dataset. If the altitude measured by the pressure sensor at a given location is substantially different from the terrain altitude corresponding to that location, the device may recalibrate the pressure sensor (or recalibrate the way it processes data from the pressure sensor).

This scenario is discussed in more detail with reference to FIG. 18 . 18 depicts a flow diagram of a technique for recalibrating pressure sensors based on terrain data.

In FIG. 18, technique 1800 may begin in block 1802, where a device (eg, a biometric tracking device) may receive location data from some form of location determination system. As used herein with respect to altimeter recalibration, position determination systems may include satellite-based position determination systems (e.g., GPS or GLONASS); cell tower-based triangulation systems, WiFi-based position lookups, radio frequency identification (RFID) tag lookup systems , Near Field Communication (NFC) tag lookup system, and any other system that can be used to provide XY position. In general, position determination systems typically rely on data received from devices remote from the position determination system (eg, satellites, RFID tags, servers, WiFi hotspots, etc.). Such remote devices are typically fixed in place (eg, cell towers and WiFi hotspots) or capable of providing very accurate information about their location (eg, GPS satellites). This data can then be used by the position determination system to determine where the position determination system is relative to these known positions. However, in some implementations, the position determination system may utilize a camera to obtain images (either automatically or culled from images taken by the user of the device hosting the position determination system) that may be analyzed to determine position. For example, such a system can analyze the image and determine that the horizon includes a particular silhouette corresponding to the image data in the associated database. The image data may be associated with a particular location in the database, and the system may determine that the image was taken at the particular location (and thus may use the particular location as location data) based on the similarity between the image horizon and the image data. Such image-based techniques are not limited to horizon silhouette recognition, guide signs (e.g., road signs, business signs, landmark signs, etc.), buildings, landmarks, etc. can all serve as potentially searchable image-based features that can correspond to specific locations, And can be used by camera-based position determination systems to obtain position data.

The location data may be provided by a location determination system located in the same device or in a different device. For example, a smart watch-type biometric monitoring device may include both a pressure sensor and a GPS receiver, and thus may obtain location data from the GPS receiver. However, in some other implementations, the biometric monitoring device may not include an on-board position determination system, but may instead include communication functionality, eg, for paired smartphone communication. In such implementations, the smartphone may have a GPS receiver, and the biometric monitoring device may obtain location data by requesting such data from the smartphone; the smartphone may thus act as a location-determining device with respect to the biometric monitoring device. The present invention is intended to cover both devices with onboard pressure sensors and position determination functionality, and devices that carry pressure sensors but rely on, for example, wirelessly accessing an off-site position determination system.

In block 1804, a first location may be determined from the location data. In some implementations, the first location may be a single coordinate obtained from location data. However, in other embodiments, the first location may be an average of multiple coordinates derived from the location data (eg, the last 20 location corrections). In yet other embodiments, the first location can be a plurality of locations.

In block 1806, the device may look up the first location in a historical terrain dataset (eg, a terrain contour database or an atlas, etc.). The device may use the historical terrain data set to determine a first terrain altitude corresponding to the first location. In some embodiments, historical terrain datasets may provide this data directly. In other implementations, it may be necessary to perform some level of analysis to extract such terrain altitudes. Examples of this analysis are discussed in more depth in Figures 21A-21C.

In block 1808, first altitude data associated with the first location may be received from the pressure sensor. It should be understood that, with respect to all altimeter-related disclosures presented herein, "altitude data" may be used to mean post-processed altitude data, such as "X ft" or "X meters"; may be considered to mean altitude pressure data such as "0.98 atmospheres" or "14.6 psi"; or voltage or current signals representing altitude or pressure. Those of ordinary skill in the art will understand how to convert between these different data formats, and while the discussion herein and the appended claims may refer to "altitude data" or "data representing altitude," it should be understood that in Within the scope of the invention, this data can be freely converted between different formats. For example, a first terrain altitude in feet may be obtained, and a first altitude data in terms of voltage signals may be obtained. The voltage signal can then be converted to a format that allows comparison with the first topographical altitude (or vice versa).

In block 1810, the first altitude data may be compared to a first terrain altitude. If there is a difference between the two that exceeds a certain threshold (eg, 5 ft), the pressure sensor may be recalibrated in block 1812 based on the first terrain altitude. Different pressure sensors may have different recalibration techniques. In some implementations, the pressure sensor can have a recalibration function that can be accessed by the device and used to recalibrate the pressure sensor. In other embodiments, the pressure sensor may not be capable of internal recalibration, and the device may require post-processing to recalibrate the output from the sensor. Once the recalibration is performed, the technique may involve proceeding to block 1814 where the technique may pause before returning to block 1802 again. If no recalibration is required, as determined in block 1810 , the technique may proceed directly to block 1814 . It should be understood that in some implementations, block 1814 may not involve waiting at all, ie, wait time = 0 seconds, and that the recalibration technique may actually always check the current measured altitude against the terrain data. However, given that errors in pressure sensor readings due to, for example, drift or weather are unlikely to occur quickly, at spaced time intervals (e.g., every It may be advantageous to perform a recalibration check at 5 minutes, 10 minutes, 15 minutes, 20 minutes, 30 minutes, 60 minutes, etc.). In some embodiments, the time interval can be set between 1 second and 24 hours.

Of course, while presented as cyclic in nature, the technique of Figure 18, and others discussed below, can also be practiced as a one-pass technique without automatic cyclic behavior.

19 depicts a flow diagram of a technique for recalibrating a pressure sensor based on terrain data obtained from a remote device.

In FIG. 19 , technique 1900 may begin in block 1902 , where a device (eg, a biometric tracking device) may receive location data from some form of location determination system, much the same as block 1802 . In block 1904 , a first location may be determined from the location data, again much the same as block 1804 .

In block 1906 , the device may look up the first location in a historical terrain dataset (eg, terrain contour dataset or map set, etc.) from the remote device to obtain a first terrain altitude, much as in block 1806 . For example, various online databases of historical terrain data may be consulted, such as the US Geological Survey database, third-party proprietary suppliers of terrain data, or other sources of terrain data, such as other sources from similar biometric monitoring devices (or other devices) The user's altimeter data can be location mapped and used to generate a historical terrain data set, which can then be subsequently used by the biometric monitoring device as a source of historical terrain data. This data set can be structured so that altitude data from newer biometric monitoring devices is weighted to a greater degree than altitude data from older biometric monitoring devices (or excludes data from biometric monitoring devices within a certain age altitude data). Since some pressure sensors/barometric altimeters may experience drift as they age, such weighting (or exclusion) may reduce the contribution of older biometric monitoring devices (and their replacements) to the data pool. Such techniques can be modified to more advantageously process data from biometric monitoring devices that, while older, are regularly recalibrated and thus comparable in performance to newer biometric monitoring devices, such as Compared to older biometric monitoring devices that recalibrate infrequently. In block 1908, the first terrain altitude may be received by the biometric monitoring device from the remote device.

The device may utilize some form of wired or wireless communication interface (eg, WiFi, Bluetooth, etc.) as part or all of the mechanism for establishing communication with the remote device.

In general, historical terrain data can be stored locally, for example, in the same device containing pressure sensors and/or position determining sensors, or remotely, for example, in a companion device (e.g., a smartphone) or in a device accessible via the Internet. in the server. Local storage may have the benefit of not requiring a communication connection to a remote device and consequently allowing faster access to data, but at the expense of increased power consumption and increased non-volatile memory storage requirements (storing historical terrain data, which may A relatively large amount of storage space is required, as compared to data stored by biometric monitoring devices). In some embodiments, a hybrid approach may be employed where a device may download a subset of historical terrain data from a remote device and store the subset locally, e.g. a local cache with referenced data due to its association with the surrounding area. High likelihood terrain data. Such conventions are applicable to any device that performs pressure sensor recalibration based on terrain data.

In block 1910, first altitude data associated with a first location may be received from a pressure sensor. In block 1912, the first altitude data may be compared to a first terrain altitude. If there is a difference between the two that exceeds a certain threshold (eg, 5ft), the pressure sensor may be recalibrated in block 1914 based on the first terrain altitude, much the same as block 1812 . Once the recalibration is performed, the technique may involve proceeding to block 1916 where the technique may pause before returning to block 1902 again. If no recalibration is required, as determined in block 1912 , the technique may proceed directly to block 1916 . It should be understood that in some implementations, block 1916 may not involve waiting at all, ie, wait time = 0 seconds, and that the recalibration technique may actually always check the current measured altitude against the terrain data. However, given that errors in pressure sensor readings due to, for example, drift or weather are unlikely to occur quickly, at spaced time intervals (e.g., every 5 minutes, 10 minutes, 15 minutes, 20 minutes, 30 minutes, 60 minutes, etc.) it may be advantageous to perform a recalibration check.

Figure 20 shows a plot of hypothetical pressure or altitude versus time. As can be seen, two graphs showing data representing altitude, one representing the "actual altitude", ie the true altitude of the barometric altimeter, and the other the "measured altitude", i.e. the altitude as measured by the barometric altimeter Altitude. The x-axis represents time units. As can be seen, as time progresses, the two altitude maps drift apart due to slight drift in the pressure sensor providing the measured altitude data. This drift becomes more and more significant during the drift period 2002 . During 2004, the pressure sensor is recalibrated based on techniques such as those described above and below. In 2006, it was seen that the recalibrated measured altitude closely maps to the actual altitude.

As mentioned, several techniques are available to determine terrain altitude from terrain datasets based on XY positions. A number of such examples are presented below, but these are not intended to be limiting, and instead of or in addition to or in combination with the listed examples may be used for obtaining terrain Various other techniques for altitude.

Figure 21A illustrates aspects of one technique for determining terrain altitude from historical terrain data. FIG. 21A shows a topographic map with XY locations 2108 (labeled by latitude location 2104 and longitude location 2102). As is the case with many position measurements, there may be an error represented by the envelope 2106 (circular in this case, but in some implementations, having other shapes). Envelope 2106 may be segmented by contour lines of the terrain dataset into segments 2110a, 2110b, 2110c, and 2110d. To arrive at the terrain altitude, the altitude represented by each segment may be multiplied by the corresponding segment area and then added together and divided by the total area of the envelope. This may provide an average estimate of the altitude of the terrain within the error envelope 2106 .

FIG. 21B illustrates aspects of another technique for determining terrain altitude from historical terrain data. FIG. 21B also shows a topographical map with XY locations 2108 (labeled by latitude location 2104 and longitude location 2102). In this example, the terrain altitude can be calculated by using the altitude closest to the terrain contour, or by averaging the altitudes of the two terrain contours that bracket the XY position together, or by enclosing the XY position in Interpolation between the altitudes of two terrain contours together (for example, interpolation along the shortest line compared to both the contour and the XY position).

Figure 21C illustrates aspects of yet another technique for determining terrain altitude from historical terrain data. 21C also shows a topographic map with XY locations 2108 (labeled by latitude location 2104 and longitude location 2102). 21C also shows an error envelope 2106 and a plurality of surrounding sample points 2114a, 2114b, 2114c, 2114d, 2114e, 2114f, 2114g, and 2114h. In this technique, the terrain altitude may be obtained by averaging the terrain altitude at each of the sample points (and in some other implementations, at the XY location 2108).

Of course, other techniques for obtaining terrain altitude from terrain datasets can also be used and are within the scope of the present invention.

In some embodiments, the frequency of pressure sensor or barometric altimeter recalibration may be adjusted based on changes in XY position. 22 depicts a flow diagram of one technique for recalibrating pressure sensors based on changes in XY position.

In FIG. 22 , technique 2200 may begin in block 2202 , where a device (eg, a biometric tracking device) may receive location data from some form of location determination system, much like block 1802 . In block 2204 , a first location may be determined from the location data, again much the same as block 1804 . In block 2206 , the device may look up the first location in a historical terrain dataset (eg, a terrain contour database or atlas, etc.), much the same as in blocks 1806 or 1906 . In block 2208, the biometric monitoring device may receive altitude data related to the first location from the pressure sensor. In block 2210, a determination may be made as to whether to recalibrate the pressure sensor based on the difference between the altitude data and the first terrain altitude. If the difference is above the first threshold, the pressure sensor may be recalibrated in block 2212 before proceeding to block 2214 . If not, the technique may proceed directly to block 2214 . In block 2214, a first terrain altitude difference threshold may be determined. In block 2216, historical topographical data may be analyzed to determine a second location that is both closest to the first location and is either higher or lower than the first topographical altitude difference threshold, as compared to the first location. In block 2218, a distance between the first location and the second location may be determined. In block 2220, an update time period may be set based at least in part on a distance between the first location and the second location. For example, the update time period may be fixed and set based on distance or based on distance divided by current speed, for example, if the distance is 5 miles and the current speed is 10 mph, then the update time period may be set to 0.5 hours. Such implementations may assume that pressure sensor recalibration may not be necessary until the wearer of the device causes an altitude change beyond the first terrain altitude difference threshold, and that this potential event is unlikely to occur until the person has traveled at least far enough As far as the closest obvious location possible for the event.

In some implementations, the update time period may be distance based but also dynamic. For example, if the distance is 5 miles, the update time period may be set so that it expires when the biometric monitoring device indicates, for example, that a total of 5 miles has been traversed. Once the update time period expires in block 2222, the technique may return to block 2202.

In another embodiment, the first terrain altitude difference threshold may also be used to establish a "fence". When the wearer of the device has not traveled far enough to potentially reach the edge of the fence, it may be assumed that its actual altitude is not above the first terrain altitude difference threshold, and if the pressure sensor indicates that it is above the first terrain altitude difference threshold , it may be recalibrated to the first terrain altitude difference threshold. This may be of particular use in large flat areas where there is little possibility of altitude change over large distances. In a more advanced implementation of this concept, the fence could actually be defined by the contours of the historical terrain dataset, and the user's real-world XY position could be compared to the fence at regular time intervals to determine if it has crossed the fence boundary, if It has not crossed, but the pressure sensor indicates a higher altitude than it should be possible given the altitude within the fence, then the pressure sensor can be recalibrated. For example, if a person is walking along a beach bounded by a cliff and the sea, the cliff may define a portion of the "fence". From historical terrain data, beaches may be determined to have an elevation of 0 ft to 10 ft, and cliffs may be greater than 10 ft in elevation. If the user stays on the beach, ie inside the fence, then his altitude should theoretically be between 0ft and 10ft, if the pressure sensor indicates a higher altitude, then a recalibration of the pressure sensor based on an altitude of 10ft can be performed. Alternatively or additionally, a recalibration check may be performed when the user of the device crosses the beach to the "cliff" area.

23 depicts a flow diagram for a technique for recalibrating a pressure sensor depending on its proximity to a building. The techniques described above provide for recalibration of the barometric altimeter or pressure sensor based on the difference between the terrain altitude and the measured altitude. In some cases, for a given location, the terrain data and the measured data may not correspond, but the two altitudes may be largely correct. For example, if an altimeter or pressure sensor is located at an XY position that also happens to be where a building is located (or near this building), then there is a chance that the person carrying the pressure sensor is actually inside the building, and if Buildings are multi-story, and a person may be at an actual altitude of tens or hundreds of feet above ground (ie, tens or hundreds of feet above the likely terrain elevation). Recalibrating the pressure sensor based on the terrain altitude at such locations may in fact incorrectly calibrate the pressure sensor. Thus, some techniques for recalibrating pressure sensors may take into account the proximity of nearby buildings (or may allow a person to be at an altitude other than ground level).

In FIG. 23 , technique 2300 may begin in block 2302 , where a device (eg, a biometric tracking device) may receive location data from some form of location determination system, much like block 1802 . In block 2304 , a first location may be determined from the location data, again much the same as block 1804 . In block 2306 , the device may look up the first location in a historical terrain data set (eg, terrain contour database or atlas, etc.), much the same as in blocks 1806 or 1906 . In block 2308, the biometric monitoring device may receive altitude data related to the first location from the pressure sensor. In block 2310, a determination may be made as to whether to recalibrate the pressure sensor based on the difference between the altitude data and the first terrain altitude. If the difference is above the first threshold, the pressure sensor may be recalibrated in block 2312 before proceeding to block 2314 . If not, the technique may proceed directly to block 2314 . In block 2314, a first threshold distance may be determined. In block 2316, it may be determined whether the first location is within a first threshold distance of a building (or other similar structure). Such determinations may be possible using USGS data (which often contain information showing the boundaries of buildings in their datasets) or other providers, for example, Google Maps contains building boundary information (and alternatively, in many metropolitan areas 3D models of buildings in the area). In block 2318, the update time period may be altered to account for proximity to the building. In some cases, the first threshold distance may be set to a value based on an error estimate of the position determination. For example, if the location determination is accurate to within 30ft, then the first threshold distance may be set to 31ft. Thus, if the location determination indicates that the building is 30ft away, recalibration may not be performed because the person wearing the device may actually be inside the building (due to the uncertainty in the location determination). However, if the location determination indicates that the building is 32ft away, then a recalibration check may be performed because it is unlikely that the person wearing the device is inside the building, even taking into account the uncertainty in the location determination.

For example, the update time period may be lost or aborted when the pressure sensor is within a first threshold distance of the building (or any building), and may be when the pressure sensor is at a distance greater than the first threshold distance from the building or other structure restart or terminate. In other embodiments, the update time period may simply be extended, e.g., to check whether altimeter recalibration is necessary, e.g. Reflects where the building is located.

24 depicts a flowchart of another technique for recalibrating a pressure sensor based on a change in XY position. In FIG. 24 , technique 2400 may begin in block 2402 , where a device (eg, a biometric tracking device) may receive location data from some form of location determination system, much the same as block 1802 . In block 2404 , a first location may be determined from the location data, again much the same as block 1804 . In block 2406 , the device may look up the first location in a historical terrain data set (eg, terrain contour database or atlas, etc.), much the same as in blocks 1806 or 1906 . In block 2408, the biometric monitoring device may receive altitude data related to the first location from the pressure sensor. In block 2410, a determination may be made as to whether to recalibrate the pressure sensor based on the difference between the altitude data and the first terrain altitude. If the difference is above the first threshold, the pressure sensor may be recalibrated in block 2412 before proceeding to block 2414 . If not, the technique may proceed directly to block 2414 . In block 2414, a first threshold distance may be determined. In block 2416, a second location may be determined. If the second location is at least a first threshold distance away from the first location, the technique may return to block 2402 . If not, the technique may return to block 2416 for gathering another second location. Using this technique, the pressure sensor recalibration check may be lost until the pressure sensor has moved a preset distance (eg, a first threshold distance) from a previous point at which the need for recalibration was assessed. In some implementations, the first threshold distance may be a land distance, ie, substantially flat (except possibly to account for the curvature of the Earth's surface). In other embodiments, the first threshold distance may be, for example, an absolute distance that also takes into account changes in altitude. In this case, the first threshold distance may also be referred to as a translation distance threshold. For example, this threshold may be between 50ft and 200ft, 500ft and 1000ft, or other values in other ranges.

25 depicts a flow diagram of a technique for recalibrating a pressure sensor based on slope. In FIG. 25 , technique 2500 may begin in block 2502 , where a device (eg, a biometric tracking device) may receive location data from some form of location determination system, much the same as block 1802 . In block 2504 , a first location may be determined from the location data, again much the same as block 1804 . In block 2506 , the device may look up the first location in a historical terrain dataset (eg, a terrain contour database or atlas, etc.), much the same as in blocks 1806 or 1906 . In block 2508, the biometric monitoring device may receive first altitude data related to the first location from the pressure sensor. In block 2510, a determination may be made as to whether to recalibrate the pressure sensor based on the difference between the first altitude data and the first terrain altitude. If the difference is above the first threshold, the pressure sensor may be recalibrated in block 2512 before proceeding to block 2514 . If not, the technique may proceed directly to block 2514 . In block 2514, a first threshold slope may be determined.

In block 2516, a second location may be determined from the location determination system, and in block 2518, second altitude data related to the second location may be received from the pressure sensor. In block 2520, a slope may be calculated based on the altitude difference represented by the first altitude data and the second altitude data and the distance between the first location and the second location. In block 2522 , the slope may be compared to a first threshold slope, and if the slope exceeds the first threshold slope, the technique may return to block 2502 . If the slope does not exceed the first threshold slope, the technique may return to block 2516 and other second location and second altitude data may be gathered.

In some implementations, as discussed above in some detail, position determination may utilize imaging from a camera. For example, a device having a pressure sensor and accessing image data from a camera may obtain a first image taken at a first location associated with a pressure sensor altitude or reading. The images can then be analyzed to determine an altitude based on the first image, which can then be used in the same manner as topographic altitude was in the techniques discussed above to determine whether the pressure sensor needs to be recalibrated, i.e. if based on the first image If the difference between the altitude at and the altitude measured by the pressure sensor exceeds a first threshold amount, the pressure sensor may be doomed to lack of calibration and may be recalibrated using the altitude based on the first image.

In some implementations, the altitude based on the first image may be determined by analyzing the image to determine, for example, a horizon silhouette corresponding to a particular viewpoint associated with a particular XY position and altitude. This data may be retrieved from a database of reference images, each reference image being associated with at least an altitude. The image can be compared to a reference image from a database, and if a suitable match is located, the altitude data of the matching reference image can be used as the altitude based on the first image.

In an alternative to other embodiments, optical character recognition (OCR) may be used to analyze images associated with pressure sensor altitude measurements to determine whether text-based information is present in the images. If text-based information is present in the image, the text-based information may be analyzed (eg, parsed) to determine whether the text-based information includes altitude data. For example, many common hiking destinations, scenic lookouts, and other points of interest include signs listing the elevation and other data at the point (see, eg, FIG. 26). These elevations are usually fairly accurate since they are derived from survey data. FIG. 26 depicts a photograph of a sign at Mount Tamalpais, California, that includes the altitude keywords "elevation" and "feet" with numerical values in between. Devices recalibrated using image-based pressure sensors can analyze text-based data to locate such altitude keywords (like phrases such as "elev," "alt," "altitude," "height," and "ht," As a non-limiting example, it may also serve as an indicator that the numerical data following or on the next line represents altitude data). As non-limiting examples, terms such as "m," "ft," "feet," "meter," or "meter" may serve as unit keywords that indicate the unit of a numerical altitude quantity. The numeric altitude quantity is usually located between the altitude keyword and the unit keyword in this flag, and is therefore easy to extract. Once extracted from the text-based data from the image data, the altitude can in fact be used as the first terrain altitude for the techniques above or the first image-based altitude in the techniques discussed above.

In a related embodiment, even if a sign or other source of text-based information in an image does not have altitude data explicitly listed, the text-based information can still be compared to a database of text-based entries to determine Whether the text's information corresponds to a database entry with an associated altitude. If a match is determined, the associated altitude may then be used in the techniques discussed above as the first image-based altitude or the first terrain altitude.

It should be understood that while the techniques above discuss the calibration of an altimeter using location information and, for example, terrain information, similar techniques may be applied in reverse to refine location information based on altitude data from an altimeter. For example, if the GPS position determination has a large error estimate, and the user is in a terrain with a lot of terrain variation, then the altimeter altitude data can be mapped to the terrain in the terrain dataset within the position determination's error estimate. Zones that fall within the error estimate may have varying altitudes, and the altitude data may be used to eliminate or filter out altitudes in the error estimate that do not have matching altitudes from the terrain data (or, more likely, are within a first threshold of altitudes ) possible XY positions. For example, if the person is on a terrain with a uniform slope and the error estimate results in the person's XY position lying in a large circular area on that slope, using altimeter data may allow the person's XY position to narrow to the circular area within a narrow band, thus improving position determination.

While the above techniques for altimeter recalibration have been outlined with respect to barometric altimeters, similar techniques for altimeter recalibration may be used with other types of altimeters that may be developed, and the present disclosure includes such implementations within its scope.

Altimeter Assisted Posture Recognition

In some embodiments, the biometric monitoring device may include an altimeter, such as a barometric altimeter or a pressure sensor (or other types of altimeter with similar or better performance), as well as other biometric sensors (such as accelerometers, heart rate sensors, etc.) and/or or environmental sensors (eg, ambient light sensor, ambient air quality sensor, etc.). As previously discussed, barometric altimeters can be quite accurate, especially for measuring changes in altitude over short periods of time (eg, seconds) (as opposed to absolute altitude).

In general, for a biometric monitoring device worn on a person's arm, forearm, wrist, hand, or finger (or, in some embodiments, on a person's leg, calf, foot, or toe), from Biometric Monitoring Device Altitude data for (as discussed above, which may include data indicative of actual altitude, data indicative of pressure that may represent altitude, and data from sensors that may be used to determine altitude (e.g., voltage output of a pressure sensor)) may be combined with time-corresponding motion data from one or more biometric sensors (e.g., accelerometers, magnetometers, gyroscopic sensors, piezoelectric sensors, electromagnetic trackers, camera-based trackers, etc.) of the biometric monitoring device, to recognize various types of gestures. Such gestures may be, for example, gestures or movements that are characteristic of a particular activity or movement. For example, swinging a baseball bat may be considered one category of posture, while curling a dumbbell may be considered another category of posture.

27 depicts a simplified block diagram of an example biometric monitoring device with an altimeter and position determination system. As can be seen, the biometric monitoring device of FIG. 27 includes one or more processors 2704 and memory 2706 communicatively connected to each other to form a controller 2702 . The controller 2702 is communicatively coupled with an altimeter or pressure sensor 2708 and with one or more motion sensors 2710 (eg, sensors such as accelerometers, gyroscope sensors, and/or magnetometers).

28 depicts a flow diagram of a technique for using altitude data and motion data from a biometric monitoring device to recognize gestures made by a person wearing the biometric monitoring device. Technique 2800 may begin in block 2802, where altitude data may be received by an altitude sensor of some kind (eg, a barometric altimeter or other sensor capable of detecting altitude or providing an output from which altitude may be determined). As discussed earlier herein with respect to altimeter recalibration, the term "altitude" is used herein to refer to both altitude and data from which altitude can be derived (including, for example, pressure data from pressure sensors, voltage levels from pressure sensors, etc.) By. In some embodiments, data from, for example, a pressure sensor may never be converted to a working unit of linear distance measurement (e.g., feet or meters) in order to recognize a particular posture, however, pressure changes reflected in this data may still be used as altitude indicator of altitude change, and it should be understood that regardless of whether the device uses motion data in conjunction with altitude data processed in terms of feet or meters (distance) or in terms of pressure or some other unit, the scope of the present invention is intended to cover such Such uses, and at least with respect to altimeter-assisted gesture recognition discussed herein, the term "altitude data" or "altitude" is intended to encompass such implementations.

In block 2804, motion data may be captured from one or more motion sensors of the biometric monitoring device. In the examples discussed herein, this motion data takes the form of accelerometer data, but other forms of motion data, such as gyroscope data or magnetometer data, may also be used. It should be understood that, with respect to the altimeter-assisted gesture recognition techniques and systems discussed herein, the term "motion data" may also include data indicative of instantaneous position or orientation (without necessarily describing motion). However, this data may be at least somewhat indicative of the motion experienced by the biometric monitoring device, given that such orientation or position sensors may provide outputs that vary based on changes in such orientation or position. Motion data captured from a biometric monitoring device may reflect the motion or orientation of the biometric monitoring device relative to some external frame of reference, and may be used to infer the position or movement of the limb wearing the biometric monitoring device. For example, for a wristwatch type biometric monitoring device, the biometric monitoring device may be worn on a person's forearm near the wrist joint. Accordingly, motion data from the biometric monitoring device may reflect the motion of the forearm on which the biometric monitoring device is worn.

In block 2806, a determination may be made as to whether the motion data and the altitude data are associated in combination with a first arm movement profile selected from a plurality of arm movement profiles. Such arm movement profiles may take various forms representing patterns of motion and altitude data, including, for example, time windowed motion and altitude characteristics. In some cases, the biometric monitoring device may be placed in a particular mode (eg, resistance training mode), and the activity mode may cause the plurality of arm movement profiles to be filtered so that only arm movement profiles relevant to the activity mode are available for selection.

In block 2808, data associated with the first arm movement profile may be stored. For example, this data may include, for example, performance or user shape data (indicating "how well" a gesture is performed compared to some standard gesture), how many repetitions of the gesture are made, how long such gestures last, the frequency of the gesture and other data. Thus, for example, for resistance training exercises, the biometric monitoring device may identify various poses associated with different types of resistance training exercises (and avoid identifying poses associated with non-resistance training exercises).

Examples of arm movement profiles that may be used to identify particular gestures may include, but are not limited to, arm movement profiles that approximate arm movements representing one or more activities such as: resting, running, walking, elliptical exercise, resistance training Workouts, pull-ups, push-ups, sit-ups, skipping rope and cardio dancing.

It should be understood that while the discussion herein regarding altimeter-assisted gesture recognition focuses on the use of a biometric monitoring device worn on a person's forearm, and on the recognition of arm gestures based on arm movement profiles, similar concepts and techniques may be used for worn Biometric monitoring devices in other locations (eg, on a person's leg, upper arm, finger, etc.). The concept of arm movement profiles can be applied to leg movement profiles (for leg-worn biometric monitoring devices), hand movement profiles, finger movement profiles, etc., where appropriate. Furthermore, while the discussion herein focuses on the wearing of a single biometric monitoring device, such techniques may also be practiced using multiple biometric monitoring devices. For example, a person may wear a biometric monitoring device on their forearm and a second biometric monitoring device on the upper arm of the same arm; the two biometric monitoring devices may communicate with each other to facilitate data sharing. Each such biometric monitoring device can provide altitude data and motion data, which can be used to provide additional insight into the movement characteristics of a person's arm and the poses a person's arm can take.

Figure 29 depicts a biometric monitoring device worn on a person's forearm and a coordinate system for the biometric monitoring device acceleration sensor; reference is made to this coordinate system with respect to Figures 30 through ZU. For clarity, Figure 29 depicts a human arm wearing a wristband style biometric monitoring device. For purposes of discussion, assume that a motion sensor, such as a three-axis accelerometer, is included within a biometric monitoring device, where the X-axis is aligned with the person's forearm and extends in a positive direction toward the person's wrist on that arm, and the Y-axis is transverse to The forearm is and generally parallel to the face of the biometric monitoring device (in this case, parallel to the person's hand/palm and extending away from the person's little finger), and the Z axis is transverse to the forearm and perpendicular to the Y axis. Of course, other coordinate systems may be used, as the choice of coordinate system is relatively arbitrary and this coordinate system is for discussion purposes only.

30 depicts examples of postures associated with a bench press-type resistance training exercise and hypothetical motion and altitude data that may be used to identify the postures. A bench press-type resistance training exercise is one in which a person rests on their back (usually on a knee-high bench) and lowers a barbell (or other weight) to chest level and then lifts the barbell up until their arms are straight (Figure 30). The image on the left shows the lowering to near chest level, and the image on the right shows the person's arms straight and the barbell raised). Black arrows indicate approximate orientation of the X-axis during bench press; white arrows indicate movement of the barbell. In a typical bench press, the bar is moved upwards a distance Δh governed by the length of the person's upper arm and a portion of the person's forearm, such as the length of the person's forearm (as measured from the elbow to the wrist) and the length of the person's arm (as measured from the person's shoulder to measured by the human wrist). Of course, this distance may vary depending on the physical attributes of the person in question, but typically this Δh may be between 1 and 2 feet.

It should be understood that, for any of the techniques of gesture recognition discussed herein, the particular size values that may be used to classify a gesture may be based on default values derived, for example, from demographic information, or may be customized to a person's build. For example, a person with shorter arms may customize parameters describing the length of their forearms and the length of their upper arms, and such customized values may be used to determine appropriate values for Δh.

The data graph shown at the bottom of FIG. 30 represents hypothetical data obtained from the accelerometer and altimeter in a biometric monitoring device worn on a person's forearm in this situation and other similar examples discussed below. superior. Acceleration data shown correlates to acceleration measured by an accelerometer aligned with the X-axis. Thus, the acceleration data may exhibit a largely constant negative 1 G acceleration due to the pull of the Earth's gravitational field. Acceleration data may also exhibit slight changes (not shown) as a person lifts and lowers heavy objects, but the magnitude of these instances may be less than the acceleration attributable to the Earth's gravitational field. In this case, the X-axis acceleration can remain relatively constant because the orientation of the person's forearm changes very little during the bench press. In contrast, the hypothetical altimeter data exhibits a very pronounced cyclic behavior, where the altitude of the human wrist moves up and down by a distance Δh. Each such cycle of movement may correspond to one repetition or "rep" of the exercise.

Similar properties can also be observed for squat-type exercises, as in the squat, a person grasps a barbell or weight across their shoulders with their upper arms directed downward and their forearms directed upward, and then their legs are bent over Move the barbell down. In order to differentiate between squat and bench press workouts based on altitude data and motion data, additional sensors may be necessary. For example, if a person wears an additional biometric tracking device on the upper arm (with a similarly oriented coordinate system, e.g., where the x-axis is aligned with the upper arm), x-axis data from the upper arm-located biometric monitoring device can be used in a bench press exercise. exhibited different behavior than during squat exercises. For example, in a squat, a person's arms may remain relatively fixed relative to their upper torso, and thus the acceleration measured by an upper arm positioned biometric monitoring device may not experience any drastic changes during the squat exercise. In contrast, in the bench press exercise, the upper arms transition from a horizontal or slightly downward orientation to a vertical upward orientation. Such transitions can be evident in athletic data and can be used to aid in distinguishing between two workouts. Another potential means for discriminating between the two exercises is through the use of Δh; squats can involve greater elevation changes than can be observed in bench presses.

Arm movement profiles looking for such behaviors may model data such as the one shown in Figure 30, although other arm movement profiles may also or alternatively be used. For example, various combinations of orientations indicated by accelerometer data (or other motion data) and altitude data may be used to determine which arm movement profile is the best fit for a given set of motion data and corresponding altitude data of. For example, if the altitude data indicates that the wearable biometric monitoring device experiences an altitude increase approximately between one upper arm length and one upper arm length plus one forearm length (e.g., between about 0.5 ft and 2 ft for a typical human ), followed by a similar decrease in altitude during a second time period after the first time period (or, in some embodiments, before the first time period), with the person's forearm between the first and second Maintaining a substantially vertical upward orientation (ie, wherein the hand is held above the elbow) during the time period, this behavior can then be interpreted as indicating a bench press arm movement profile.

If multiple substantially consecutive instances of motion and altitude associated with an arm movement profile are detected, such as the four instances evident in the data of Figure 30, each such instance may be interpreted as being associated with a selected arm movement profile Linked workout repetitions. Thus, 4 repetitions of the bench press are represented in Figure 30.

In some embodiments, arm movement profiles may be identified in real time. However, in other implementations, the biometric monitoring device may check the motion data and altitude data with some delay so that periodic behavior may be more apparent. For example, in the context of resistance training exercises, where it is common for a person to perform multiple repetitions of the same exercise, there may be a high degree of commonality in the motion data and altitude data for each repetition of the exercise, which may be used Any of various forms of pattern analysis facilitate the extraction of one-repetition data, which can then be evaluated to determine whether the one-repetition data correlates with the data for the selected arm movement profile. In some implementations, the biometric monitoring device may be configured such that arm movement profiling occurs only after the end of a collection of periodic data traces, which may indicate the end of a collection of one type of exercise. Collection processing thereafter may provide a more accurate estimate of overall performance, but may not be applicable in situations where real-time feedback to the user needs to be provided. For example, if the biometric monitoring device was configured to play music every five repetitions of a resistance training exercise, the biometric monitoring device would need to monitor in real time the number of occurrences of the poses associated with that exercise for the repetition count to be accurate and is up to date.

31 depicts examples of postures associated with a lateral raise-type resistance training exercise (also referred to herein as a lateral raise-type exercise) and hypothetical motion and altitude data that can be used to identify the postures.

A side raise resistance training exercise is an exercise in which a person grasps a dumbbell (or a piece of equipment that approximates the weight) in each hand, stands with their arms at their sides, and then rotates each arm around the shoulders to So that the arms are basically in line with each other and level. The arm is then allowed to return to the side of the person.

The left image in Figure 31 shows a side raise exercise with the person's arms at their sides, and the right image shows the same person with the arms extended. The black arrows indicate the approximate orientation of the X-axis during the lateral raise exercise; the white arrows indicate the movement of the dumbbell. In a typical lateral raise, the dumbbell moves upwards a distance Δh, which is typically governed by the combined length of a person's upper arm and forearm. Of course, this distance may vary depending on the physical attributes of the person in question, but typically this Δh may be between 1 and 2 feet.

The data plot shown at the bottom of Figure 31 represents hypothetical data obtained from the accelerometer altimeter of the biometric monitoring device. Two acceleration profiles are shown, one for the X axis and one for the Z axis. As can be seen, at the start of the repetition, the X-axis acceleration may be approximately 1G because the forearm is pointing down and the Earth's gravitational field essentially exerts an acceleration of 1G along the forearm axis. Meanwhile, the Z-axis acceleration is about 0G because the Z-axis is essentially horizontal and thus not affected by the acceleration due to the Earth's gravitational field. However, as the person raises their arms, the acceleration along the X axis drops cooperatively with the corresponding drop in acceleration to -1 G on the Z axis (which is negative, simply due to the orientation of the coordinate system). Near the end of the repetition, the X-axis acceleration and Z-axis acceleration return to approximately their original values. At the same time, the altitude data may exhibit an increase in altitude, Δh, which, as noted above, may be approximately between 1 and 2 feet.

Similar properties were also observed for front raise type exercises, and similar techniques were used to identify such exercises.

Arm movement profiles looking for such behaviors may model data such as the one shown in Figure 31, although other arm movement profiles may also or alternatively be used. For example, if the altitude data indicates that the wearable biometric monitoring device experienced an altitude increase during the first time period of approximately one upper arm length plus one forearm length (e.g., between about 1 ft and 2 ft for a typical human or between 1.5 ft and 2 ft), followed by a similar decrease in altitude during a second time period after the first time period (or, in some embodiments, before the first time period), and the motion data Indicates that the human forearm transitions from a substantially vertical downward orientation (i.e., wherein the hand is held below the elbow) to a substantially horizontal position during a first time period, and the human forearm transitions from a substantially horizontal position to a substantially horizontal position during a second time period vertical position, then this behavior can be interpreted as indicating a lateral raise, lateral raise, or front raise moving profile.

32 depicts an example of a pose associated with a deadlift-type resistance training exercise and hypothetical motion and altitude data that may be used to identify the pose.

A deadlift-type resistance training exercise is typically an exercise in which a person raises a barbell off the ground and up to hip height and then lowers the barbell again. Lifting is accomplished primarily by moving the back and legs; the arms remain extended upwards facing downward.

The left graph in Figure 32 shows a deadlift-type exercise just after the person lifts the barbell; the right graph shows a deadlift-type workout just after the person has lifted the barbell. Black arrows indicate the approximate orientation of the X-axis during deadlift-type exercises; white arrows indicate movement of the barbell. In a typical deadlift, the barbell is moved upwards a distance Δh, typically defined as the point where a person's fist is about 2" to 8" from the ground when the person is standing with their hands at their sides (depending on the barbell plates used). The diameter, which can control the distance measured between the deadlift exercise (the distance that can be lifted). Of course, this distance may vary depending on the physical attributes of the person in question, but typically this Δh may be between 1.5 and 2 feet.

The data plot shown at the bottom of Figure 32 represents hypothetical data obtained from the accelerometer altimeter of the biometric monitoring device. As can be seen, during repetitions of a deadlift, the X-axis acceleration may be approximately 1G because the forearm is pointing down throughout the lift and the Earth's gravitational field essentially exerts an acceleration of 1G along the forearm axis. Meanwhile, the altitude data may exhibit an increase in altitude, Δh, which, as noted above, may be approximately between 1.5 feet and 2 feet.

Arm movement profiles looking for such behaviors may model data such as that shown in Figure 32, although other arm movement profiles may also or alternatively be used. For example, if the altitude data indicates that the wearable biometric monitoring device experienced an altitude increase of about Δh during the first time period (eg, between about 1.5ft and 2ft for a typical person), followed by A similar decrease in altitude during a second time period after the first time period, and the motion data indicates that the person's forearm remained in a substantially vertically downward orientation during the first time period and the second time period, then this behavior can be accounted for Translates to instruct deadlift arm movement profile.

33 depicts examples of poses associated with a curl-type resistance training exercise and hypothetical motion and altitude data that may be used to identify the poses.

A curl-type resistance training exercise is typically an exercise in which a person lifts a dumbbell from a position where their arms are normally extended downward, into a position where their arms are straight relative to their upper arms, and returns the dumbbells to the starting position.

The left diagram in Figure 33 shows a curl-type exercise just before the person lifts the dumbbell (in this case, each hand lifts the dumbbell, but the curl can be done in an alternating fashion or one arm at a time, since each arm can Moving independently, a biometric monitoring device on each forearm may be required to accurately track the number of repetitions performed by each arm); the figure on the right represents a curl-type exercise just after the person has lifted the dumbbell. Black arrows indicate approximate orientation of the X-axis during curl-type exercises; white arrows indicate dumbbell movement. In a typical curl, the dumbbell moves upwards a distance Δh, which is typically governed by a distance about twice the length of a human forearm. Of course, this distance may vary depending on the physical attributes of the person in question, but typically this Δh may be between 1 ft and 2 ft.

The data plot shown at the bottom of Figure 33 represents hypothetical data obtained from the accelerometer altimeter of the biometric monitoring device. As can be seen, during a repetition of the flexed arm, the X-axis acceleration may be approximately 1G during the first time period, and may be approximately -1G during the second time period; this is due to the fact that the forearm is substantially The above is the opposite fact. At the same time, the altitude data may show an increase in altitude Δh during the first period of time and a decrease in altitude Δh during the second period of time. As noted above, Δh may be approximately between 1 and 2 feet.

Arm movement profiles looking for such behaviors may model data such as that shown in Figure 33, although other arm movement profiles may also or alternatively be used. For example, if the motion data indicates that the forearm wearing the biometric monitoring device transitions from a downward-pointing orientation to an upward-pointing orientation during a first time period, followed by a transition back to a downward-pointing orientation during a second time period following the first time period Orientation, and if the altitude data indicates that the wearable biometric monitoring device experienced an altitude increase Δh (e.g., typically between about 1 foot and 2 feet) during the first time period, then this situation may be interpreted as indicating flexion Arm Workout Arm Movement Profile.

34 depicts examples of poses associated with a good morning resistance training workout and hypothetical motion and altitude data that can be used to identify the poses.

A good morning resistance training exercise is, for example, an exercise in which a person stands with a barbell resting on their shoulders, with the bar passed over the back of their neck and grasped in both hands. From a standing position, the person then turns their torso forward, with some optional bending of the knees, until their torso is substantially horizontal (as if bowing to someone). The person then returns to the upright position.

The left image in Figure 33 shows a good morning exercise just before the person has turned their torso into a horizontal position; the right image shows a good morning exercise just after the person has transitioned to a horizontal torso position. Black arrows indicate the approximate orientation of the X-axis during the Good Morning workout; white arrows indicate the movement of the barbell. In a typical good morning, a biometric monitoring device worn on the forearm can move up and down a distance Δh, typically governed by a distance approximately equal to the body length of the person performing the good morning. Of course, this distance may vary depending on the physical attributes of the person in question, but typically this Δh may be between 1.5ft and 2ft.

The data plot shown at the bottom of Figure 33 represents hypothetical data obtained from the accelerometer altimeter of the biometric monitoring device. As can be seen, during the good morning repeat, the X-axis acceleration at the beginning of the good morning repeat due to the upward facing orientation of the forearm at this stage may be approximately 1 G, and the Z-axis acceleration due to this orientation may be zero or close to zero. When the person bows forward, the X-axis acceleration can approach zero and the Z-axis acceleration can drop to about -1G. When the person straightens up, the acceleration can return to its original state. Meanwhile, the altitude sensor can indicate the altitude change Δh, which can usually range from 1.5ft to 2ft.

Arm movement profiles looking for such behaviors may model data such as that shown in Figure 33, although other arm movement profiles may also or alternatively be used. For example, if the motion data indicates that the forearm wearing the biometric monitoring device transitions from pointing up to a substantially horizontal orientation during a first time period, followed by transitioning back to pointing during a second time period after the first time period Orienting upward, and if the altitude data indicates that the wearable biometric monitoring device experienced an altitude decrease Δh (e.g., typically between about 1 foot and 2 feet) during the first time period and a corresponding altitude during the second time period height, then this situation can be interpreted as indicating good morning exercise arm movement profile.

While the examples above have focused on resistance training exercise pose recognition, altimeters can be used in conjunction with motion sensors to sense a wide range of poses used in a variety of sports.

In one such implementation, the biometric monitoring device may be configured to recognize use of the elliptical exerciser. In an elliptical exercise machine, a person walks, runs, or climbs stairs in place. A person's foot typically rests on a platform that follows an elliptical path; because the person's foot is continuously supported by the platform, there is little impact on the person's foot and its joints from the walking/running/stair climbing activities simulated by the elliptical exercise machine. influences. Many elliptical exercise machines also feature handle bars that the user can grasp while using the machine; the handles typically move in shallow horizontally oriented arcs in sync with the number of steps taken.

35 depicts an example of hypothetical motion and altitude data that may be used to differentiate between postures associated with walking and postures associated with using an elliptical trainer. For example, a biometric monitoring device may detect elliptical exerciser use in much the same manner as it detects walking behavior (eg, using a peak count algorithm). Accelerometer data representing actual walking behavior may have a regular series of spikes, such as that shown in FIG. 35 . In contrast, accelerometer data representing walking on an elliptical machine may exhibit similar periodicity, but may exhibit drastically reduced magnitude due to the low impact volume experienced while using the elliptical exercise machine, as in Figure 35 Acceleration graph shown for walking on the elliptical machine. However, there are also other activities (eg, bicycling) that can produce this regular cyclic behavior with reduced magnitude. In such cases, motion data may be combined with altitude data to potentially discriminate between elliptical exerciser use and other low-impact cycling activities (eg, biking). For example, because a person using an elliptical exerciser can keep their handles on the handle bar of the elliptical exerciser, their hands (and thus the biometric monitoring device on the wearer's forearm) can be synchronized with the number of steps taken. Going through shallow cyclical altitude changes, these altitude changes won't happen on the bike because the handlebars on the bike stay at a constant altitude while pedalling. Thus, the elliptical exerciser arm movement profile may be based on factors such as the characteristics discussed above.

For example, altitude data obtained by a biometric monitoring device may exhibit cyclic behavior during a first time period, wherein small altitude changes during each cycle correlate with the altitude experienced by the handles of the elliptical machine during normal use. The changes are commensurate. During the first time period, the athletic data obtained by the biometric monitoring device may exhibit cyclic behavior with substantially the same frequency as the cyclic behavior in the altitude data. The motion data may include acceleration data indicative of an acceleration magnitude during the first time period that is less than an acceleration magnitude otherwise used by the biometric monitoring device to determine whether to return A pedometer is incremented due to acceleration data indicative of a person wearing the wearable biometric tracking device engaging in normal walking or running activity with the person's feet off the ground. Altitude data and motion data having such characteristics can be correlated with elliptical exerciser arm movement profiles.

In some embodiments, a biometric monitoring device having altimeter motion sensors may be configured to compare data provided by these sensors, or data derived at least in part based on data from such sensors, with a particular reference motion profile, and determine the reference motion The degree of deviation of the profile from this data. In the context of this discussion, an arm motion profile (eg, a first arm motion profile) may refer to a data profile indicative of the movement of a person wearing an executing biometric monitoring device, eg, as the movement of a person's forearm. The first arm motion profile may contain motion sensor-based data, as well as altimeter sensor-based data; in some embodiments, it may also contain orientation or position data at fixed points in time. In contrast, a reference motion profile may refer to information indicative of data that would be produced by, or derived from, the sensors when said sensors are used to record data during a "normal" or "ideal" performance posture of a particular activity.

For example, in the context of a resistance training exercise, a particular resistance training exercise may involve performing certain movements at certain speeds and holding certain positions for certain periods of time. For example, moving a dumbbell quickly at the start of a curl can be considered bad form (or bad practice) in a curl because it can have the effect of affecting the dumbbell through inertia, which can occur during the lift. Minimize muscular exercise during recovery. Arm motion profiles derived from data obtained during this poorly shaped curl may reflect such undesirable behavior. In contrast, a reference motion profile for a curled arm pose may include information reflecting slower lift velocities at the beginning of the curled arm, since such slower velocities produce a more intense workout and are considered more desirable.

In some implementations, a reference motion profile may be defined so as to encompass an "ideal" posture, while in other implementations, the reference motion profile may be less stringent and may encompass "normal" or "acceptable" postures.

The biometric monitoring device may be configured to determine the degree of deviation between the reference motion profile and the arm motion profile; this deviation information may be used to provide an estimate of the shape of the person when performing the gesture in question. The degree of bias can be assessed using any of a variety of techniques. For example, in some implementations, a reference motion profile may include defined boundaries for various parameters over time, and if a corresponding parameter in the arm motion profile falls within the boundaries, then the arm motion profile may be determined to indicate "Good shape" is either given a low or zero degree of deviation from the reference profile. In some other or additional embodiments, the reference motion profile may be a collection of data sets, each data set indicating an "ideal" for various sensor outputs (or data derived therefrom) for a particular gesture over time. value, and the deviation analysis may involve comparing how well the corresponding sensor output from the arm motion profile (or data derived therefrom) matches the ideal data set. For example, a reference motion profile may include triaxial acceleration, triaxial velocity, triaxial jerk, and triaxial position data representing an ideal posture for a particular activity, and corresponding data from an arm motion profile may then be compared with the reference motion profile data. Compare to determine how much deviation there is between the two profiles.

In some cases, the first arm movement profile may also serve as a corresponding reference motion profile for a particular pose, but in other implementations the two profiles may be different. For example, a less stringent set of criteria can be used in a first arm movement profile to identify a particular motion or gesture, and a stricter set of criteria can be used in a corresponding reference motion profile to determine how well that motion or gesture is performed .

Embodiments of a biometric monitoring device configured to evaluate the shape of a gesture may be configured to utilize a first reference motion profile associated with one or more gestures, such as: swinging a baseball bat; swinging a softball stick; swinging a tennis racket; swinging a badminton racket; swinging an English squash racket; swinging an American squash racket; swinging a golf wood; swinging a golf iron; swinging a golf wedge; swinging a golf putter; swinging a golf chip and putt swinging a golf club; performing resistance training exercises; performing martial arts movements; performing gymnastics movements; performing yoga exercises; hitting a ball in a pool sport such as pool; hitting a ball in a billiard sport such as snooker; Hitting a ball in pool sports such as pocket pool; swimming; bowling; shooting with a rifle; shooting with a pistol; stabbing with a knife; swinging and parrying with a knife.

In a biometric monitoring device where there is gesture shape evaluation, such as discussed above, the biometric monitoring device may determine whether the degree of deviation between the arm motion profile of the gesture and the reference motion profile exceeds a certain deviation threshold. If the deviation threshold is exceeded, the biometric monitoring device may be configured to provide some form of feedback or indication to the user. For example, a biometric monitoring device may include a feedback mechanism such as a display, one or more lights, an audio device, and/or a vibration motor. Feedback or indications may for example be (depending on the available feedback mechanism): numerical scoring, vibration, sound, graphics, icons, animations, messages indicating fatigue, messages warning the wearer to slow down and messages warning said wearer to take a break . Some implementations of the biometric monitoring device may also store the evaluation of the shape based on the degree of deviation for future reference, eg, for downloading to a website for later viewing. This may allow the person's progress in shape to be assessed over extended time intervals so that any improvement (or deterioration) in shape can be observed.

As discussed above, a biometric monitoring device may be configured to track repetitions of some activity (eg, a resistance training exercise). For example, a biometric monitoring device may analyze motion and altitude data and determine that there are 5 repetitions of a particular gesture (also referred to herein as an "example" with respect to altimeter-assisted gesture recognition). In some such implementations, the biometric monitoring device may use the number of apparent repetitions to assist in determining whether a repeated gesture represents a particular activity. For example, if a person is riding a merry-go-round, they may sit on a horse that moves up and down and grasp the bar with their hands so that their forearms are aligned with the bar. While the carousel is in motion, a wrist-worn biometric monitoring device can produce data somewhat similar to that produced during a bench press exercise, such as repeated 1ft to 2ft elevation changes coupled with a substantially vertical orientation of a person's forearm . However, a person may experience hundreds of repetitions of up and down motion on a merry-go-round, whereas this number of repetitions for resistance training would be highly atypical. Thus, repetition counts can also be used as a factor for identifying a gesture or series of gestures.

In some embodiments, the biometric tracking device may be configured to track between 1 and 5 instances or repetitions, ii) between 5 and 8 instances or repetitions, iii) 8 to 10 instances or repetitions of a particular gesture. between 10 examples or repetitions, iv) between 10 and 12 examples or repetitions, v) between 12 and 15 examples or repetitions or vi) between 15 and 20 examples or repetitions. In some embodiments, the biometric monitoring device may adapt the number of repetitions considered "typical" for the user's resistance training, depending on the type of resistance training the user is engaging in: e.g. training increases strength, training increases substantially equally Strength and muscle mass, training builds muscle mass and emphasizes some strength, trains builds muscle mass and emphasizes some endurance, trains builds endurance and emphasizes some muscles and trains primarily to build endurance. Thus, the number of repetitions that may be "typical" for a bench press exercise may vary depending on the type of resistance training a person is doing, and the person may include the repetition count in the case of a biometric monitoring device that uses the rep count to assist in identifying the pose. Changes how biometric monitoring devices recognize gestures while inside.

In some implementations, a biometric monitoring device may include a display that changes what is displayed based on certain movements or gestures. For example, if a person wearing such a biometric monitoring device on their forearm moves the forearm into a watch-watching position, this movement can be evaluated as corresponding to the arm movement profile for looking at the watch. This implementation is described in more detail in U.S. Patent Application Serial No. 14/029,763, filed September 17, 2013, the entire contents of which are hereby incorporated by reference for that purpose. If the biometric monitoring device includes an altimeter, the biometric monitoring device may combine the altitude data from the altimeter with the motion data to provide a more accurate determination of when a person moves their forearm into the watch-watching position. For example, motion data may indicate that a person has moved their forearm from a downwardly oriented position to a horizontally oriented position, which is coupled with an altitude change of about the length of the forearm (eg, about 1 ft). In some implementations, the biometric monitoring device may further determine that the altitude remains constant for a non-zero period of time immediately following the period of time in which the motion occurs before changing what is displayed. The biometric monitoring device may change what is displayed in response to detecting watch movement by, for example, turning on the display and displaying the time or other data.

In some implementations of a biometric monitoring device equipped with an altimeter, the altimeter may serve as a mechanism for determining when to wear (or not wear) the biometric monitoring device. For example, a biometric monitoring device with an altimeter may monitor altimeter data to determine whether the altitude indicates that the biometric monitoring device has not experienced an altitude change rate that exceeds a first altitude change rate during a first time period. If the first altitude change rate threshold is not exceeded within the first time period, the biometric monitoring device may determine that it is not currently being worn, and may transition to a low power standby or off state, for example. In some such embodiments, the biometric monitoring device may also include a shock or acceleration sensor, and may only detect a shock event from this shock or acceleration sensor (e.g., a shock event may occur when the biometric monitoring device is placed on a hard surface). occurs) after this altitude rate-of-change threshold monitoring is performed.

In some other such implementations, the biometric monitoring device may monitor the altitude data after transitioning to the low power state to determine whether the altitude change rate has changed beyond a second altitude rate change threshold (and may be, for example, a non-zero altitude rate of change, any change in altitude may be a sufficient indicator that the biometric monitoring device has been picked up and should be turned on). If this altitude change is detected, the biometric monitoring device may transition from a low power state to a higher power state and/or turn on a display of the biometric monitoring device to display a message.

Although the above discussion has focused on the wearing of a single biometric monitoring device, such techniques may also be practiced using multiple biometric monitoring devices. For example, a person may wear a biometric monitoring device on their forearm and an upper arm on the same arm.

Altimeter Assisted Airplane Mode

In some implementations, a biometric monitoring device with an altimeter (or other electronic device with an altimeter) can analyze altimeter data to determine when to transition itself into "airplane mode." This concept is briefly discussed earlier in this disclosure and is explored in more depth here.

In general, most altimeters are pressure-driven devices, and thus may provide inaccurate altitude data in aircraft that are pressurized above certain altitudes. However, the technique below can also be practiced using non-pressure-based altimeters, such as altitude measurements obtained via GPS measurements (which are notoriously inaccurate, but given the large altitude magnitudes involved, It may be more suitable for aircraft mode determination purposes). If a non-pressure based altimeter is used, the portion of the technique discussed below that takes into account the pressurization of the aircraft may be omitted as it is not necessary.

It should also be understood that the term "altitude sensor data" as used herein with respect to the aircraft mode implementation of the biometric monitoring device refers to data that is commonly used to determine altitude (although, as discussed above, the altitude calculated from this data can be Inaccurate in some conditions where the altitude measurement is pressure based and the aircraft is pressurized above a certain altitude). This data may be provided from altitude, with units directly proportional to altitude, or units requiring conversion or other post-processing to provide altitude, such as pressure or a voltage indicative of pressure. The techniques outlined below may be performed using altitude sensor data in any of these formats, with some modifications.

36 shows a flowchart detailing an example algorithm that may be used by a biometric tracking device to determine whether to place the biometric tracking device in a mode associated with airplane travel. In certain implementations, the biometric tracking device may detect changes in stress and/or motion characteristics of an aircraft taking off or landing by receiving altitude sensor data from the altitude sensor. The biometric tracking device may have a controller including memory containing computer-executable instructions to control the processor to analyze data from an altitude sensor or other sensor to determine whether the sensor data indicates that an aircraft is taking off or landing.

The memory may contain various algorithms to determine whether the altitude sensor data or other sensor data is indicative of an aircraft takeoff or landing. Figure 36 is an example of this algorithm. In block 3602, the controller may receive sensor data. For example, the sensor data may be from an altitude sensor, or may be sensor data from other sensors. The sensor data may be altitude sensor data alone or may be a combination of altitude sensor data and other sensor data. For example, an altitude sensor may measure the ambient atmospheric pressure around the biometric tracking device and output the altitude sensor data as a voltage to the controller. For example, other sensor data may be acceleration data from an acceleration sensor, communication data from a communication interface or audio data from an audio sensor. The controller may then analyze the sensor data in block 3604 .

In block 3606, the controller may determine whether the sensor data indicates that the aircraft is taking off. The controller can make this determination in a number of ways. Some implementations of the biometric tracking device may use only altitude sensor data to make this determination, while other implementations may use altitude sensor data paired with other sensor data to make this determination. Still other implementations may make this determination via a combination of these different methods. For example, in some implementations, a biometric monitoring device may utilize multiple algorithms, with one algorithm using altitude sensor data only and another algorithm using altitude sensor data paired with acceleration data. In this embodiment, if any of the plurality of algorithms determines that the data indicates a takeoff, the controller may determine that the sensor data indicates a takeoff. Other embodiments may require a subset or all of the algorithms to indicate a takeoff before determining that data from various sensors indicates a takeoff. The algorithms described herein that may be used by the controller to determine whether the sensor data indicates that the aircraft is taking off may also be used by the controller to determine whether the sensor data indicates that the aircraft is landing. The algorithm used to determine whether the sensor data indicates that the aircraft is landing may have thresholds that change from positive to negative or negative to positive or to a different value entirely.

The controller may analyze the altitude sensor data and determine via a number of different algorithms that the altitude sensor data is indicative of a takeoff. For example, the controller may determine that the altitude sensor data indicates that the aircraft is taking off if the altitude sensor data indicates an altitude rate of change that exceeds an altitude rate of change threshold. The altitude rate threshold may be any rate of altitude change indicative of an aircraft takeoff or landing, for example an altitude change rate greater than 500 feet per minute. Other altitude change rates may also be used. For example, a commercial aircraft has an altitude change rate at takeoff of around 2,000 feet per minute, so an altitude change rate at or above this amount may be a reliable indicator of aircraft takeoff. An implementation of a biometric tracking device that will primarily be used to detect the takeoff of a commercial aircraft may have an altitude change rate threshold of 2,000 feet per minute or slightly less.

In certain embodiments, the biometric monitoring device may determine that the aircraft has taken off if the rate of altitude change exceeds an altitude rate of change threshold throughout the altitude change time period. For example, some implementations of a biometric tracking device may contain an algorithm that requires the altitude change rate to exceed an altitude change rate threshold for an altitude change time period of approximately 90 seconds before the processor determines that the altitude sensor data indicates that the aircraft is taking off. If the altitude sensor data indicates that the altitude change rate falls below the altitude change rate threshold at any time during the altitude change time period, the processor may determine that the altitude sensor data does not indicate that the aircraft is taking off. Other embodiments may have altitude change time periods greater than 90 seconds, and/or may have different altitude change rate thresholds. For example, an implementation may have an altitude change rate threshold of 1,000 feet per minute and an altitude change time period of at least 120 seconds.

An alternative algorithm for determining whether the altitude sensor data indicates an aircraft takeoff is to determine the total altitude change per unit of time. The controller may determine that the altitude sensor data indicates that the aircraft is taking off if the total altitude change per unit of time exceeds the threshold altitude change within the threshold time period. For example, a certain implementation may have a threshold altitude change value of 1,500 feet and a threshold time period of 180 seconds. If the altitude sensor data paired with the time data indicates an altitude change of 1,500 feet over a period of 180 seconds, the controller may determine that the altitude sensor indicates that the aircraft is taking off (or landing). This alternative algorithm can be used to determine an aircraft takeoff or landing in situations where the aircraft pauses at an altitude in the middle of a takeoff or landing cycle. Other implementations may have different threshold altitude change values and threshold time periods.

The controller may also utilize other sensor data as well as altitude sensor data. For example, the controller may receive acceleration data and analyze the acceleration data to determine whether the acceleration data indicates vibration consistent with vibration from one or more aircraft engines. In some embodiments, a biometric monitoring device may use motion sensors (e.g., single-axis or multi-axis accelerometers), such as a triaxial accelerometer that detects acceleration along three orthogonal linear axes, to detect biometric tracking devices Orientational magnetometers, gyroscopes to detect rotational motion of biometric monitoring devices, or any combination of the above. In other implementations, the controller may analyze the acceleration data to determine whether the acceleration data indicates horizontal and/or vertical acceleration consistent with the acceleration of the aircraft takeoff. Takeoff may be indicated by sustained horizontal or vertical acceleration (e.g., sustained acceleration greater than 10 ft/ ^sec2 for a period of 20 seconds or more), or from sustained acceleration such that the total velocity from sustained acceleration is within the time period The end of will be greater than the threshold speed. Any reasonable threshold speed consistent with the takeoff speed of the aircraft may be used, for example a speed of 150 mph.

In addition to acceleration data, the controller may analyze other sensor data, such as audio data to determine whether audio data is indicative of background noise consistent with the aircraft's engine noise, or to determine whether communication data is consistent with signals from, for example, the aircraft's WiFi. The communication data of the communication corresponding to the communication of the aircraft communication device.

If the controller is not sure that the altitude sensor data indicates that the aircraft is taking off, the controller may return to block 3602 . Otherwise, if the controller determines in block 3606 that the altitude sensor data indicates takeoff of an aircraft, the biometric tracking device may be placed in a mode associated with air travel in block 3608 . The modes associated with air travel may vary depending on the implementation. For example, in a certain implementation, a mode associated with air travel may be "airplane mode," in which the wireless communication circuitry of the biometric tracking device is placed in a standby or off state. The communication circuit can be a communication circuit using WiFi, Bluetooth, ANT, near field communication, ZigBee, IEEE802.11, IEEE802.15, infrared data association (IrDA) protocol and other communication protocols. In another embodiment, the mode associated with air travel may be a mode that places the biometric tracking device in a low power or sleep state. Another implementation of a biometric tracking device may include a stair climbing mode that tracks the number of flights of stairs climbed by the wearer, and such a biometric monitoring device may turn off the stair climbing mode when placed in a mode associated with air travel.

Alternatively, the biometric tracking device may also be placed into a mode associated with air travel via an external input. For example, a biometric tracking device may be placed in a mode associated with air travel upon receipt of an input signal via a wireless communication interface, eg, via receiving a signal from a personal computing or communication device such as a smartphone. In certain implementations, the smartphone may contain an application or functionality that will automatically send a signal to the biometric tracking device when the smartphone is placed in airplane mode. Thus, when the smartphone is placed in airplane mode, the biometric tracking device may also be placed in a mode associated with air travel, such as airplane mode. Similarly, when smartphone airplane mode is deactivated, the smartphone may send a signal to the biometric monitoring device to cause the biometric monitoring device to exit airplane mode (such implementations may require that the biometric monitoring device power up) the receiver to scan for this signal). Other implementations may place the biometric tracking device into a mode associated with air travel upon receiving input from a user of the biometric tracking device.

37 shows a flowchart detailing an example algorithm used by a biometric tracking device to determine whether to exit a mode associated with airplane travel. Blocks 3702 and 3704 are similar to blocks 3602 and 3604 in FIG. 36 . However, in block 3702 of FIG. 37, the biometric tracking device may receive sensor data while in a mode associated with airplane travel rather than in a normal operating mode. The sensor data in Figure 37 is similar to the sensor data described in Figure 36, and may be altitude sensor data or a combination of altitude sensor data and data from other sensors, as previously described. In block 3704, the controller analyzes the sensor data.

In block 3706, the controller determines whether the sensor data indicates the aircraft during landing. The controller may determine that the sensor data is indicative of the aircraft during landing using any of the approaches described above for block 3606, where perhaps the magnitude of certain values changes or reverses to reflect the fact that the aircraft is descending rather than ascending . For example, the total altitude gain threshold of 1,500 feet described in block 3606 may be a threshold for an altitude gain of 1,500 feet in block 3606, while in block 3706 the threshold may be an altitude loss of 1,500 feet. Other implementations may have different thresholds between the algorithms used to calculate whether the aircraft is taking off and whether the aircraft is landing. For example, an implementation may have a total altitude change threshold of a gain of 1,500 feet in magnitude to determine an aircraft takeoff, but a loss of 2,000 feet in magnitude to determine an aircraft landing. Using another example from block 3606, the altitude change time period for determining whether the aircraft is taking off may be a time period of 60 seconds or more, but for determining whether the aircraft is landing, the altitude change time period may be 180 seconds or time periods below 180. The difference between the altitude change time periods used to determine takeoff and landing can be attributed to how the aircraft is typically operated. The aircraft may take off and gain altitude at an initial rate higher than the rate at which the aircraft loses altitude when it lands. Therefore, the altitude change time period used to determine the takeoff may be smaller than the altitude change time period used to determine the landing.

Additionally, prior to determining in block 3706 that the sensor data indicates landing of the aircraft, the controller may further determine the altitude of the biometric tracking device prior to determining that the altitude sensor data indicates landing of the aircraft and causing the biometric tracking device to exit a mode associated with air travel A period in which the altitude changes a little bit, as detected by the altitude sensor. Alternatively, detecting a sustain period of small altitude changes following a period of altitude decrease may allow the controller to determine that the aircraft has actually landed. Commercial aircraft often spend a period of several minutes taxiing and landing on a flat runway after landing. This period of small altitude changes may correspond to the period of taxiing and may allow the biometric tracking device to determine that the aircraft has actually landed and is taxiing or landing on a flat runway before exiting a mode associated with air travel.

Various embodiments of the biometric tracking device may allow its controller to determine that the aircraft has landed and taxied via a number of different means. For example, the controller may determine that the aircraft has landed when it determines from the altitude sensor data and possibly other sensor data that the rate of change of altitude over a period of time does not exceed a threshold amount (after detecting an extended decrease in altitude). Embodiments of the biometric tracking device may make a determination that the aircraft has landed after the controller determines that the altitude sensor data indicates an altitude decrease or rate of altitude decrease that exceeds a threshold altitude decrease amount, and after the controller determines the altitude sensor data Glide while indicating an altitude change rate of less than 50 feet per minute for a period of 120 to 600 seconds (possibly 300 seconds), such as was previously described in this disclosure for block 3706 . Other embodiments may make a determination that the aircraft has landed, and after the controller determines that the altitude sensor data indicates that the total altitude change does not exceed a threshold amount (e.g., 100 feet or less) within a threshold time period (e.g., 600 seconds or less). 100 feet or less) (after a prolonged altitude decrease occurs). Additional embodiments may have different threshold amounts.

If the controller determines in block 3706 that the sensor data indicates landing of an aircraft, the controller may cause the biometric monitoring device to exit a mode associated with air travel in block 3708 . The controller may exit the communication circuit of the biometric tracking device by turning on the communication circuit of the biometric tracking device, by placing the biometric tracking device in a normal power state, by turning on the stair climbing mode, or by causing other changes in the operation of the biometric monitoring device. Travel-associated patterns. The controller in block 3708 may exit the mode associated with air travel by reversing any actions taken or commands issued by the controller in block 3608.

38 shows a flowchart detailing an example algorithm used by a biometric tracking device to determine whether to first place the biometric tracking device in a mode associated with air travel and then determine whether to exit the mode associated with air travel. The flowchart of FIG. 38 is a combination of the flowcharts of FIGS. 36 and 37 and details the complete process of entering and then exiting the biometric tracking system of a mode associated with air travel. Blocks 3802 through 3808 correspond to blocks 3602 through 3608 of FIG. 36 , while blocks 3810 through 3816 correspond to blocks 3702 through 3708 of FIG. 37 . The previous descriptions of the blocks in FIGS. 36 and 37 also apply to the corresponding blocks in FIG. 38 .

39A is an example graph showing equivalent air pressure inside a pressurized compartment of an example aircraft for takeoff compared to ambient air pressure at the same altitude. Figure 39A is a representative graph. In FIG. 39A, the x-axis represents time (in seconds) and the y-axis represents equivalent altitude pressure (in feet). Equivalent altitude pressure is the barometric pressure normally experienced at a reference altitude. For example, a barometric pressure of 29.92 inHg would correspond to the equivalent altitude pressure of 0 feet in FIG. 39A since 29.92 inHg is the mean barometric pressure at sea level. As another example, the equivalent altitude pressure of 8,000 feet in Figure 39A would correspond to the mean barometric pressure at 8,000 feet. Note that higher equivalent altitude pressures in Figure 39A correspond to lower barometric pressures because barometric pressure decreases as altitude increases. Certain embodiments of a biometric tracking device may utilize barometric pressure data in an algorithm without converting the barometric pressure data to an equivalent altitude.

Dashed line 3902 represents air pressure outside the aircraft containing the biometric tracking device. In FIG. 39A , the aircraft takes off, so as time increases, dashed line 3902 shows that the equivalent altitude pressure for altitude increases to correspond to the higher altitudes reached by the aircraft.

Solid line 3904 represents the air pressure experienced by the biometric tracking device inside the aircraft. The aircraft in Figure 39A has a pressurized compartment similar to that of most commercial airliners. The cabin in an airplane is pressurized at the equivalent of 7,800 feet during flight. Thus, in FIG. 39A , the increase in equivalent altitude pressure (represented by solid line 3904 ) experienced by the biometric tracking device inside the aircraft is similar to the increase in dotted line 3902 as the altitude increases initially. However, after the aircraft passed an altitude of 7,800 feet at 180 seconds, the cabin was pressurized to maintain the same barometric pressure. Thus, after 180 seconds, the aircraft's cabin is pressurized at an equivalent of 7,800 feet, and the equivalent altitude pressure experienced by the biometric tracking device remains constant at 7,800 feet, even though the actual altitude of the aircraft still increases.

Region 3916, represented by the cross-hatched box in FIG. 39A, represents the period during which the equivalent altitude pressure experienced by the biometric tracking device matches the equivalent altitude pressure outside the aircraft at takeoff, which is the period from 0 to 0 in FIG. 39A. seconds to a period of 180 seconds. Because the equivalent altitude pressure experienced by the biometric tracking device remains constant after 180 seconds in FIG. 39A , the biometric tracking device in FIG. The altitude sensor data is optimally performed when the takeoff of the aircraft is detected. Thus, attempting to detect an equivalent altitude that is too high with an altitude sensor may complicate detection of aircraft takeoff using the altitude sensor. Accordingly, certain embodiments of the biometric tracking device may limit the equivalent altitude pressure used by the controller to determine whether the aircraft is taking off or landing to an altitude that is less than the equivalent altitude at which common airliner aircraft are pressurized.

39B is an example graph illustrating altitude sensor data from various example situations. Figure 39B is a representative graph. In FIG. 39B, the x-axis represents time (in seconds) and the y-axis represents equivalent altitude pressure (in feet). The equivalent altitude pressure in Figure 39B is similar to the equivalent altitude pressure described in Figure 39A.

In Figure 39B, line 3906 represents a biometric tracking device in an aircraft taking off. Line 3908 represents a biometric tracking device in a landed aircraft. Line 3910 represents a biometric tracking device in an elevator of a skyscraper. Line 3912 represents a biometric tracking device in a car driving up a steep hill. Line 3914 represents a biometric tracking device on an extreme playground roller coaster.

In FIG. 39A , line 3906 is similar to line 3904 . However, Figure 39B only shows the aircraft of line 3906 before the aircraft reaches 6,000 feet, and thus the interior of the aircraft in line 3906 has not yet been pressurized. Line 3906 shows the linear rise in equivalent altitude pressure as the aircraft takes off.

Line 3908 represents the equivalent altitude pressure experienced by a biometric tracking device in a landed aircraft. The equivalent altitude pressure of line 3908 decreases from 6,000 feet at 0 seconds to 0 feet at 300 seconds. Thus, at zero 300 seconds, the aircraft of line 3908 may have landed at sea level. The absolute value of the rate of altitude decrease in line 3908 is the same as the absolute value of the rate of altitude increase in line 3906 .

Line 3910 represents the equivalent altitude pressure experienced by a biometric tracking device in an elevator in a skyscraper. The equivalent altitude pressure of line 3910 initially increases at a faster rate than line 3906 . This is due to the fact that high-speed elevators of skyscrapers often gain altitude at a faster rate than airplanes gain altitude during takeoff. However, because the height of the building is limited, at 50 seconds, the elevator reaches the top of the building, and line 3910 no longer gains the equivalent altitude after 50 seconds. Thus, while a controller with an algorithm that analyzes portions of the sensor data for a duration of less than 50 seconds may determine that the altitude sensor data for line 3910 from 0 seconds to 50 seconds is equivalent to the altitude sensor data for aircraft takeoff, with the analysis The controller of the algorithm for portions of sensor data greater than 50 seconds in duration may be able to determine that the altitude sensor data of line 3910 is not indicative of aircraft takeoff.

Line 3912 represents the equivalent altitude pressure experienced by a biometric tracking device in a car driving up a steep hill. Although the altitude sensor data of line 3912 determines a gain of 2,000 feet in altitude, the rate of altitude gain may be too slow for the controller of the biometric tracking device to determine that the altitude sensor data indicates that the aircraft is taking off. Alternatively, if the controller of the biometric tracking device analyzes only a limited duration of altitude sensor data, the controller may determine that the total value of the altitude gain for line 3912 may not exceed a threshold magnitude of altitude gain during the limited duration Instruct the aircraft to take off with the indicated altitude sensor data.

Line 3914 represents the equivalent altitude pressure experienced by a biometric tracking device on an extreme fairground roller coaster. The fairground roller coaster illustrated by line 3914 may be a fairground roller coaster with constant up and down motion, such as that of an extreme roller coaster. Although the rate of altitude gain and loss of line 3914 is equivalent to that of an aircraft takeoff or landing, the total magnitude of the altitude gain is much smaller than that of an aircraft takeoff or landing. Accordingly, a controller comprising an algorithm that utilizes an altitude gain or total altitude gain over a set duration in its determination of whether the altitude sensor data indicates that an aircraft is taking off or landing may be able to determine that the change in altitude of line 3912 does not indicate that the aircraft is taking off or landing. Takeoff or landing, this is a constant change in positive and negative values due to altitude change or an aggregate value due to altitude change that does not exceed a threshold amount.

40 shows a flowchart detailing an example algorithm used by a biometric tracking device to determine whether to notify a user of user inactivity for a period greater than the inactivity time period. The example algorithm illustrated in FIG. 40 may be an algorithm that monitors the user's movement and notifies the user when the controller of the biometric tracking device determines that the user has not moved beyond a threshold amount within a set period of time.

The example algorithm illustrated in FIG. 40 may be linked to the patterns associated with air travel described in FIGS. 36-38. Accordingly, the biometric tracking device may utilize an algorithm when placing the biometric tracking device in a mode associated with air travel. This algorithm may be useful when placing a biometric tracking device in a mode associated with air travel, since the user of a biometric tracking device on an airplane may be mostly stationary and in a seated position, allowing the blood to flow A buildup around his or her lower extremities. Having an algorithm that will remind the user to move periodically will help keep the user's blood circulating through his or her body, preventing potential injury from blood clotting and other potentially serious afflictions that can strike someone who sits for too long.

In block 4002, a controller of a biometric tracking system may receive data from a biometric sensor. The biometric sensor may be a sensor embedded in the biometric tracking device or a biometric sensor located on another device, where the biometric sensor data is communicated to the biometric tracking device via, for example, a WiFi or Bluetooth connection. A biometric sensor may be any sensor that can directly or indirectly detect a user's movement, such as a heart rate sensor, pedometer, acceleration sensor, light sensor, gyroscope, or any other sensor described elsewhere in this disclosure.

In block 4004, the controller analyzes the data from the biometric sensor according to an algorithm stored by the controller's memory.

In block 4006, the controller determines whether the wearer has moved beyond a threshold amount of movement within a first period of time. The controller can make this determination in a number of ways. For example, a biometric tracking device may include a biometric sensor that detects a user's heart rate, and if the user's heart rate has remained at a resting pulse rate for the duration of a first time period, the controller may determine that the user has has not moved within the threshold movement amount. Alternatively, the biometric tracking device may utilize a pedometer to detect the number of steps the user has taken, and if the detected number of steps does not exceed a threshold amount within the first period of time, the controller may determine that the user has not walked within the first period of time. Movement Threshold movement amount. Other embodiments of the biometric tracking device may utilize an acceleration sensor or a gyroscope to detect the user's movement to determine that the user has not moved a threshold amount of movement within the first period of time. The first time period may be any reasonable time period for tracking the movement of a user on an aircraft flight, such as a time period of 15 hours or less. The controller may also include different threshold amounts for detecting the user's movement, such as 100, 300, 500, or 1,000 steps for the threshold step count for implementations using a pedometer.

If the controller determines in block 4006 that the user has moved beyond the threshold amount of movement within the first period of time, the controller returns to block 4002 . If the controller determines in block 4006 that the user has not moved beyond the threshold amount of movement within the first time period, the controller continues to block 4008 .

In block 4008, the biometric tracking device notifies the user via the user interface that the user has not moved beyond the threshold amount of movement. The user interface can be of many different types including, for example, a graphical display, a vibration motor embedded within the biometric tracking device to generate tactile feedback, or a sound producing component such as a speaker or piezoelectric device. The user interface may notify the user via a number of different means including messages on a graphical display, tactile feedback such as a vibration or series of vibrations, a beep, multiple beeps, or a melodic tune.

In certain implementations, the memory of the biometric tracking device may include code to determine that the user is asleep, likely to be asleep, or likely to be asleep and to delay notifying the user accordingly. The controller of the biometric tracking device may determine that the user is asleep, likely to be asleep, or likely to be asleep via a number of different ways. For example, the biometric tracking device may include a light sensor, and the controller may analyze the light sensor data to determine that the light sensor data indicates light levels typically associated with sleeping environments, and thus determine that the user is asleep, likely asleep, or likely sleep. Alternatively, the controller may determine that the user is asleep, likely to be asleep, or likely to be asleep when the biometric tracking device may be manually placed in sleep tracking mode by the user. Additionally, the controller may determine that the user is asleep, likely to be asleep, or likely to be asleep when analysis of the biometric data reveals that the biometric data indicates or is likely to indicate that the user is asleep.

If the controller determines that the user is asleep or likely to be asleep, the controller may delay providing notification to the user that the user has not moved beyond the threshold amount of movement for a later period of time, such as after the controller determines that the wearer is not asleep or is likely to be asleep. Time period when not asleep.

The concepts and examples discussed above with respect to altimeter recalibration, altimeter-assisted pose recognition, and altimeter-assisted aircraft mode may be implemented in a device (eg, a biometric monitoring device) using computer-executable instructions, eg, stored in a computer-readable memory. The instructions may be executed by one or more processors communicatively coupled to the memory and also communicatively coupled to the pressure sensor or barometric altimeter. Such instructions may be provided using any of a variety of programming languages, including but not limited to C++, Java, iOS, Android, and the like. In some implementations, some or all of the instructions may be hardcoded into an application specific integrated circuit (ASIC). The term "control logic" may be used herein with respect to altimeter recalibration, altimeter-assisted attitude recognition, and altimeter-assisted aircraft mode techniques and systems discussed herein to refer to the Any combination of processor/memory/software components.

It should be understood that the concepts and examples discussed above with respect to altimeter recalibration, altimeter-assisted posture recognition, and altimeter-assisted aircraft modes may be implemented in a device or system, such as a biometric monitoring or tracking device or other electronic device; such as a computer, processor or a circuit-assisted method; or computer-executable instructions such as transmitted to a device or stored on a non-transitory machine-readable medium.

There are many concepts and embodiments described and illustrated herein. Although certain embodiments, features, attributes and advantages have been described and illustrated herein, it is to be understood that many other and different and/or similar embodiments, features, attributes and advantages are apparent from the description and illustration. Accordingly, the above embodiments are provided as examples only. It is not intended to be exhaustive or to limit the invention to the precise forms, techniques, materials and/or configurations disclosed. Many modifications and variations are possible in accordance with the invention. It is to be understood that other embodiments may be utilized and operational changes may be made without departing from the scope of the present invention. Thus, the scope of the present invention is not limited to the foregoing description, since the description of the above embodiments has been presented for purposes of illustration and description.

Importantly, the present invention is neither limited to any single aspect or embodiment, nor to any combination and/or permutation of such aspects and/or embodiments. Furthermore, each of the aspects of the invention and/or embodiments thereof may be used alone or in combination with one or more of the other aspects and/or embodiments of the invention. For the sake of brevity, many of those permutations and combinations will not be individually discussed and/or illustrated herein.

Claims (24)

1. a kind of wearable biometric tracks of device, the wearable biometric tracks of device includes：

Pressure sensor, the ambient air air pressure for being configured to detect around the wearable biometric tracks of device and Output represents the data of height above sea level, wherein representing that the data of height above sea level are at least partially based on by the pressure sensor The ambient air air pressure of measurement；

Motion sensor, the motion sensor be configured to detect the wearable biometric tracks of device movement and The corresponding exercise data of output；And

Control logic, wherein the pressure sensor, the motion sensor and the control logic connect by correspondence, and The control logic is configured to：

A) data for representing height above sea level are received,

B) exercise data is received,

C) determine to represent when the data of height above sea level and the exercise data are moved with multiple arms in profile in combination The first arm movable pulley it is wide related and

D) stored in response to described determine in (c) and the wide associated data of the first arm movable pulley.

2. wearable biometric tracks of device according to claim 1, wherein the wearable biometric tracking dress It puts and is configured to be worn in the position for the group being made of the following：The arm of people, the forearm of people, the hand of people and people Finger.

3. wearable biometric tracks of device according to claim 1, wherein the pressure sensor is pressure altitude Meter.

4. wearable biometric tracks of device according to claim 1, wherein the motion sensor include be selected from by One or more sensors of the group of the following composition：Accelerometer, gyroscope, magnetometer, piezoelectric transducer, electromagnetic tracker And the imaging sensor based on camera.

5. wearable biometric tracks of device according to claim 1, wherein the multiple arm movement profile includes It is similar to one or more arms movement profile for representing one or more movable arm motions, each activity is selected from by the following The group of composition：Running, walking, resistance exercise exercise, chin-up, push-up, sit-ups, are skipped rope and are had at elliptic exercise Oxygen dancing.

6. wearable biometric tracks of device according to claim 1, wherein the control logic is further configured With：

Determine with the first arm movable pulley associated first arm motion outline of exterior feature and

Determine that the first arm motion outline refers to motion outline with the first arm movable pulley exterior feature associated first Between the degree of deviation.

7. wearable biometric tracks of device according to claim 6, wherein the first-hand arm movement profile and institute It is both associated one or more with the common action for being selected from the group being made of the following with reference to motion outline to state first A posture is associated：Baseball bat is waved, softball bat is waved, waves tennis racket, wave racket, wave English wall ball racket, wave Dance ＆ art formula wall ball racket waves Golf club, waves golf iron, wave golf wedge, wave golf Ball push rod, wave golf cut push rod, wave golf mixed pole, perform resistance exercise take exercise, perform Wushu, hold Row gymnastic, perform yoga exercise, bat in billiard movement, take stroke, play bowling, with riflery, use pistol Shooting, with lancination, wave knife and with knife lattice gear.

8. wearable biometric tracks of device according to claim 6, further include by correspondence with the control The interface arrangement of logical connection processed, the control logic be further configured with：

H) determine the degree of deviation when be more than deviation first threshold amount and

I) it is more than described determine of the first threshold amount in response to the degree of deviation and causes described in the interface arrangement offer The degree of deviation is more than the instruction of the first threshold amount, wherein：

A) interface arrangement includes one or more selected from the group being made of the following：Display, one or more lamps, Vibrating motor and audio devices；And

B) instruction includes one or more selected from the group being made of the following：Numerical value score, vibration, sound, figure What the message and the warning wearer that shape, icon, animation, the message of instruction fatigue, caution wear person are slowed down were had a rest Message.

9. wearable biometric tracks of device according to claim 1, wherein the first-hand arm movement profile and resistance Power training type activity of crooking one's arm is associated, and the exercise data and represent height above sea level the data include an example or During two or more substantial continuous examples, the exercise data and the data and described first for representing height above sea level Arm movement profile is related, wherein：

Exercise data instruction wear the forearm of the wearable biometric tracks of device during cycle first time from It tilts down orientation and is converted to the orientation that is inclined upwardly,

The exercise data instruction wears the forearm of the wearable biometric tracks of device in the week first time Be converted to during second time period after phase from the orientation that is inclined upwardly tilt down orientation and

Represent that the data of height above sea level indicate the wearable biometric tracks of device in phase cycle first time Between height above sea level of the experience between 1ft and 2ft increase.

10. wearable biometric tracks of device according to claim 9, wherein the exercise data and expression height above sea level The data of degree are wide related to the first arm movable pulley.

11. wearable biometric tracks of device according to claim 9, wherein the number of the example is selected from by following The group of each composition：I) 1 to 5 examples, ii) 5 to 8 examples, iii) 8 to 10 examples, iv) 10 to 12 examples, v) 12 to 15 examples and vi) 15 to 20 examples.

12. wearable biometric tracks of device according to claim 11, wherein the number (i) of the example is arrived (vi) whether scope is the wearer based on the instruction wearable biometric tracks of device for selected from respectively by following The purpose of the group of each composition just carries out the data of resistance exercise and selects：I) gain in strength, II) substantially equally increase Strength amount and muscle mass, III) enhance muscle mass and emphasize some strength, IV) enhance muscle mass and emphasize some endurance, V) increase Strong endurance simultaneously emphasizes some muscle and VI) mainly enhance endurance.

13. wearable biometric tracks of device according to claim 1, wherein the first-hand arm movement profile and resistance Power trains hard act type activity to be associated, and the exercise data and represent height above sea level the data include an example or During two or more substantial continuous examples, the exercise data and the data and described first for representing height above sea level Arm movement profile is related, wherein：

Represent that the data of height above sea level indicate that the wearable biometric tracks of device passes through during cycle first time The increase of the height above sea level between 1.5ft and 2ft is gone through,

Represent that the data of height above sea level indicate that the wearable biometric tracks of device passes through during second time period Go through the height above sea level between 1.5ft and 2ft reduction and

The forearm of the wearable biometric tracks of device substantially all described first is worn in exercise data instruction Substantial orthogonality is maintained during time cycle and the second time period and is downwardly oriented.

14. wearable biometric tracks of device according to claim 1, wherein the first-hand arm movement profile and resistance Power trains deep-knee-bend or sleeping push type activity to be associated, and in the exercise data and represents that the data of height above sea level include one When example or two or more substantial continuous examples, the exercise data and the data and the institute for representing height above sea level It is wide related to state the first arm movable pulley, wherein：

Represent that the data of height above sea level indicate that the wearable biometric tracks of device passes through during cycle first time The increase of the height above sea level between 0.5ft and 2ft is gone through,

Represent that the data of height above sea level indicate that the wearable biometric tracks of device passes through during second time period Go through the height above sea level between 0.5ft and 2ft reduction and

The forearm of the wearable biometric tracks of device substantially all described first is worn in exercise data instruction Substantial orthogonality and upwardly-directed is maintained during time cycle and the second time period.

15. wearable biometric tracks of device according to claim 1, wherein the first-hand arm movement profile and resistance Power trains front raise or side raise type activity to be associated, and in the exercise data and represents that the data of height above sea level include When one example or two or more substantial continuous examples, the exercise data and the data for representing height above sea level It is wide related to the first arm movable pulley, wherein：

Represent that the data of height above sea level indicate that the wearable biometric tracks of device passes through during cycle first time The increase of the height above sea level between 1ft and 2ft is gone through,

Represent that the data of height above sea level indicate that the wearable biometric tracks of device passes through during second time period The reduction of the height above sea level between 1ft and 2ft is gone through,

Exercise data instruction wears the forearm of the wearable biometric tracks of device from cycle first time The substantial orthogonality at beginning and be downwardly oriented the substantial horizontal orientation being converted at the end in cycle first time and

The forearm that the wearable biometric tracks of device is worn in the exercise data instruction is all from second time The substantial horizontal orientation at the beginning of phase is converted to the substantial orthogonality at the end of the second time period and determines downwards To.

16. wearable biometric tracks of device according to claim 1 is instructed wherein the first arm movable pulley is wide with resistance Practicing good morning type activity is associated, and in the exercise data and represents that the data of height above sea level include an example or two Or during more than two substantially continuous example, the exercise data and the data of height above sea level and first arm are represented Mobile profile is related, wherein：

Represent that the data of height above sea level indicate that the wearable biometric tracks of device passes through during cycle first time The increase of the height above sea level between 1.5ft. and 2ft. is gone through,

Represent that the data of height above sea level indicate that the wearable biometric tracks of device passes through during second time period The reduction of the height above sea level between 1.5ft. and 2ft. is gone through,

Exercise data instruction wears the forearm of the wearable biometric tracks of device from cycle first time The substantial horizontal orientation at beginning be converted to substantial orthogonality at the end in cycle first time and it is upwardly-directed and

The forearm that the wearable biometric tracks of device is worn in the exercise data instruction is all from second time The substantial orthogonality at the beginning of phase and the upwardly-directed substantial level being converted at the end of the second time period are determined To.

17. wearable biometric tracks of device according to claim 1, wherein the first-hand arm movement profile with it is ellipse Circular knitting machine activity is associated, and the exercise data and represents the data and described the of height above sea level when there is following situation One arm movable pulley is wide related：

Represent that the data of height above sea level show the behavior of cycling during cycle first time, wherein during each cycling Small height above sea level variation matches with the height above sea level variation undergone during normal use by the handle of elliptical machine,

The exercise data shows the behavior of cycling, and substantially has with representing the data of height above sea level described first The identical frequency of the cycling behavior during time cycle and

The exercise data includes acceleration information, and acceleration information instruction is less than in other cases by the control The acceleration magnitude during cycle first time for the acceleration magnitude that logic uses, to determine whether that finger should be attributed to Show that the foot for the people's participation people for wearing the wearable biometric tracks of device leaves the normal walking on ground or runs to live Dynamic acceleration information, and it is incremented by pedometer.

18. wearable biometric tracks of device according to claim 1 further comprises with the control logic with logical The display that letter mode connects, wherein：

I) the first arm movable pulley is wide associated with seeing wrist-watch activity；

II) data of the exercise data and expression height above sea level are moved when there is following situation with first arm Profile is related：

Represent that the data of height above sea level indicate that the wearable biometric tracks of device passes through during cycle first time Go through the about increase of the height above sea level of 1ft. and

The exercise data instruction wears the forearm of the wearable biometric tracks of device in phase cycle first time Between from substantial orthogonality and be downwardly oriented and be converted to substantial horizontal orientation；And

III) control logic is further configured to respond to the exercise data described in (c) and represents the institute of height above sea level It states data and sees that wrist-watch activity the first movement profile is relevant and determine to cause the display that clock related data is presented.

19. wearable biometric tracks of device according to claim 18, wherein in addition being indicated in the exercise data Wear second time week of the forearm in followed by described cycle first time of the wearable biometric tracks of device When the substantial horizontal orientation is maintained in the phase, the exercise data and the data and described first for representing height above sea level Arm movement profile is related, and the second time period has the non-zero duration.

20. wearable biometric tracks of device according to claim 1, wherein the multiple arm movement profile includes It is similar to the arm movement profile of user's rest, and the control logic is further configured to：

P) duration that the first arm movable pulley exterior feature is substantially similar to user's rest arm movement profile is tracked； And

Q) determine that the first arm movable pulley exterior feature is substantially similar to the described lasting of user's rest arm movement profile Time is more than rest duration threshold.

21. a kind of wearable biometric tracks of device, including：

Pressure sensor, the height above sea level and output for being configured to detect the wearable biometric tracks of device represent The corresponding data of height above sea level；And

Motion sensor, the motion sensor be configured to detect the wearable biometric tracks of device movement and The corresponding exercise data of output；And

Control logic, wherein biometric monitoring arrangement, the pressure sensor and the control logic connect by correspondence, And the control logic is configured to：

A) determine to represent when the data of height above sea level indicate the wearable biometric tracks of device at the first time During cycle not yet experience more than the first height above sea level change rate threshold value height above sea level change rate and

B) filled at least responsive to the data instruction wearable biometric tracking for determining to represent height above sea level in (a) Put the height above sea level variation not yet undergone during cycle first time more than the first height above sea level change rate threshold value Rate, during the second time period after cycle first time by the wearable biometric tracks of device be placed in compared with Low-power consuming state.

22. wearable biometric tracks of device according to claim 21, wherein the control logic is further through matching somebody with somebody Put with：

C) determine when the exercise data indicates that the wearable biometric tracks of device is close in week first time Undergo before phase crash and

D) perform b) as follows：It can wear described in the data instruction of height above sea level except determining to represent in (a) Wearing biometric tracks of device, not yet experience is more than the first height above sea level change rate threshold during cycle first time Outside the height above sea level change rate of value, it is additionally in response to determine that the exercise data indicates the wearable biometric in (c) Tracks of device be close in before cycle first time undergo crash and by the wearable biometric tracks of device It is placed in lower power consumption state.

23. wearable biometric tracks of device according to claim 21, wherein the control logic is further through matching somebody with somebody Put with：

C) determine when the data for representing height above sea level indicate the wearable biometric tracks of device described second During time cycle experience more than the second height above sea level change rate threshold value height above sea level change rate and

D) in response to determining to represent that the data of height above sea level indicate that the wearable biometric tracks of device exists in (c) Experience causes institute more than the height above sea level change rate of the second height above sea level change rate threshold value during the second time period It states wearable biometric tracks of device and exits the lower power consumption state.

24. wearable biometric tracks of device according to claim 23, wherein the control logic is further through matching somebody with somebody Put with：

E) in response to determining to represent that the data of height above sea level indicate that the wearable biometric tracks of device exists in (c) Experience causes institute more than the height above sea level change rate of the second height above sea level change rate threshold value during the second time period The display for stating wearable biometric tracks of device is converted to on-state and display message from closing or dormant state.