CN108742559B

CN108742559B - Wearable heart rate monitor

Info

Publication number: CN108742559B
Application number: CN201810586889.4A
Authority: CN
Inventors: 谢尔顿·杰骄·袁; 洪廷旭
Original assignee: Fitbit LLC
Current assignee: Feibit Co ltd
Priority date: 2013-06-03
Filing date: 2014-06-03
Publication date: 2022-01-07
Anticipated expiration: 2034-06-03
Also published as: CN104207755B; CN108742559A; CN104207755A

Abstract

Some embodiments provide a wearable fitness monitoring device that includes a motion sensor and a photoplethysmographic (PPG) sensor. The PPG sensor comprises: (i) a periodic light source; (ii) a photodetector; and (iii) circuitry to determine a heart rate of the user from the output of the light detector. Some embodiments provide methods for operating a heart rate monitor of a wearable fitness monitoring device to measure one or more characteristics of a heartbeat waveform. Some embodiments provide methods for operating the wearable fitness monitoring device in a low power state when the device is determined not to be worn by a user. Some embodiments provide methods for operating the wearable fitness monitoring device in a normal power state when the device is determined to be worn by a user.

Description

Wearable heart rate monitor

The application is a divisional application of an invention patent application with the application date of 2014, 6, and 3, and the application number of CN201410243169.X, and the invention name of the invention is 'wearable heart rate monitor'.

Technical Field

Background

Recent consumer concerns about personal health have led to the provision of a variety of personal health monitoring devices on the market. Until recently, such devices tended to be complex to use and were typically designed for one activity, such as a bicycle travel computer.

Recent advances in 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 be provided in extremely small sizes that were previously impractical. For example, a Fitbit Ultra is a biometric monitoring device that is about 2 inches long, 0.75 inches wide, and 0.5 inches deep; with a pixilated display, battery, sensors, wireless communication capabilities, power and interface buttons, packaged within this small volume, and an integrated clip for attaching the device to a pocket or other portion of a garment.

Methods and devices are provided for activating an HR monitor based on user motion and skin proximity in an energy efficient manner. The present invention also provides a method of operating the LEDs and light detector of a heart rate monitor to obtain an accurate reading of heart rate tailored for different user characteristics such as skin tone.

Disclosure of Invention

The 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 become apparent from the description, the drawings, and the claims. Note that the relative dimensions of the following figures may not be drawn to scale unless explicitly indicated as scaled figures.

Some embodiments of the present invention provide a method of operating a heart rate monitor of a wearable fitness monitoring device having a plurality of sensors including the heart rate monitor. The method involves: (a) operating the heart rate monitor in a first mode while also operating in a second mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin, wherein the first mode is configured to determine one or more characteristics of a user's heartbeat waveform when the wearable fitness monitoring device is in close proximity to the user; (b) from information collected in the second mode, determining that the heart rate monitor is not proximate to the user's skin; and (c) in response to determining that the heart rate monitor is not proximate to the user's skin, ending operation of the heart rate monitor in the first mode. In some embodiments, the heart rate monitor is an optical heart rate monitor. In some embodiments, the heart rate monitor includes a photoplethysmography sensor. In some embodiments, the one or more characteristics of the user's heartbeat waveform include the user's heart rate.

In some embodiments, operation (a) above involves periodically operating the heart rate monitor in the second mode while continuously operating the heart rate monitor in the first mode. In some embodiments, operation (a) involves operating the heart rate monitor in the second mode for no more than about 50% of the time of occurrence.

In some embodiments, operating the heart rate monitor in the second mode involves pulsing a light source in the heart rate monitor at a second mode frequency and detecting light from the light source at the second mode frequency; and operating the heart rate monitor in the first mode involves pulsing a light source in the heart rate monitor at a first mode frequency and detecting light from the light source at the first mode frequency. In some embodiments, the second mode frequency is greater than the first mode frequency.

In some embodiments, operating the heart rate monitor in the second mode involves pulsing a light source in the heart monitor at a second frequency; detecting light from the light source at the second frequency; and determining whether the light detected at the second frequency has an intensity and/or pattern indicating that the light from the light source has interacted with the user's skin. In some embodiments, said pulsing said light in said second mode source involves emitting a series of light pulses, some with variable intensity and others with constant intensity.

In some embodiments, operating the heart rate monitor in the second mode involves: emitting a series of light pulses with variable intensity; and determining whether the detected light corresponding to the train of light pulses has a variable response corresponding to the variable intensity of the light pulses.

In some embodiments, the disclosed method for operating the heart rate monitor further involves operating the heart rate monitor in a skin characterization mode configured to determine at least one setting for operating the heart rate monitor in the first mode. Operating in the skin characterization mode involves: (i) pulsing a light source in the heart monitor by emitting a series of light pulses having variable intensity; (ii) detecting an intensity, intensity variation and/or intensity pattern of light after the pulsating light has interacted with the user's skin; and (iii) determining a response characteristic of the user's skin from the intensity, intensity change and/or intensity pattern detected in (ii). In some embodiments, the method further involves adjusting a gain and/or a luminous intensity of the heart rate monitor using the response characteristic of the user's skin for operating in the first mode. In some embodiments, the response characteristic is dependent on an opacity of the user's skin. In some embodiments, the emitted train of light pulses from (i) having variable intensity is used in the second mode and in the skin characterization mode. In some embodiments, operating the heart rate monitor in the skin characterization mode involves periodically operating the heart rate monitor in the skin characterization mode while continuously operating the heart rate monitor in the first mode.

In some embodiments, the plurality of sensors of the device includes a motion detection sensor. In some embodiments, the motion detection sensor includes an accelerometer, a magnetometer, an altimeter, a GPS detector, or a combination of any of these sensors. In some embodiments, the disclosed methods for operating a heart rate monitor involve: determining from information output by the motion detection sensor that the wearable fitness monitoring device has been quiet for at least a defined period; and in response to detecting that the wearable fitness monitoring device has been quiet for at least a defined period, performing operation (c) described above.

In some embodiments, the method for operating a heart rate monitor further involves, while not operating in the first mode prior to (a): (i) detecting motion of the wearable fitness monitoring device using a motion detection sensor and/or detecting proximity of the heart rate monitor to the user's skin by operating the heart rate monitor in a third mode; and (ii) initiate operation of the first mode of the heart rate monitor when the wearable fitness monitoring device is determined to be in close proximity to the user.

The above embodiments relate to detecting the device in an unworn state, while the subsequent embodiments relate to detecting the device in a worn state.

Some embodiments provide a method of operating a heart rate monitor of a wearable fitness monitoring device having a plurality of sensors including the heart rate monitor and a motion detection sensor. The method involves: (a) detecting motion of the wearable fitness monitoring device using the motion detection sensor; (b) in response to detecting the motion in (a), operating the heart rate monitor in a wear detection mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin; and (c) upon determining that the wearable fitness monitoring device is proximate to the user's skin via the wear detection mode, operating the heart rate monitor in a first mode configured to determine one or more characteristics of the user's heartbeat waveform. In some embodiments, the wear detection mode occurs no more than about 50%. In some embodiments, when the heart rate monitor is not operating or operating in a low power mode, (a) is performed. In some embodiments, operation (a) involves detecting an output from the motion detection sensor, wherein the output exceeds a defined threshold.

In some embodiments, the method further comprises, prior to (a): (i) operating the heart rate monitor in the first mode while also operating in a second mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin; (ii) from information collected in the second mode, determining that the heart rate monitor is not proximate to the user's skin; and (iii) in response to determining that the heart rate monitor is not proximate to the user's skin, ending operation of the heart rate monitor in the first mode.

In some embodiments, operating the heart rate monitor in the wear detection mode involves pulsing a light source in the heart rate monitor at a wear detection mode frequency, and detecting light from the light source at the wear detection mode frequency. In some embodiments, operating the heart rate monitor in the first mode involves pulsing the light source in the heart rate monitor at a first mode frequency and detecting light from the light source at the first mode frequency. In some embodiments, the wear detection mode frequency is greater than the first mode frequency.

In some embodiments, operating the heart rate monitor in the wear detection mode involves: emitting pulses of light from a light source in the heart rate monitor having a second frequency and/or phase; detecting light from the light source at the second frequency and/or phase; and determining whether the light detected at the second frequency and/or phase has an intensity and/or pattern indicating that the light from the light source has interacted with the user's skin. In some embodiments, emitting a pulse of light from the light source involves emitting a series of pulses of light having a variable intensity. In some embodiments, a first one of the series of light pulses has an intensity that is at least 5 times greater than a second one of the series of light pulses. In some embodiments, the train of light pulses includes a first set of pulses having an intensity that provides a variable response when interacting with light skin, and the train of light pulses also includes a second set of pulses having an intensity that provides a variable response when interacting with dark skin. In some embodiments, operating the heart rate monitor in the wear detection mode involves: emitting a series of light pulses with variable intensity; and determining whether the detected light corresponding to the train of light pulses has a variable response corresponding to the variable intensity of the light pulses.

In some embodiments, the method of operating a heart rate monitor of a wearable fitness monitoring device further involves: determining from information output by the motion detection sensor that the wearable fitness monitoring device has been quiet for at least a defined period; and in response to detecting that the wearable fitness monitoring device has been quiet for at least a defined period, powering down the device.

Some embodiments of the present invention provide methods for determining skin characteristics of a user wearing a fitness monitoring device. Some embodiments provide a method for adjusting the operation of a heart rate monitor of the fitness monitoring device. Some embodiments provide a method of operating a heart rate monitor of a wearable fitness monitoring device to adjust at least one setting of operating the heart rate monitor. The operation of the heart rate monitor involves: (a) pulsing a light source in the heart monitor in a skin characterization mode by emitting a series of light pulses, at least some of the light pulses having variable intensities relative to each other; (b) detecting a change in intensity of light from the light pulses emitted in the skin characterization mode after the light has interacted with the user's skin; (c) determining a response characteristic of the user's skin from the intensity change of the light detected in (b); and (d) adjusting a gain and/or light emission intensity of the heart rate monitor operating in a first mode for detecting one or more characteristics of a heart beat waveform of the user using the response characteristic of the user's skin.

In some embodiments of the method, the response characteristic is dependent on an opacity of the skin of the user. In some embodiments, operating in the first mode and operating in the skin characterization mode are performed simultaneously. In some embodiments, simultaneously operating in the first mode and operating in the skin characterization mode involves periodically determining a response characteristic of the user's skin while continuously operating in the first mode. In some embodiments, the skin characterization pattern occurs at a time of no more than about 50%.

In some embodiments, operating in the first mode involves pulsing the light source in the heart rate monitor at a first frequency, and detecting light from the light source at the first frequency after the light has interacted with the user's skin. In some embodiments, operating in the skin characterization mode involves pulsing the light source in the heart rate monitor at a second frequency, and detecting light from the light source at the second frequency. In some embodiments, the second frequency is greater than the first frequency.

In some embodiments, the operation of determining the response characteristic of the user's skin involves determining an intensity level and/or pattern of two or more light pulses detected at the second frequency. In some embodiments, the train of light pulses includes some light pulses having variable intensity and other light pulses having constant intensity.

In some embodiments, the train of light pulses comprises at least two light pulses having variable intensities. In some embodiments, the train of light pulses comprises at least four light pulses having variable intensities. In some embodiments, the train of light pulses emitted in the skin characterization pattern includes at least two light pulses having variable intensities. The operation of determining a response characteristic of the user's skin in (c) involves determining a function or characteristic of the change in intensity of light from the light pulses detected in (b). A function or characteristic of the intensity variation is the response characteristic of the user's skin to adjust gain and/or light emission intensity of the heart rate monitor operating in a first mode.

In some embodiments, determining the function or characteristic involves determining a slope of a change in intensity of light from the light pulses detected in (b).

In some embodiments, adjusting the gain and/or light emission intensity of the heart rate monitor for operation in the first mode involves reducing the emission intensity.

In some embodiments, the wearable fitness monitoring device includes a motion detection sensor. In some embodiments, the motion detection sensor includes an accelerometer, a magnetometer, an altimeter, a GPS detector, a gyroscope, or a combination of any of these sensors.

Some embodiments provide a method of operating a heart rate monitor of a wearable fitness monitoring device, the heart rate monitor having a light source and a light detector. The method involves adjusting operation of the heart rate monitor based on skin characteristics of a user to improve performance. The method involves: (a) the heart rate monitor is operated in a first mode while also operating in a skin characterization mode for determining characteristics of the skin of the user. The first mode is configured to determine one or more characteristics of a heartbeat waveform of the user. The skin characterization mode operation involves generating data points representing emission intensity from the light source and corresponding detection levels from the light detector. The method further involves: (b) fitting the data points of the skin characterization pattern to a mathematical relationship relating light source emission intensity to light detector detection level; (c) using the mathematical relationship to determine a light source emission intensity setting that provides a predetermined light detector detection level identified as providing good heart rate monitor performance; and (d) adjusting the light source emission intensity to the setting determined in (c) for operation in the first mode.

In some embodiments, the mathematical relationship is linear. In some embodiments, the predetermined photodetector detection level was previously determined to have a high signal-to-noise ratio. In some embodiments, the method further involves, prior to (b), determining a slope of a line fitting the data points representing emission intensity from the light source and corresponding detection levels from the light detector; and setting the light source emission intensity for operation in the first mode based on the determined slope and preset values of emission intensity level.

In some embodiments, the first mode is performed concurrently with the skin characterization mode. In some embodiments, simultaneously operating in the first mode and operating in the skin characterization mode involves periodically determining a response characteristic of the user's skin while continuously operating in the first mode. In some embodiments, the skin characterization pattern occurs at a time of no more than about 50%.

In some embodiments, operating in the first mode involves pulsing the light source in the heart rate monitor at a first frequency, and detecting light from the light source at the first frequency after the light has interacted with the user's skin. In some embodiments, operating in the skin characterization mode involves pulsing the light source in the heart rate monitor at a second frequency, and detecting light from the light source at the second frequency after the light has interacted with the skin of the user. In some embodiments, the second frequency is greater than the first frequency.

In some embodiments, operating in the skin characterization mode further involves determining an intensity level and/or pattern of two or more light pulses detected at the second frequency after the light has interacted with the user's skin.

In some embodiments, operating the heart rate monitor in the skin characterization mode involves emitting a series of light pulses, and wherein some of the light pulses have variable intensities and other light pulses have constant intensities compared to each other.

In some embodiments, operating the heart rate monitor in the skin characterization mode involves emitting a series of light pulses. In some embodiments, at least two of the light pulses have a variable intensity compared to each other. In some embodiments, at least four of the light pulses have variable intensities compared to each other.

Some embodiments of the present invention provide a wearable fitness monitoring device having a motion sensor configured to provide an output corresponding to motion of a user wearing the fitness monitoring device and a photoplethysmography (PPG) sensor. The PPG sensor comprises: (i) a periodic light source; (ii) a light detector positioned to receive periodic light emitted by the periodic light source after interaction with a user's skin; and (iii) circuitry to determine a heart rate of the user from the output of the light detector. In some embodiments, the periodic light source includes two periodic light sources straddling the light detector. In some embodiments, the photoplethysmography sensor further includes a housing having a recess in which the light detector is disposed. In some embodiments, the housing of the photoplethysmographic sensor further includes a second recess in which the periodic light source is disposed. In some embodiments, the housing of the photoplethysmographic sensor protrudes above a bottom surface of the wearable fitness monitoring device by at least about 1mm and is configured to press against the user's skin when worn.

In some embodiments, the photoplethysmographic sensor further includes a spring configured to resist compression when the protruding housing is pressed against the user's skin. In some embodiments, the photoplethysmographic sensor further includes an IML film over the light detector and the periodic light source. In some embodiments, wherein the periodic light source of the PPG sensor is an LED.

Some embodiments of the invention provide a wearable fitness monitoring device having a motion sensor, a PPG sensor, and control logic. The PPG sensor comprises: (i) a periodic light source; (ii) a light detector positioned to receive periodic light emitted by the periodic light source after interaction with a user's skin; and (iii) circuitry to determine a heart rate of the user from the output of the light detector. The control logic is configured to: (a) operating the heart rate monitor in a first mode while also operating in a second mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin, wherein the first mode is configured to determine one or more characteristics of a user's heartbeat waveform when the wearable fitness monitoring device is in close proximity to the user; (b) from information collected in the second mode, determining that the heart rate monitor is not proximate to the user's skin; and (c) in response to determining that the heart rate monitor is not proximate to the user's skin, ending operation of the heart rate monitor in the first mode. In some embodiments, operating the heart rate monitor in the second mode involves pulsing a light source in the heart rate monitor at a second mode frequency, and detecting light from the light source at the second mode frequency. In some embodiments, operating the heart rate monitor in the first mode involves pulsing the light source in the heart rate monitor at a first mode frequency and detecting light from the light source at the first mode frequency.

In some embodiments, the control logic of the wearable fitness monitoring device is configured to: (a) detecting motion of the wearable fitness monitoring device using the motion detection sensor; (b) in response to detecting the motion in (a), operating the heart rate monitor in a wear detection mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin; and (c) upon determining that the wearable fitness monitoring device is proximate to the user's skin via the wear detection mode, operating the heart rate monitor in a first mode configured to determine one or more characteristics of the user's heartbeat waveform.

In other embodiments, the control logic of the wearable fitness monitoring device is configured to: (a) pulsing a light source in the heart monitor in a skin characterization mode by emitting a series of light pulses, at least some of the light pulses having variable intensities relative to each other; (b) detecting a change in intensity of light from the light pulses emitted in the skin characterization mode after the light has interacted with the user's skin; (c) determining a response characteristic of the user's skin from the intensity change of the light detected in (b); and (d) adjusting a gain and/or light emission intensity of the heart rate monitor operating in a first mode for detecting one or more characteristics of a heart beat waveform of the user using the response characteristic of the user's skin.

In further embodiments, the wearable fitness monitoring device control logic is configured to: (a) the heart rate monitor is operated in a first mode while also operating in a skin characterization mode for determining characteristics of the user's skin. The first mode is configured to determine one or more characteristics of a heartbeat waveform of the user, and the skin characterization mode involves generating a signal representative of an emission intensity from the light source and a corresponding detection level from the light detector. The control logic is further configured to: (b) fitting the data points of the skin characterization pattern to a mathematical relationship relating light source emission intensity to light detector detection level; (c) using the mathematical relationship to determine a light source emission intensity setting that provides a predetermined light detector detection level identified as providing good heart rate monitor performance; and (d) adjusting the light source emission intensity to the setting determined in (c) for operation in the first mode.

Drawings

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

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

FIG. 2A illustrates an example portable monitoring device that can be secured to a user via the use of a strap.

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

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

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

FIG. 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 implementation.

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

Fig. 4B and 4C illustrate examples of PPG sensors having a photodetector and two LED light sources.

Fig. 5 illustrates an example of an optimized PPG detector having protrusions with curved sides to avoid discomfort to the user.

FIG. 6A illustrates an example of a portable monitoring device having a strap; an optical sensor and a light emitter may be placed on the tape.

Fig. 6B illustrates an example of a portable biometric monitoring device having a display and a wristband. Furthermore, an optical PPG (e.g., heart rate) detection sensor and/or emitter may be located on the side of the biometric monitoring device. In one embodiment, these various may be located in side mounted buttons.

Fig. 7 depicts a user pressing the side of the portable biometric monitoring device to make heart rate measurements 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.

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

Fig. 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.

Fig. 10 illustrates an example of a portable biometric monitoring device having a bicycle application thereon that can display bicycle speed and/or cadence of pedals, among other metrics.

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

Fig. 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.

Fig. 11C illustrates an example block diagram of a PPG sensor similar to that of fig. 11A, which additionally uses sample and hold circuitry.

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

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

Fig. 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.

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

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

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

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

Fig. 13A illustrates an example of measuring heart rate using a motion signal and an optical PPG signal.

Fig. 13B illustrates another example of measuring heart rate using a motion signal and an optical PPG signal.

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

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

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

FIG. 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.

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

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

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

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

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

Fig. 16B illustrates an example schematic diagram of a circuit for a PPG sensor that uses a controlled current source to compensate for the "bias" current before the transimpedance amplifier.

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

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

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

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

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

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

Fig. 16I illustrates an example schematic diagram of a circuit for a PPG sensor that includes ambient light removal functionality and differencing between successive samples.

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

Fig. 17A shows a schematic diagram of a process for determining whether a heart rate monitor is worn ("on-wrist") or not worn ("off-wrist") using a light detection mechanism.

Fig. 17B shows a schematic diagram of a process for extracurral detection (to exit PPG heart rate monitoring) and on-wrist detection (to enter PPG heart rate monitoring) using separate light detection mechanisms.

FIG. 18A shows a process flow diagram for a wearable fitness monitoring device with a heart rate monitor operating in different modes in an energy-efficient manner, according to some embodiments.

Fig. 18B shows another process flow diagram for operating a wearable fitness monitoring device with a heart rate monitor, which begins with simultaneous operation of a first mode for detecting cardiac signals and a second mode for detecting an unworn state, in accordance with some embodiments.

Fig. 18C shows a sketch of light pulses used in some embodiments to provide data of heart rate versus light pulses used to detect proximity of a user's body.

Fig. 18D shows another sketch of light pulses that may be used in some embodiments to provide data of heart rate versus light pulses for detecting proximity of a user's body.

Fig. 19A shows two relationships between the intensity of light emitted by a light source of a heart rate monitor and a signal detected by a photodetector of the heart rate monitor.

Fig. 19B depicts the time modulation of the TIA signal caused by the heart beat.

Fig. 19C shows a flow chart of a process for operating the heart rate monitor of the wearable fitness monitoring device by adjusting the light emission power and/or light detection gain of the heart rate monitor.

FIG. 19D shows a light pulse signal pattern that may be used to adjust the light source intensity and/or light detection gain of a heart rate monitor.

Fig. 19E and 19F show the mathematical relationship of linearity and nonlinearity between emission and detection intensities, respectively, for different skin characteristics.

Detailed Description

The present invention is directed to biometric monitoring devices (which may also be referred to herein and in any reference incorporated by reference as "biometric tracking devices," "personal health monitoring devices," "portable biometric monitoring devices," "biometric monitoring devices," etc.), which may generally be described as wearable devices, typically of small size, designed to be worn by a person relatively continuously. While worn, such biometric monitoring devices gather data about activities performed by the wearer or the physiological state of the wearer. This data may include data representing the surrounding environment around the wearer or the wearer's interaction with the environment, such as motion data about the wearer's movements, ambient light, ambient noise, air quality, etc., as well as physiological data obtained by measuring various physiological characteristics of the wearer, 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 provides several biometric monitoring devices that are all very small and very light, for example, 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 able to be worn for long 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 used to obtain the heart rate data, or a GPS receiver in a smartphone may be used to obtain the 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 may be applied to both standalone biometric monitoring devices as well as biometric monitoring devices that utilize sensors or functionality provided in external devices (e.g., external sensors, sensors or functionality provided by smartphones, etc.).

In general, the concepts discussed herein may be implemented in stand-alone biometric monitoring devices as well as biometric monitoring devices that utilize external devices (as appropriate).

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

The functionality discussed herein may be provided using several different approaches. For example, in some implementations, a processor may be controlled by computer-executable instructions stored in a memory so as to provide the functionality as described herein. In other embodiments, this functionality may be provided in the form of circuitry. In yet other implementations, such 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 general purpose microprocessors coupled with memory that stores executable instructions for controlling the general purpose microprocessor.

The independent biometric monitoring device may be provided in several form factors and may be designed to be worn in a variety of ways. In some implementations, the biometric monitoring device may be designed to be insertable into a wearable case or may be insertable into a plurality of different wearable cases, such as a wristband case, a belt clip case (belt clip case), a pendant case, a case configured to be attached to a piece of exercise equipment such as a bicycle, and so forth. Such embodiments are described in more detail in, for example, U.S. patent application No. 14/029,764, filed on 9/17/2013, which is hereby incorporated by reference for this purpose. In other implementations, the biometric monitoring device may be designed to be worn in only one way, e.g., 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).

A portable biometric monitoring device according to the embodiments and implementations described herein may have a shape and size suitable for coupling to (e.g., securing to, wearing, being supported by, 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, a processor, a biometric sensor, a memory, an environmental sensor, and/or a wireless transceiver that may communicate with a client and/or a server. An example of a wrist-worn portable biometric monitoring device is shown in fig. 2A-2C. The device may have a display, buttons, electronics packaging, and/or attachment straps. The attachment strap may be secured to the user via the use of a hook and loop (e.g., Velcro), a snap, and/or a strap with a memory in its shape (e.g., via the use of a spring metal strap). In fig. 2B, sensor protrusions and recesses for mating with a charger and/or data transmission cable can be seen. In fig. 2C, a cross-section through an electronic device package is shown. Of note are sensor bumps, a main PCB board, and a display.

The portable biometric monitoring device may 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 devices capable of serving as internet-accessible data sources, thus permitting 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 calculate and store a number of steps of the user using one or more biometric sensors. The biometric monitoring device may then transmit data representing the user's number of steps to an account on a network service (e.g., www.fitbit.com), a computer, a mobile phone, or a healthcare station where the data may be stored, processed, and observed by the user. Indeed, the biometric monitoring device may measure or calculate a plurality of other physiological metrics in addition to or instead of the user's steps. These physiological metrics include, but are not limited to, energy expenditure, such as caloric burn values, number of floors climbed and/or downstairs, 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, swimming one-way counts, swimming stroke type and detected counts, bicycle distance and/or speed, blood pressure, blood glucose, skin conductance, skin and/or body temperature, muscle status measured via electromyography, brain activity measured by electroencephalography, weight, body fat, intake, nutrient intake from food, drug intake, sleep cycles, such as clock time, sleep stages, sleep quality and/or duration, pH levels, hydration levels, respiration rates, and other physiological measures. The biometric monitoring device may also measure or calculate metrics related to the environment surrounding 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 spent in the dark and/or duration), noise exposure, radiation exposure, and magnetic fields. Further, a biometric monitoring device or system that collects a data stream from a biometric monitoring device may calculate a metric derived from this data. For example, a device or system may calculate a user's stress and/or relaxation level via a combination of heart rate variability, skin conductance, noise pollution, and sleep quality. In another example, a device or system may determine the efficacy of a medical intervention (e.g., a drug) via a combination of drug intake, sleep data, and/or activity data. In yet another example, the biometric monitoring device or system may determine the efficacy of the 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 No. 13/156,304 entitled "Portable Biometric Monitoring device and method of Operating the Same", filed on 8.2011 and U.S. patent application No. 61/680,230 entitled "Fitbit Tracker (Fitbit Tracker"), filed on 8.2012, both of which are hereby incorporated by reference in their entirety.

Physiological sensor

The biometric monitoring devices discussed herein may use one, some, or all of the following sensors to acquire physiological data, including but not limited to the physiological data summarized in the following table. All combinations and permutations of physiological sensors and/or physiological data are intended to fall within the scope of the present invention. The biometric monitoring device may include, but is not limited to, the following types of one, some, or all of the sensors specified for acquiring corresponding physiological data; indeed, other types of sensors may also or alternatively be used to acquire corresponding physiological data, and such other types of sensors are also intended to fall within the scope of the present disclosure. Further, the biometric monitoring device may derive physiological data from corresponding sensor output data, including but not limited to the number or type of physiological data it may derive from the sensor.

In one example embodiment, the biometric monitoring device may include an optical sensor to detect, sense, sample, and/or generate data that may be used to determine information representative of, for example, the user's stress (or level thereof), blood pressure, and/or heart rate. (see, e.g., FIGS. 2A-3C and 11A-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 body of the user, and a light detector (photodiode, phototransistor, etc.) to sample, measure, and/or detect the response or reflection of such light from the user's body and provide data used to determine data representative of the user's stress (or level thereof), blood pressure, and/or heart rate (e.g., such as by using a photoplethysmogram).

In one example embodiment, the heart rate measurement of the user may be triggered by criteria determined by one or more sensors (or processing circuitry connected thereto). For example, when data from a motion sensor indicates periods of being stationary or having little motion, the biometric monitoring device may trigger, acquire, and/or obtain heart rate measurements or data. (see, e.g., FIGS. 9, 12A and 12B).

In one embodiment, when a motion sensor indicates user activity or motion (e.g., motion that is inappropriate or not optimal for triggering, acquiring, and/or obtaining desired heart rate measurements or data (e.g., data to determine a resting heart rate of a user), the biometric monitoring device and/or sensor used to acquire and/or obtain the desired heart rate measurements or data may be placed in or remain in a low power state. Because heart rate measurements made during exercise may be less reliable and may be corrupted by motion artifacts, it may be desirable to reduce the frequency (and thus power usage) at which heart rate data samples are collected 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 characteristics that trigger, acquire, and/or obtain desired heart rate measurements or data (e.g., 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 sensor used to acquire heart rate data (e.g., if the amount of user motion exceeds a certain threshold, the biometric monitoring device may increase the sampling rate and/or increase the sampling resolution mode of the sensor used to acquire heart rate measurements or data). Further, 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 (e.g., 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 to, for example, enable more accurate adaptive filtering of the heart rate signal. (see, e.g., FIG. 9).

Fig. 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 (e.g., via use of an accelerometer), the user may be considered "active" by the biometric monitoring device, and high sample rate heart rate detection may occur to reduce motion artifacts in the heart rate measurements. This data may be saved and/or displayed. In the case where the biometric monitoring device determines that the user is not moving (or is relatively sedentary), low sample 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 a photoplethysmogram, the motion signal may be used to determine or establish a particular method or technique of data acquisition or measurement (e.g., synchronous detection rather than non-amplitude modulation methods) by the heart rate sensor and/or analysis thereof. (see, e.g., FIG. 11E). In this way, data indicative of the amount of user motion or activity may cause the biometric monitoring device to establish or adjust the type or technique of data acquisition or measurement used by the one or more optical heart rate sensors.

For example, in one embodiment, the biometric monitoring device (or heart rate measurement techniques as disclosed herein) may adjust and/or reduce the sampling rate of the optical heart rate sampling when the motion detector circuit detects or determines that the motion of the biometric monitoring device wearer is below a threshold (e.g., if the biometric monitoring device determines that the user is sedentary or asleep). (see, e.g., FIG. 9). In this way, the biometric monitoring device may control its power consumption. For example, the biometric monitoring device may reduce power consumption by reducing the sensor sampling rate, e.g., the biometric monitoring device may sample the heart rate (via the heart rate sensor) once every 10 minutes or 10 seconds every 1 minute. It is noted that additionally or alternatively, the biometric monitoring device may control power consumption via controlling data processing circuit analysis and/or data analysis techniques in accordance with motion detection. Thus, the motion of the user may affect the heart rate data acquisition parameters and/or the 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 may be improved by using one or more algorithms to remove motion artifacts. The movement of the user (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 use motion signals captured from other sensors as a guide to remove the component of the PPG signal attributable to motion (motion artifact). In one embodiment, an adaptive filter may be used to remove motion artifacts in the PPG signal based on a hybrid Kalman filter (Kalman filter) and a least mean square filter or a recursive least squares filter. The heart rate may then be extracted from the clean/filtered signal using a peak count algorithm or a power spectral density estimation algorithm. Alternatively, a kalman filter or a particle filter may be used to remove such movement artifacts.

Another method that may be used to calculate heart rate frequency is to create a heart rate signal model as Y ═ Y_dc+∑a_k*coskθ+b_kSin k θ, where k is the order of the harmonic components and θ is the model parameter for heart rate. This model may then be fitted to the signal using an extended kalman filter or a particle filter. This model takes advantage of the fact that: the signal is not sinusoidal and therefore contains power at both the fundamental harmonic and a number of additional harmonics.

Alternatively, the signal may be modeled as Y ═ Y_dc+∑a_k*sin(k*w_motiont+θ)+∑b_k*sin(k*w_HRt + phi) where w_motionEstimated directly from the accelerometer signal (or another motion sensor signal).

Ambient light and skin color

Ambient light and skin tone may make it difficult to extract the heart rate of the user from the PPG signal. The effect of ambient light may 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 that the two signals are obtained at times in close proximity to each other).

The impact of skin tone may be reduced by varying the intensity of the PPG light source, the wavelength of the light emitted from the light source, and/or by using the ratio or difference of the received signals corresponding to the two different wavelengths. The skin tone may be determined by using user input (e.g., the user types his skin tone), an image of a human face, etc., and the skin tone may then be used to calibrate the algorithm, light source brightness, light source wavelength, and receiver gain. The effect of skin tone (and the tightness of the user wearing the device) on the original PPG signal may also be measured by sending a signal with a known amplitude to the light source and then measuring the signal received from the photodetector. Such signals may be sent for an extended period of time (in order to capture data over multiple expected heartbeats) and then averaged to produce a steady state data set that is 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.

Heart rate estimation improvement using heuristics

After obtaining an initial estimate of the heart rate (e.g., a peak count by power spectral density estimation), it may be used to impose a bound on the allowable rate of the heart rate. These bounds can be optimized for each user because each user will have a unique heart rate profile. For example, each user may be estimated for their sedentary heart rate while stationary, and this may serve as a lower limit when the user is walking. Similarly, one-half of the walking frequency as calculated from the pedometer may serve as a good lower limit for the expected heart rate.

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

HR quality metric

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, an algorithm that determines the user's heart rate (or other PPG-derived metric such as respiration) may take certain actions, including having the user tighten the watchband, ignoring certain portions of the collected heart rate data (e.g., data segments with low quality metrics), and weighting certain portions of the heart rate data (e.g., data with higher quality metrics may be given more weight in calculating the heart rate).

In one embodiment, the signal quality metric may be derived as follows: a scatter plot is drawn where the x-axis is time and the y-axis is the frequency of the peak in the PPG signal at that given instant. A problem to overcome using this strategy is that there may be multiple and/or zero peaks at a given time. The best fit line captures the linear relationship in this scatter plot. A high quality signal should have a set of peaks that fit well to the line (over a short time span), while a poor signal will have a set of peaks that cannot be well described by the line. Thus, the quality of the fit to the line provides a good measure of the quality of the PPG signal itself.

Sedentary, sleep and activity classification metrics

In yet another example embodiment, the biometric monitoring device may use the sensor 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 periods of non-sedentary or user activity that exceed a predetermined threshold) to calculate heart rate variability. The biometric monitoring device (or external device) may use heart rate variation as an indicator of heart health or stress.

Indeed, in some embodiments, the biometric monitoring device may measure and/or determine a stress level and/or heart health of the user while the user is sedentary and/or asleep (e.g., as detected and/or determined by the biometric monitoring device). Some embodiments of the biometric monitoring device of the present invention may use sensor data indicative of heart rate changes, galvanic skin response, skin temperature, body temperature, and/or heart rate to determine a stress level, a health state (e.g., risk, onset, or progression of fever or cold), and/or heart health of a user. In this manner, the processing circuitry of the biometric monitoring device may determine and/or track a "baseline" stress level of the user over time and/or a cardiac "health" over time. In another embodiment, the device may measure the physiological parameter of the user during one or more periods in which the user is not moving (or the user's motion is below a predetermined threshold, such as when the user is sitting, lying down, sleeping, or in a sleep stage (e.g., deep sleep)). This data may also be used by the biometric monitoring device as a "baseline" 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.

Sleep monitoring

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

Real-time, windowing, or batch processing may be used to determine transitions between awake, sleep, and sleep stages. For example, a decrease in heart rate may be measured in a time window (heart rate rising at the beginning of the window and decreasing in the middle (and/or end) of the window). Arousal and sleep stages may be classified by hidden markov models using motion signals (e.g., reduced intensity of motion), heart rate variation, skin temperature, galvanic skin response, and/or changes in ambient light levels. The transition point may be determined by a change point algorithm (e.g., bayesian change point analysis). The transition between awake and sleep may be determined by observing periods in which the user's heart rate decreases by at least some threshold for a predetermined duration but is within a predetermined margin of the user's resting heart rate (which is observed, for example, as the minimum heart rate of the user while sleeping). Similarly, the transition between sleep and awake may be determined by observing an increase in the heart rate of the user above a predetermined threshold above the resting heart rate of the user.

In some embodiments, the biometric monitoring device may be one component of a system for monitoring sleep, where the system includes an auxiliary device (e.g., an alarm clock) configured to communicate with the biometric monitoring device and adapted to be placed in proximity to a sleeper. In some implementations, the accessory device has a shape and mechanical and/or magnetic interface to receive a biometric monitoring device for secure custody, communication, and/or charging. However, the accessory device may also be universally applicable to biometric monitoring devices, such as smartphones that are 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, e.g., bluetooth 4.0 and bluetooth low energy protocols), RFID, NFC, or WLAN. The auxiliary device may include sensors to assist 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 auxiliary device may communicate with an external service or server (e.g., a personal computer), such as www.fitbit.com. Communication with the auxiliary device may be accomplished via wired (e.g., ethernet, USB) or wireless (e.g., WLAN, bluetooth, RFID, NFC, cellular) circuitry and protocols to transfer data to and/or from the auxiliary device. The secondary device may also act as a relay to communicate data from an external service or server (e.g., personal computer, mobile phone, tablet computer) such as www.fitbit.com or other services (e.g., data such as news, social network updates, email, calendar notifications, etc.) to the biometric monitoring device and/or from the biometric monitoring device to the external service or server. The calculation of user sleep data may be performed on one or both devices or an external service (e.g., a 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 communicated thereto 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 assistive device may display data indicative of the user's heart rate, total steps for the day, activity and/or sleep goal achievement, weather for the day (measured by the assistive device or reported by an external service for a location), and so on. In another example, the secondary device may display data related to the ranking of the user relative to other users, such as the total number of weeks. 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 (e.g., vibration motor, speaker), the auxiliary device may act as a back-up alarm (e.g., using an audio speaker). The auxiliary device may also have an interface (e.g., a display and buttons or a touch screen) to create, delete, modify, or enable alarms on the first and/or auxiliary devices.

Sensor-based standby mode

In another embodiment, the biometric monitoring device may automatically detect or determine whether it is attached to, disposed on, and/or worn by the user. In response to detecting or determining that the biometric monitoring device is not attached to, disposed 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, e.g., the optical heart rate sensor and/or circuitry may be placed in a lower power or sleep mode. For example, in one embodiment, the biometric monitoring device may include one or more light detectors (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), the one or more light detectors provide a low return signal, the biometric monitoring device may interpret the data as indicating that the device is not worn. Upon such a determination, the device may reduce its power consumption, such as by "disabling" or adjusting operating conditions of the stress and/or heart rate detection sensors and/or circuits, among other device circuits or displays (e.g., by reducing duty cycle of the light source and/or detector or disabling the light source and/or detector, turning off the device display, and/or disabling or attenuating associated circuits or portions thereof). Further, the biometric monitoring device may periodically determine (e.g., once per second) whether the operating conditions of the stress and/or heart rate detection sensor and/or associated circuitry should be restored to normal operating conditions (e.g., the light source, detector and/or associated circuitry should return to a normal operating mode for heart rate detection). In another embodiment, the biometric monitoring device may restore the operating condition of the stress and/or heart rate detection sensor and/or associated circuitry upon detecting a triggerable event, such as upon detecting motion of the device (e.g., based on data from one or more motion sensors) and/or detecting user input via the user interface (e.g., a touch, bump, or dial interaction with the biometric monitoring device). In some related embodiments, for power saving purposes, the biometric monitoring device may reduce the default rate of its heart rate measurement collection to, for example, one measurement per minute when the user is not active, and the user may have an operating mode that places the device on demand or generates measurements at a faster rate (e.g., once per second), for example, by pushing a button.

Optical sensor

In one embodiment, the optical sensor (source and/or detector) may be disposed on an interior or skin side of the biometric monitoring device, i.e., the side of the biometric monitoring device that contacts, touches, and/or faces the user's skin (hereinafter referred to as the "skin side"). (see, e.g., FIGS. 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 ambient environment (the environmental side). (see, e.g., FIGS. 6A through 7).

Notably, the 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, the optical sensor may obtain data related to ambient light conditions of the environment.

Where the optical sensor is disposed or arranged on the skin side of the biometric monitoring device, in operation, a light source in the biometric monitoring device may emit light on the user's skin, and in response, a light detector in the biometric monitoring device may sample, acquire and/or detect corresponding reflections and/or emitted light from the skin (and from inside the body). One or more light sources and photodetectors 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 photodetectors. 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 data representative of, for example, heart rate, respiration, heart rate variability, oxygen saturation (SpO) of the user ₂) Blood volume, blood glucose, skin moisture and/or skin pigmentation level.

The light source may emit light having one or more wavelengths specific to 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 to or directed 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 a photodiode positioned to sample, measure, and/or detect a response or reflection corresponding to such light may provide data that may be used to determine or detect heart rate. In contrast, a light source that emits light having a wavelength in the red spectrum (e.g., an LED that emits light having a wavelength corresponding to the red spectrum) and a light source that emits light having a wavelength in the infrared spectrum (e.g., an LED that emits light having a wavelength corresponding to the IR spectrum) and positioned to emit light having a wavelength in the infrared spectrumA photodiode that samples, measures, and/or detects the response or reflection of such light may provide a means by which SpO may be determined or detected ₂The data of (1).

Indeed, in some embodiments, the color or wavelength of light emitted by a light source, such as an LED (or a set of LEDs), may be modified, adjusted and/or controlled according to a predetermined type or operating condition of the acquired physiological data. Here, the wavelength of light emitted by the light source may 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 skin temperature or ambient temperature of the user is low, the color of the light emitted by the LEDs may be switched from infrared to green in order to enhance the signal corresponding to cardiac activity. (see, e.g., FIG. 11D).

In some embodiments, the biometric monitoring device may include a window (e.g., to temporarily inspect, 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., having a selected wavelength) onto the user's skin and permit the response or reflection of the light to pass back through the window for sampling, measurement, and/or detection by, for example, one or more photodiodes. In one embodiment, circuitry related to emitting and receiving light may be disposed inside the device housing and under or behind a plastic or glass layer (e.g., sprayed with infrared ink) or an infrared lens or filter that permits infrared light to pass but does not permit light in the human visual spectrum to pass. In this way, the light transmission of the window is not visible to the human eye.

The biometric monitoring device may use light pipes or other light transmissive structures to facilitate the emission of light from the light source to the user's body and skin. (see, e.g., FIGS. 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 may be directed back to the optical circuit in the biometric monitoring device via the same or similar structure. Indeed, the light transmissive structure may use materials and/or optical designs that facilitate low light loss (e.g., the light transmissive structure may include lenses that facilitate light collection, and portions of the light transmissive structure may be coated with or adjacent to a reflective material to facilitate internal reflection of light within the light transmissive structure), thereby improving the signal-to-noise ratio of the photodetector and/or facilitating a reduction in power consumption of the light source and/or photodetector. In some embodiments, the light pipe or other light transmissive structure may include a material that selectively emits light having one or more particular or predetermined wavelengths (with an emission efficiency higher than that of light having other wavelengths), thereby acting as a bandpass filter. Such band pass filters can be tuned to improve signals of particular physiological data types. For example, in one embodiment, an in-mold label or "IML" light transmissive structure may be implemented, wherein the light transmissive structure uses materials with predetermined or desired optical properties to create specific bandpass characteristics, such as to pass infrared light more efficiently than light having other wavelengths (e.g., light having wavelengths in the human visible spectrum). In another embodiment, the biometric monitoring device may use a light transmissive structure having an optically opaque portion (including certain optical properties) and an optically transparent portion (including optical properties different from the optically opaque portion). Such light transmissive structures may be provided via a dual injection or two-step molding process, wherein an optically opaque material is injected into the mold separately from an optically transparent material. Biometric monitoring devices implementing such light transmissive structures may include different light transmission characteristics for different wavelengths depending on the direction of light travel through the light transmissive structure. For example, in one embodiment, the optically opaque material may reflect a particular range of wavelengths in order to more efficiently emit 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, the reflective structure may be placed in the field of view of the light emitter and/or light detector. For example, the sides having apertures (or light-transmissive structures performing such a lane run) that direct light from the light emitter to the user's skin and/or from the user's skin to the light detector may be covered in a reflective material (e.g., chrome plating) to facilitate light transmission. The reflective material may increase the efficiency of transmitting light from the light source to the skin and then from the skin back to the detector. The reflective coating holes may be filled with an optical epoxy or other transparent material to prevent liquids from entering the device body while still allowing light to be transmitted with low transmission losses.

In another embodiment where light-transmissive structures (e.g., structures created or formed via IML) are implemented, such light-transmissive structures may include a mask composed of an opaque material that circumscribes the aperture of one, some, or all of the light sources and/or detectors. In this way, the light-transmissive structure can selectively "define" a preferred volume of the user's body from which light is emitted to or detected. It is noted that other mask configurations may be used or implemented in conjunction with the concepts described and/or illustrated herein; all such masking configurations implemented, for example, to improve photoplethysmogram signals and in conjunction with the concepts described and/or illustrated herein are intended to fall within the scope of the present invention.

In another embodiment, the light emitter and/or detector may be configured to emit light through a hole or series of holes in the exterior of the device. The hole or series of holes may be filled with a light transmissive epoxy, such as an optical epoxy. The epoxy may 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 technique also has the following advantages: the epoxy may form a waterproof seal, preventing water, perspiration, or other liquids from entering the device body through holes on the exterior of the device that allow the light emitter and detector to emit light to and receive light from the exterior of the biometric monitoring device body. Epoxy resins with high thermal conductivity may be used to help prevent overheating of the light source (e.g., LED).

In any of the light-transmitting structures described herein, the exposed surface of the optical device (light-transmitting structure) or device body may comprise a hard paint, a hard dip, or an optical coating, such as an anti-reflective, anti-scratch, anti-fog, and/or wavelength band blocking (e.g., ultraviolet light blocking) coating. Such characteristics or materials may improve the operation, accuracy, and/or durability of the biometric monitoring device.

Figure 4A illustrates an example of one 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 a wristband type biometric monitoring device (with its side facing the user's skin).

Fig. 4B and 4C illustrate examples of PPG sensors having a photodetector and two LED light sources. These components are placed in a biometric monitoring device having a protrusion on the back side. A light pipe optically connects the LED and photodetector to the surface of the user's skin. Under the skin, light from the light source scatters from blood in the body, some of which may scatter or reflect back into the photodetector.

Fig. 5 illustrates an example of a biometric monitoring device with an optimized PPG detector having protrusions with curved sides to avoid discomfort to the user. Furthermore, the surface of the light pipe that optically couples the photodetectors and LEDs to the wearer's skin is contoured to maximize the luminous flux coupling between the LEDs and photodetectors and the light pipe. The end of the light pipe facing the skin of the user is also corrugated. This profile can focus or defocus light to optimize the PPG signal. For example, the profile may focus the emitted light to a certain depth and location that conforms to the area where blood flow may occur. The apexes of these focal points may overlap or be in close proximity so that the photodetector receives the maximum possible amount of scattered light.

In some embodiments, the biometric monitoring device may include a concave or convex shape (e.g., a lens) on the skin side of the device to focus light toward a particular volume at a particular depth in the skin and increase the efficiency of collecting light from the spot to the photodetector. (see, e.g., FIGS. 4A through 5). Where such biometric monitoring devices also use light pipes to selectively and controllably route light, it may be advantageous to shape the end of the light guide to have a degree of cylindricity (e.g., the end of the light guide may be a cylindrical surface (or portion thereof) defined by a cylindrical axis nominally parallel to the skin side) (e.g., 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 cylindrical axis is nominally parallel to the forearm of the wearer, which may have the effect of limiting the amount of light entering such lenses from a direction parallel to the forearm of the person and increasing the amount of light entering such lenses from a direction perpendicular to the forearm of the person, because ambient light is more likely to reach the sensor detection area from a direction not obscured by the strap of the biometric monitoring device (i.e., along the forearm axis of the user) than from a direction obscured by the strap (i.e., perpendicular to the forearm of the user), such a configuration may improve signal-to-noise ratio by increasing the efficiency of transmitting light from the emitter onto or into the skin of the user while reducing "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 this emitted light (signal or data representative of the response to the emitted light).

In another embodiment, the optically transmissive epoxy may be molded into a concave or convex shape to also provide beneficial optical properties to the sensor. For example, during application of a light transmissive epoxy, the top of the light transmissive structure formed by the epoxy may be shaped into a concave surface to allow light to be more efficiently coupled to 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 the distance between (i) the light source and/or associated detector and (ii) the user's skin. See, e.g., fig. 3A, which provides a cross-sectional view of a sensor protrusion of an example portable monitoring device. In fig. 3A, two light sources (e.g., LEDs) are placed on either side of the photodetector to enable PPG sensing. The 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 may be placed over the lower surface of the sensor protrusion to form a seal. This transparent layer may serve other functions, such as preventing liquid from entering the device at the location where the light source or photodetector is placed. This transparent layer may be formed via in-mold labeling or "IML". The light source and photodetector may be placed on a flexible PCB.

Such a configuration may improve the luminous flux coupling efficiency between the components of the optical sensor and the user's body. For example, in one embodiment, the light source and/or associated detector may be disposed on a flexible or bendable substrate that is flexible, allowing the skin side of the biometric monitoring device (which may be made of a compliant material) to conform (e.g., without additional processing) or be capable of being shaped (or compliant) to conform to the shape of the body portion (e.g., the wrist, arm, ankle, and/or leg of the user) to which the biometric monitoring device is coupled or attached during normal operation, such that the light source and/or associated detector is proximate to the skin of the user (i.e., there is little or no gap between the skin side of the device and the adjacent surface of the user's skin). See, for example, fig. 6A. In one embodiment, the light source and/or associated detector may be disposed on a Flat Flex Cable (Flat Flex Cable) or "FFC" or flexible PCB. In this embodiment, a flexible or bendable substrate (e.g., an FFC or flexible PCB) may be connected to a second substrate (e.g., a PCB) within the device on which other components (e.g., data processing circuitry) are disposed. Optical components of different heights can be mounted to different "fingers" of the flexible substrate and pressed or secured 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.

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

Some embodiments of the biometric monitoring device may be adapted to be worn or carried on a user's body. In some embodiments including an optical heart rate monitor, the device may be a wrist-worn or arm-mounted accessory such as a watch or bracelet. (see, e.g., FIGS. 2A through 7). In one embodiment, the optical elements 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 to and facing the wrist). (see, e.g., FIGS. 2A through 3C).

In another embodiment, the optical heart rate monitor may be located on one or more external or environmental side surfaces of the biometric monitoring device. (see, e.g., FIGS. 6B and 7). In such embodiments, the user may touch the optical window (behind which the optical elements of the optical heart rate monitor are located) with a finger of the opposite hand to initiate heart rate measurements (and/or other metrics related to heart rate, such as heart rate variability) and/or collect data that may be used to determine the user's heart rate (and/or other metrics related to heart rate). (see, e.g., FIG. 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 other detectors). Such infrared-based proximity detectors and/or capacitive touch/proximity detectors may be disposed in or on and/or functionally, electrically and/or physically coupled to the optical window to detect or determine the presence of a user's finger, for example.

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 disposed in close proximity to the optical window to facilitate a user to press the button when a finger is placed over the optical window. (see, e.g., FIG. 7). In one embodiment, the optical window may be embedded in the push button. Thus, when a user presses a button, it may trigger a measurement of the finger pressing the button. Indeed, the button may be given a certain shape and/or press resistance 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, or the like, adapted to be worn on the body, clipped or mounted to an article of clothing, stored in an article of clothing (e.g., a pocket), or stored in an ornament (e.g., a handbag).

In one particular embodiment, the biometric monitoring device may include a protrusion on the skin side or inner side of the device. (see, FIGS. 2A to 6A). When coupled to a user, the protrusions may engage the skin with a 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 emitter and/or detector of the optical sensor may be disposed near the window or light transmissive structure or arranged in the protrusion. (see, e.g., FIGS. 2B and 6A). As such, when attached to the user's body, the raised window portion of the biometric monitoring device may engage the user's skin with greater force than the surrounding device body, thereby providing a more secure physical coupling between the user's skin and the optical window. That is, the protrusions may cause a persistent contact between the biometric monitoring device and the user's skin, which may reduce the amount of stray light measured by the photodetector, reduce relative motion between the biometric monitoring device and the user, and/or provide improved local pressure on the user's skin; all of this 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 in place of a heart rate sensor, and include sensors such as skin temperature sensors (e.g., non-contact thermopiles utilizing optical windows or thermistors bonded to the outer surface of the protrusion by thermal epoxy), pulse oximeters, blood pressure sensors, EMG or Galvanic Skin Response (GSR) sensors, and the like.

Additionally or alternatively, a portion of the skin side of the biometric monitoring device may include a friction enhancing mechanism or material. For example, the skin side of the biometric monitoring device may include a plurality of raised or recessed regions or portions (e.g., small bumps, ridges, grooves, and/or divots). Further, a friction enhancing material (e.g., a gel-like material such as silicone or other elastic material) may be disposed on the skin side. Indeed, the back of the device made of gel may also provide friction, while also improving user comfort and preventing stray light from entering. As noted above, the friction enhancing mechanism or material may be used alone or in conjunction with a biometric monitoring device having protrusions as described herein. In this regard, the biometric monitoring device may include a plurality of raised or recessed regions or portions (e.g., nubs, ridges, grooves, and/or divots) in or on the raised portions of the device. Indeed, such raised or recessed regions or portions may be incorporated/embedded into or onto the window portions of the protrusions. Additionally or alternatively, the raised portion may be composed of or coated with a friction enhancing material (e.g., a gel-like material such as silicone). Notably, the use of protrusions and/or friction may improve the measurement accuracy of data acquisition corresponding to certain parameters (e.g., heart rate variation, galvanic skin response, skin temperature, skin coloration, heat flux, blood pressure, blood glucose, etc.) by reducing the motion of the biometric monitoring device (and thus the sensor) relative to the user's skin during operation, particularly when the user is moving.

Some or all of the interior or skin-side housing of the biometric monitoring device may also be composed of a metallic material (e.g., steel, stainless steel, aluminum, magnesium, or titanium). Such a configuration may provide structural rigidity. (see, e.g., FIG. 2B). In such embodiments, the device body may be designed to be hypoallergenic by using hypoallergenic "nickel-free" stainless steel. It is noted that it may be advantageous to use some type of metal (e.g., a stainless steel grade that is ferrous) that is at least slightly ferrous (at least in certain locations). In such embodiments, the biometric monitoring device, where it includes a rechargeable energy source (e.g., a rechargeable battery), may be interconnected with the charger via a connector that secures itself to the biometric monitoring device using a magnet coupled to the ferrous material. Further, the biometric monitoring device may also engage a dock or docking station using this magnetic characteristic to facilitate data transfer. Furthermore, such a housing may provide enhanced electromagnetic shielding that will enhance the integrity and reliability of the optical heart rate sensor and heart rate data acquisition process/operation. Further, the skin temperature sensor may be physically and thermally coupled to the metal body, such as by thermal epoxy, to sense the temperature of the user. In embodiments including 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 (which may be located in the protrusion in one embodiment, and/or which may be disposed or placed flush with a surface of the biometric monitoring device in another embodiment) are attached, fixed, included, and/or secured to the biometric monitoring device via a liquid-tight seal (i.e., a method/mechanism that prevents liquid from entering into the body of the biometric monitoring device). For example, in one embodiment, a back side of the device made of metal (such as, but not limited to, stainless steel, aluminum, magnesium, or titanium) or a rigid plastic may provide a structure that is sufficiently rigid to maintain the structural integrity of the device while accommodating a water-tight seal of the sensor package. (see, e.g., FIGS. 2B through 3C).

In a preferred embodiment, the package or module of the optical sensor may be connected to the device by a pressure sensitive adhesive and a liquid gasket. See, e.g., fig. 3C, which provides another cross-sectional view of a PPG sensor embodiment. Notably, there are no protrusions in this PPG sensor. In addition, liquid gaskets and/or pressure sensitive adhesives are used to prevent liquids from entering the device body. 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. Notably, the present embodiment may also use a waterproof glue, hydrophobic membrane, such as Gore-Tex, o-ring, sealant, grease, or epoxy, to secure or attach the optical sensor package/module to the biometric monitoring device body.

As discussed above, the biometric monitoring device may include being disposed on the skin side orMaterials on the inner side that include high reflectivity characteristics, such as polished stainless steel, reflective paint, and polished plastic. In this way, light scattered from the skin side of the device may be reflected back to the skin in order to, for example, improve the signal-to-noise ratio of the optical heart rate sensor. In effect, this effectively increases the input optical signal compared to the non-reflective (or less reflective) device body backside. Notably, in one embodiment, the color of the skin side or inner side of the biometric monitoring device may be selected to provide certain optical characteristics (e.g., reflect light of a particular or predetermined wavelength) in order to improve the signal with respect to certain physiological data types. For example, where the skin side or inside of the biometric monitoring device is green, the measurement of heart rate may be enhanced due to the preferred emission of light of a wavelength corresponding to the green spectrum. Where the skin side or interior side of the biometric monitoring device is red, the SpO may be enhanced due to preferred emission of light of a wavelength corresponding to the red spectrum₂The measurement of (2). In one embodiment, the color of the skin side or inner side of the biometric monitoring device may be modified, adjusted and/or controlled according to a predetermined type of acquired physiological data.

Fig. 11A depicts an example schematic block diagram of an optical heart rate sensor in which light is emitted from a light source towards the skin of a user and the reflection of this light from the skin/body interior of the user is sensed by a light detector, the signal from which is then digitized by an analog-to-digital converter (ADC). The intensity of the light source may be modified (e.g., via a light source intensity control module) to maintain a desired reflected signal intensity. For example, the light source intensity may be reduced to avoid saturation of the output signal from the light detector. As another example, the light source intensity may be increased to maintain the output signal from the light detector within a desired range of output values. Notably, active control of the system can be achieved via linear or non-linear control methods (e.g., proportional-integral-derivative (PID) control, fixed step control, predictive control, neural networks, hysteresis, etc.), and can also use information derived from other sensors in the device (e.g., motion, galvanic skin response, etc.). Fig. 11A is provided for illustration and not to limit implementation of such systems to, for example, ADCs integrated within MCUs or MCUs used in this regard. Other possible implementations include the use of 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 light detector while the light source is turned off or attenuated to save power. In embodiments where relative changes in the output of the photodetector are critical (e.g., heart rate measurements), the sample and hold circuitry may not have to maintain an accurate copy of the output of the photodetector. In such cases, the sample and hold may be reduced to, for example, diodes (e.g., schottky diodes) and capacitors. The output of the sample and hold circuit may be presented to an analog signal conditioning circuit (e.g., a salen-based (Sallen-Key) band pass filter, a level shifter, and/or a gain circuit) to condition and amplify the signal within the band of interest (e.g., 0.1Hz to 10Hz for cardiac or respiratory function), which may then be digitized by an ADC. See, for example, fig. 11B.

In operation, circuit topologies such as those already described herein (e.g., sample and hold circuits) remove DC and low frequency components of the signal and help resolve AC components related to heart rate and/or respiration. Embodiments may also include analog signal conditioning circuitry for variable gain settings that may be controlled to provide suitable signals (e.g., not saturated). The performance characteristics (e.g., ramp rate and/or gain bandwidth product) and power consumption of the light source, photodetector, and/or sample and hold may be significantly higher than the analog signal conditioning circuit to achieve fast duty cycling of the light source. In some embodiments, the power provided to the light source and the light detector can be controlled separately from the power provided to the analog signal conditioning circuit to provide additional power savings. Alternatively or additionally, the circuit may use functionality such as enable, disable, and/or shutdown to achieve power savings. In another embodiment, the output of the photodetector and/or sample and hold circuit may be sampled by the ADC in addition to or instead of the analog signal conditioning circuit to control the light intensity of the light source or to measure a physiological parameter of interest (e.g., when the analog signal conditioning circuit has not stabilized after changing the light intensity setting). Notably, because 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 may be adjusted to enhance signal quality and/or the ADC may be oversampled. 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, for example, fig. 11C.

PPG DC offset removal technique

In another embodiment, the sensor device may incorporate a differential amplifier to amplify the relative change in the output of the photodetector. See, for example, fig. 11F. In some embodiments, a digital average or digital low pass filtered signal may be subtracted from the output of the light detector. This modified signal may then be amplified, then digitized by the ADC. In another embodiment, an analog average or analog low pass filtered signal may be subtracted from the output of the photodetector, such as through the use of a sample and hold circuit and an analog signal conditioning circuit. The power provided to the light source, photodetector, and differential amplifier can be controlled separately from the power provided to the analog signal conditioning circuit to improve power savings.

In another embodiment, a signal (voltage or current, depending on the particular sensor implementation) may be subtracted from the raw PPG signal to remove any bias in the raw PPG signal, and thus increase the gain of or amplify the PPG signal containing heart rate (or other cyclical parameter, such as heart rate variation) information. This signal may be set to a default value in the factory, set to a value based on the user's particular skin reflectivity, absorption, and/or color, and/or may change depending on feedback from the ambient light sensor or depending on the analysis of the PPG signal itself. For example, if the PPG signal is determined to have a large DC offset, a constant voltage may 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 reaching the photodetector from the PPG light source (e.g., from the sun or from indoor lighting) or light reflected from the PPG light source.

In another embodiment, a differential amplifier may be used to measure the difference between the current and previous samples instead of 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 PPG signal quality. The signals may then be integrated to obtain the original time domain signal.

In another embodiment, the optical detector module may incorporate a transimpedance amplifier stage with variable gain. Such a configuration may avoid or minimize saturation by bright ambient light and/or bright light emitted from the light source. For example, the gain of the transimpedance amplifier may be automatically reduced by a variable resistor and/or a set of multiplexed resistors in the negative feedback path of the transimpedance amplifier. In some embodiments, the device may 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 (e.g., synchronous detection). See, e.g., fig. 11E. In other aspects, if the ambient light has sufficient brightness to obtain a heart rate signal, the light source may reduce the brightness and/or be turned off completely.

In yet another embodiment, the aforementioned processing techniques may be used in combination to optically measure a physiological parameter of a user. See, e.g., fig. 11G. This topology may allow the system to operate in a low power measurement state and circuit topology (when applicable) and be adapted to higher power measurement states and circuit topologies as desired. For example, the system may measure a physiological parameter of interest (e.g., heart rate) using analog signal conditioning circuitry when the user is stationary or sedentary to reduce power consumption, but switch directly to over-sampling of the photodetector output when the user is active.

In embodiments in which the biometric monitoring device includes a heart rate monitor, processing the signal to obtain the heart rate measurement may include filtering and/or signal conditioning, such as band pass filtering (e.g., 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 (e.g., motion, galvanic skin response, skin temperature, etc.) may be used to adjust the signal conditioning method used. Under certain operating conditions, the heart rate of the user may be measured by counting the number of signal peaks within a time window or by utilizing the fundamental frequency or a second harmonic of the signal (e.g., via a Fast Fourier Transform (FFT)). In other cases, such as heart rate data acquired while the user is in motion, an FFT may be performed on the extracted signal and spectral peaks, which may then be subsequently processed by a multi-target tracker (which starts, continues, merges, and deletes tracking of the spectrum). In some embodiments, a similar set of operations may be performed on the motion signals, and the output may be used for activity discrimination (e.g., sedentary, walking, running, sleeping, lying down, sitting, cycling, typing, elliptical training, weight training) to assist the multi-target 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 tracking continuation towards increasing frequency. Similarly, the activity discriminator may determine that the user has stopped running or is running slower, and this information may be used to preferentially bias the tracking continuation toward decreasing frequency. Tracking may be accomplished with single-scan or multi-scan multi-target tracker topologies, such as joint probabilistic data association tracker, multi-hypothesis tracking, nearest neighbors, and the like. Estimation and prediction in the tracker can be accomplished via kalman filters, spline regression, particle filters, interactive multi-model filters, and the like. The tracking selector module may use the output trajectory from the multi-spectral tracker and estimate the heart rate of the user. The estimate may be taken as the most likely trajectory, a weighted sum of the probabilities of the trajectories against which they are heart rate, etc. Furthermore, the activity discriminator may influence the selection and/or fusion of obtaining the heart rate estimate. For example, if the user is sleeping, sitting, lying down or sedentary, the previous probability may favor a heart rate in the range of 40 to 80 bpm; whereas if the user is running, jogging or performing other strenuous exercise, the previous probability may be biased towards an elevated heart rate in the range of 90 to 180 bpm. The impact of the activity discriminator may be based on the speed of the user. When the user is not moving, the fundamental frequency of the steerable signal is estimated (or obtained entirely therefrom). A trajectory corresponding to a user's heart rate may be selected based on criteria indicating a change in activity; for example, if the user starts walking from a stationary position, a trajectory illustrating a shift towards higher frequencies may be preferentially selected.

The 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 (e.g., a bluetooth low-energy equipped mobile phone). In some embodiments, the biometric monitoring device may include a signal strength indicator represented by the pulse of an LED that may be viewed by the user. The pulse may be timed or correlated to coincide with the user's heartbeat. The intensity, pulse rate, and/or color of the LED may be modified or adjusted to imply signal strength. For example, a brighter LED intensity may represent a stronger signal or in an RGB LED configuration, a green LED may represent a stronger signal.

In some embodiments, the intensity of the heart rate signal may be determined by the energy (e.g., sum of squares) of the signal in a frequency band of, for example, 0.5Hz to 4 Hz. In other embodiments, the biometric monitoring device may have strain gauges, pressure sensors, force sensors, or other contact indication sensors that may be incorporated or built into the housing and/or the band (in those embodiments where the biometric monitoring device is attached to a band (such as a watch, bracelet, and/or arm band) or mounted with a band (which may then be secured to a user)). A signal quality metric (e.g., 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 optically monitor heart rate via a photodetector array (e.g., a grid of photodiodes or CCD cameras). The motion 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 may be in contact with the skin or offset from the skin by a small distance. The detector array and its associated optics may be actively controlled (e.g., by a motor) to maintain a stable image of the target and acquire a heart rate signal. This opto-mechanical stabilization may be achieved using information from motion sensors (e.g., gyroscopes) or image features. In one embodiment, the biometric monitoring device may implement relative motion cancellation using a coherent or incoherent light source illuminating the skin and an array of photodetectors, with each photodetector associated with a comparator for comparing intensities between neighboring detectors, obtaining a so-called speckle pattern, which may be tracked using a variety of image tracking techniques, such as optical flow, template matching, edge tracking, and the like. In this embodiment, the light source used for motion tracking may be different from the light source used for optical heart rate monitoring.

In another embodiment, the biometric monitoring device may consist of a plurality of photodetectors and light emitters (photoemitters) distributed along the surface of the device that touches the skin of the user (i.e., the skin side of the biometric monitoring device). (see, e.g., FIGS. 2A through 6A). For example, in the example of a bracelet, there may be multiple photodetectors and emitters placed at various points along the inner circumference of the band. (see, e.g., FIG. 6A). A heart rate signal quality metric associated with each location may be calculated to determine the best or set of best locations for estimating the user's heart rate. Some of the sites may then be deactivated or turned off, for example, to reduce power consumption. The device may periodically check the 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, the biometric monitoring device may include a heart rate monitoring system that includes a plurality of sensors such as optical, acoustic, pressure, electrical (e.g., ECG or EKG), and motion, and fuses information from two or more of these sensors to provide an estimate of heart rate and/or mitigate noise induced by motion.

In addition to or instead of heart rate monitoring (or other biometric monitoring), in some embodiments, the biometric monitoring device may include optical sensors to track or detect ultraviolet light exposure, time and duration of all outdoor light exposure, type of light source and duration and intensity of light source (fluorescent light exposure, incandescent light exposure, halogen, etc.), exposure to television (based on light type and flash rate), whether the user is indoors or outdoors, time of day and location, based on light conditions. In one embodiment, the ultraviolet light detection sensor may consist of a reverse biased LED emitter driven as a photodetector. For example, the photocurrent generated by such a detector may be characterized by the time it takes to measure the capacitance of the LED (or alternatively, a parallel capacitor) to discharge.

All 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 an audio or passive acoustic sensor, 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 films, and the like. The acoustic sensors may be disposed on one or more sides of the device, including the side that touches or faces the skin (the skin side) and the side that faces the environment (the environment side).

A skin-side acoustic or audio sensor may detect any type of sound emitted through the body, and such sensors may 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 (via sounds emitted from the vocal cords through the body).

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, such as transitions in sleep stages. In some embodiments, the biometric monitoring device may be a wrist or arm mounted device that incorporates a band made of conductive rubber or fabric so that the cutaneous electrically responsive electrodes may be hidden in the band. Because the electrodermal response circuit may be subjected to changing temperature and environmental conditions, it may also include circuitry to enable automatic calibration, such as two or more switchable reference resistors in parallel or series with the human skin/electrode path, which allows real-time measurement of known resistors to characterize the response of the electrodermal response circuit. 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 resistance of the human skin.

Circuit for performing PPG

The PPG circuit may be optimized to obtain 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 may be used to perform this optimization (see fig. 16A-16J);

sample and hold circuits and differential/instrumentation amplifiers that can be used for PPG sensing. The output signal is an amplified difference between current and previous samples referenced to a given voltage.

-a controlled current source to compensate for the "bias" current before the transimpedance amplifier. This allows applying a large gain 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 rail may be required).

Differential/instrumentation amplifier with ambient light cancellation.

-a photodiode that compensates for the current dynamically generated by the DAC.

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

Ambient light removal using the "switched capacitor" method.

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

Ambient light removal and differentiation between successive samples.

Fig. 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 circuits may be an amplified difference between a current sample and a previous sample referenced to a given voltage.

Fig. 16B illustrates an example schematic of a circuit of a PPG sensor using a controlled current source to compensate for the "bias" current before the transimpedance amplifier. This allows applying a large gain at the transimpedance amplifier stage.

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

Various circuits and concepts related to heart rate measurement using PPG sensors are discussed in more detail in united states provisional patent application No. 61/946,439, filed on 28/2/2014, which was previously incorporated by reference herein in the "cross-reference to related applications," section, and is again hereby incorporated by reference with respect to the contents for 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.

Biometric feedback

Some embodiments of the biometric monitoring device may provide feedback to the 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 a display of the biometric monitoring device (or on a display of an auxiliary device in communication with the biometric monitoring device). This waveform may provide similar feedback to that displayed on an ECG or EKG machine. In addition to providing the user with an indication of the PPG signal, which may be used to estimate various cardiac metrics (e.g., heart rate), the waveform may also provide feedback that may enable the user to optimize the location and pressure at which the user wears the biometric monitoring device. For example, the user may see that the waveform has a low amplitude. In response, 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 degree of fit of the biometric monitoring device in order to improve signal quality.

In another embodiment, feedback regarding the quality of the PPG signal may be provided to the user via a method other than displaying the waveform. The biometric monitoring device may sound an audible alarm (e.g., a beep) if the signal quality (e.g., signal-to-noise ratio) exceeds a certain threshold. The biometric monitoring device may provide visual cues to the user (e.g., via use of a display) to change the position of the sensor and/or increase the pressure with which the device is worn (e.g., by tightening the wristband 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 performs any of these, it may provide feedback to the user regarding the waveforms from those sensors. If the signal-to-noise ratio of these sensors is low or the signal quality suffers for other reasons, the user may be instructed how he can improve the signal. For example, if a heart rate cannot be detected from the ECG sensor, the device may provide a visual message to the user indicating that it is wetting or wetting 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, for example, to acquire environmental data, including the environmental data outlined 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 environmental data summarized in the following table 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. Further, the device may derive environmental data from corresponding sensor output data, but is not limited to the type of environmental data it may derive from the sensor.

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 acquire any or all of the environmental and physiological data described herein using any sensor now known or later developed, all of which are intended to fall within the scope of the present invention.

In one embodiment, the biometric monitoring device may include an altimeter sensor disposed or located inside the device housing, for example. (see, e.g., FIGS. 12B and 12C; FIG. 12C illustrates a sensor having a physiological sensor, an environmental sensor, andan example of a portable biometric monitoring device connected to a location sensor of a processor). In this case, the device housing may have vents that allow the device to measure, detect, sample, and/or experience any changes in external pressure internally. In one embodiment, the vent may prevent water from entering the device while facilitating the change in pressure measured, detected, and/or sampled via the altimeter sensor. For example, an exterior surface of a biometric monitoring device may include a vent type configuration or architecture (e.g., Gore) ^TMA vent) that allows ambient air to move into and out of the housing of the device (which allows the altimeter sensor to measure, detect, and/or sample changes in pressure), but reduces, prevents, and/or minimizes 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 the gel. The gel may act as a relatively impermeable, incompressible, yet flexible diaphragm 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 may give the device a higher level of environmental protection with or without the use of an environmentally sealed vent. The device may have a higher survival rate with the gel fill height meter in a position including, but not limited to: locations with high humidity, washing machines, dishwashers, clothes dryers, steam or sauna rooms, showers, sinks, bathtubs and devices may be exposed to moisture, exposed to liquid or immersed in any location in the 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 following table (e.g., data from two or more sensors may be used in combination to determine metrics such as the metrics listed below). The biometric monitoring device may include, but is not limited to, the number, type, or combination of sensors specified below. Further, such biometric monitoring devices may derive included data from corresponding sensor combinations, but are not limited to the number or type of data that may be calculated 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 the biometric monitoring device is brought into proximity or detectable proximity with the second device, it may trigger the start of new functionality on the second device (e.g., launch of an "application" on the mobile phone and radio synchronization of physiological data from the device to the second device). (see, e.g., FIG. 10). Indeed, the biometric monitoring device of the present invention may implement any of the circuits and techniques described and/or illustrated in U.S. provisional patent application 61/606,559 ("Near Field Communication System and Method of Operating Same", filed 3/5/2012, "inventor: James Park, the contents of which are hereby incorporated herein by reference for this purpose).

Fig. 10 illustrates an example of a portable biometric monitoring device having a bicycle application thereon that can display bicycle speed and/or cadence of pedals, among other metrics. The application may be launched whenever the biometric monitoring device is near a passive or active NFC tag. This NFC tag may be attached to a handle bar of a user.

In another embodiment, the biometric monitoring device may include a location sensor (e.g., GPS circuitry) and a heart rate sensor (e.g., photoplethysmogram circuitry) to generate GPS or location-related data and heart rate-related data, respectively. (see, e.g., FIGS. 12B and 12C). The biometric monitoring device may then fuse, process, and/or combine data from these two sensors/circuits to determine, correlate, and/or "map" a geographic region, for example, from physiological data (e.g., heart rate, stress, activity level, amount of sleep, and/or calorie intake). In this way, 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 photoplethysmogram-related data (notably, each of which may be considered a data stream) to determine or correlate a user's heart rate as a function of activity level (e.g., as determined by the user's acceleration, velocity, location, and/or distance traveled (as determined by GPS measurements and/or from GPS-related data)). (see, e.g., FIGS. 12B and 12C). Here, in one embodiment, the heart rate as a function of speed may be "plotted" for the user, or the data may be broken down into different levels, including but not limited to sleep, rest, sedentary, moderate activity, and high activity.

Indeed, some embodiments of the biometric monitoring device may also correlate GPS-related data with a database of predetermined geographic locations (having activities associated therewith for a set of predetermined conditions). For example, the activity determination and corresponding physiological classification (e.g., heart rate classification) may include correlating GPS coordinates (corresponding to locations of exercise equipment, health clubs, and/or gyms) of the user with physiological data. In these situations, the user's heart rate during, for example, gym fitness may be automatically measured and displayed. Notably, many physiological classifications may be based on GPS related data, including location, acceleration, altitude, distance, and/or velocity. Such databases include geographic data, and may compile, form, and/or store physiological data on the biometric monitoring device and/or an external computing device. Indeed, in one embodiment, a user may create their own location database or add or modify a location database to better classify 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 a remote device using wired or wireless circuitry to calculate, for example, an inaccurate biometric or physiological quality or quantity, such as pulse transit time, that may be otherwise difficult to calculate or calculate. 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 may improve detection of a user taking a step (as 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 may 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 a user's food log (a log of food ingested by the user, its nutritional content, and portions thereof). The food log entry may be automatically entered into the food log or may be entered by the user himself via interaction with the biometric monitoring device (or an auxiliary or remote device in communication with the biometric monitoring device, such as a smartphone, or some other device in communication with the biometric monitoring device, such as a server). Information about 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 the coffee. In another example, if a user eats a larger portion of food late at night, it may take longer to fall asleep than usual. Any combination of food inputs and corresponding results in biometrics may be incorporated into such feedback systems.

The fusion of food intake data with biometric data may also enable some embodiments of the biometric monitoring device to estimate a glucose level of the user. This may be particularly useful for users with diabetes. Through algorithms involving glucose levels and the user's activities (e.g., walking, running, calorie burning) and nutrient intake, the biometric monitoring device may be able to advise the user when they may have abnormal blood glucose levels.

Processing task delegation

Embodiments of the biometric monitoring device may include one or more processors. For example, a standalone 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). In the case where there are multiple sensors, there may also be multiple sensor processors. The application processor may also have sensors directly connected to it. The sensor and the application processor may 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 daughter board (daughterboard) that is made up of all analog components. This board may have some of the electronics typically found on the main PCB, such as, but not limited to, transimpedance amplifiers, filter circuits, horizontal shifters, sample and hold circuits, and microcontroller units. Such a configuration may allow the daughter board 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 may have a variety of advantages over analog daughter board to main PCB connections, including but not limited to noise reduction and reduction in the number of necessary cables. The daughter board may be connected to the motherboard using a flexible cable or a set of wires.

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

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

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

The application may be loaded onto the application processor and/or any external storage device via a variety of wired, wireless, optical, or capacitive mechanisms, including but not limited to USB, Wi-Fi, bluetooth low energy, NFC, RFID, zigbee.

The application may also be cryptographically signed with the electronic signature. The application processor may restrict execution of the application to those with the correct signature.

System integration in biometric monitoring devices

In some implementations of the biometric monitoring device, the sensors or electronic systems in the biometric monitoring device or some 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 in a heart rate sensor as discussed in U.S. provisional patent application No. 61/946,439, filed on day 2/28 2014 and previously incorporated by reference herein) may also serve as a photodetector for determining ambient light levels, such as may be used to correct for the effects 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 or in communication with an onboard optical sensor, such as a component in an optical heart rate monitor. For example, the photodetector of the optical heart rate sensor (or, if present, the ambient light sensor) may also serve as a receiver for an optical transmission channel (e.g., infrared communication).

In some implementations of a biometric monitoring device, a hybrid antenna may be included that combines a radio frequency antenna (e.g., a bluetooth antenna or a GPS antenna) with an inductive loop (e.g., as may be used in a Near Field Communication (NFC) tag or inductive charging system). In such implementations, the functionality of two different systems may be provided in one integrated system, saving package volume. In such hybrid antennas, the 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, for example, a planar inverted-F antenna. At the same time, the inductive loop may also serve its normal function, such as providing 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 greater detail in united states provisional patent application No. 61/948,470, filed 3/5/2014, which was previously incorporated by reference in the "cross-reference to related applications" section and again specifically incorporated by reference herein 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 contemplated within the scope of this disclosure.

Method of wearing a device

Some embodiments of the biometric monitoring device may include a housing sized and shaped to facilitate securing the biometric monitoring device to a user's body during ordinary operation, wherein the device, when coupled to the user, does not measurably or significantly affect the user's activity. The biometric monitoring device may be worn differently depending on the particular sensor package integrated into the biometric monitoring device and the data that the user would like to acquire.

A user may wear some embodiments of the biometric monitoring device of the present invention on their wrist or ankle (or arm or leg) by using a strap that is flexible and thus easily fits to the user. The band may have an adjustable circumference, thus allowing it to fit to a user. The band may be constructed of a material that shrinks when exposed to heat, thus allowing the user to establish a customized fit. The band is detachable and replaceable as necessary from the "electronics" portion of the biometric monitoring device.

In some embodiments, the biometric monitoring device may consist of two main components: a body (containing "electronics") and a strap (facilitating attachment of the device to a user). The body may include a housing (e.g., made of plastic or plastic-like material) and an extension tab (e.g., made of metal or metal-like material) protruding from the body. (see, e.g., FIGS. 2C through 3C). The strap (e.g., made of thermoplastic urethane) may be attached to the body, for example, mechanically or adhesively. The band may extend from a portion of the circumference of the user's wrist. The distal end of the urethane strap may be connected with Velcro or hook and loop elastic fabric strap looped around a D-ring on one side and then attached back to itself. In this embodiment, the closure mechanism may allow the user to make tape length adjustments indefinitely (unlike indexing 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 (e.g., if it is worn or otherwise undesirably worn before the useful life cycle of the device is terminated). In one embodiment, the Velcro or fabric may 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 a necklace, chest strap, bra, adhesive patch, glass, ear cord or toe strap (toe band). Such biometric monitoring devices may be built in as follows: portions of the sensor package/biometric monitoring device are removable and may be worn in any number of ways including, but not limited to, those listed above.

In another embodiment, embodiments of the biometric monitoring device of the present invention can be worn to clip to an article of clothing or stored in an article of clothing (e.g., a pocket) or decoration (e.g., a handbag, backpack, purse). Because such biometric monitoring devices may not be close to the user's skin, in embodiments that include heart rate measurements, the measurements may be obtained 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)) by the user manually placing the device in a particular mode (e.g., by pressing a button, covering a capacitive touch sensor with a fingertip, etc., possibly with a heart rate sensor embedded in the button/sensor).

User interface with device

Some embodiments of the biometric monitoring device may include functionality for one or more methods for allowing interaction with the device, either locally or remotely.

In some embodiments, the biometric monitoring device may visually communicate data via a digital display. A physical embodiment of such a display may use any one or more display technologies, including but not limited to one or more of the following: LEDs, LCDs, AMOLEDs, electronic inks, clear display technologies, graphic displays, and other display technologies, such as TN, HTN, STN, FSTN, TFT, IPS, and OLET. Such a display may show data acquired or stored locally on the device or may display data acquired remotely from other devices or internet services. The biometric monitoring device may use a sensor (e.g., an ambient light sensor "ALS") to control or adjust the amount of screen backlighting (if backlighting is used). For example, in a dark lighting scenario, the display may be dimmed to save battery life, while in a bright lighting scenario, the display brightness may be increased to make it easier to read by the user.

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

In some embodiments, the use of an electronic ink display may allow the display to remain on without battery drain of the non-reflective display. This "always on" functionality may provide an enjoyable user experience in the case of, for example, a watch application where the user may simply glance at the biometric monitoring device to see the time. The electronic ink display always displays content without compromising the battery life of the device, allowing the user to see the time (as it would on a conventional watch).

Some implementations of the biometric monitoring device may use light, such as LEDs, to display the user's heart rate (by modulating the amplitude of light emitted at the frequency of the user's heart rate). The device may depict heart rate zones (e.g., aerobic, anaerobic, etc.) via the color of the LED (e.g., green, red) or a series of LEDs that light up as the heart rate changes (e.g., progress bars). The biometric monitoring device may be integrated or incorporated into another device or structure, such as glass or goggles, or communicate with the 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 a device is to use a vibration-induced motor. The device may use this method alone or in conjunction with a plurality of other motion-inducing techniques.

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

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

● the user needs to wake up at a particular time

● the user should wake up when he is 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 in a preselected time window bounded by the earliest and latest times the user wants to wake up.

● receiving the E-mail

● the user has been inactive for a certain period of time. Notably, this may be integrated with other applications such as a meeting calendar or sleep tracking application to plan, streamline, or adjust the behavior of inactive reminders.

● the user has been active for a certain period of time

● A user has an appointment or calendar event

● the user has reached a certain activity metric

● the user has walked a certain distance

● the user has reached a certain number of miles

● the user has reached a certain speed

● the user has accumulated up to a certain altitude

● the user has walked a certain number of steps

● user has recently made heart rate measurements

● user's heart rate has reached a certain level

● the user has a normal, active, or resting heart rate that is a particular value or in a particular range

● the heart rate of the user has entered or exited a certain target range or training zone

● the user has a new heart rate "zone" target to be reached, in this case a heart rate zone trained for running, cycling, swimming, etc. activities

● the user has swim one single trip or completed a certain number of single trips in the pool

● the external device has information that needs to be communicated to the user, such as an incoming telephone call or any of the above reminders

● 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 sensors, and/or respiratory data

These examples are provided for illustration and are not intended to limit the scope of information that may be communicated by such embodiments of the biometric monitoring device (e.g., to a user). Note that the data used to determine whether the alert condition is satisfied may be obtained from the first device and/or one or more secondary devices. The biometric monitoring device itself may determine whether the criteria or conditions for alerting have been met. Alternatively, a computing device (e.g., a server and/or mobile phone) in communication with the biometric monitoring device may determine when an alert should occur. In view of the present disclosure, one skilled in the art may envision other information that the biometric monitoring device may communicate to the user. For example, the biometric monitoring device may communicate with the user when the target has been met. The criteria for meeting this goal may be based on physiology, context, and environmental sensors on the first device and/or other sensor data from one or more auxiliary devices. The target may be set by a user or may be set by the biometric monitoring device itself and/or another computing device (e.g., a server) in communication with the biometric monitoring device. In an example embodiment, the biometric monitoring device may vibrate when the biometric target 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 the secondary device in real time. For example, such biometric monitoring devices may be capable of communicating with a mobile phone via bluetooth low energy in order to give real-time feedback to the user of heart rate, heart rate variation, and/or stress. Such biometric monitoring devices may train or permit the user to breathe "at a time" in a particular manner that relieves stress (e.g., by taking a slow deep breath). Stress may be quantified or assessed via changes in heart rate, heart rate variation, skin temperature, athletic activity data, and/or electrodermal response.

Some embodiments of the 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 movement into commands for the device. Such motions may include, but may not be limited to, touchdown, turning the wrist, bending one or more muscles, and swinging arms. Another method of user input may be via the use of buttons such as, but not limited to, capacitive touch buttons, capacitive 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 may always sample and be ready to respond to any gesture or input without an intervening event, such as pushing a physical button. Such biometric monitoring devices may also be input via the use of audio commands. All of these input methods may be integrated locally into the biometric monitoring device or into a remote device that may communicate with such biometric monitoring devices via a wired or wireless connection. Further, the user may also be able to manipulate the biometric monitoring device via a remote device. In one embodiment, such a remote device may have internet connectivity.

Alarm device

In some embodiments, the biometric monitoring device of the present invention can act as a wrist mounted vibration alarm to silently wake the user from sleep. Such biometric monitoring devices may track a user's sleep quality, wake-up period, sleep delay, sleep efficiency, sleep stages (e.g., deep sleep and REM), and/or other sleep-related metrics via one or a combination of heart rate, heart rate variation, electrodermal response, motion sensing (e.g., accelerometer, gyroscope, magnetometer), and skin temperature. The user may specify a desired alarm time or time window (e.g., set the alarm to sound at 7 am and 8 am). Such embodiments may use one or more of the sleep metrics to determine an optimal time within the alarm window to wake the user. In one embodiment, when the vibratory alarm is active, the user may cause it to fade or turn off by tapping or tapping the device (which is detected, for example, via a motion sensor, pressure/force sensor, and/or capacitive touch sensor in the device). In one embodiment, the device may attempt to wake the user at the optimum moment in the sleep cycle by starting a small vibration at the particular user's sleep stage or at a time before the alarm setting. Which may gradually increase the intensity or noticeability of the vibration as the user progresses towards arousal or towards an alert setting. (see, e.g., FIG. 8).

Fig. 8 illustrates the functionality of an example portable biometric monitoring device smart alarm feature. The biometric monitoring device may be capable of detecting or may communicate with a device that may detect a sleep stage or state of the user (e.g., light or deep sleep). the user may set a window of time (e.g., 6:15 am to 6:45 am) during which the user would like to wake.

The biometric monitoring device may be configured to allow the user to select or create an alert vibration pattern of their choice. The user may be able to "snooze" or delay the alarm event. In one embodiment, the user may be able to set the amount of delay for the "doze" 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 dozing 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. Thus, it may press the nap 3 times to delay the alarm for 5 minutes each time it presses the nap to delay the alarm. In such embodiments, the snooze function does not turn off the alarm if the user attempts to press the snooze a fourth time.

Some biometric monitoring devices may have information about the user's calendar and/or schedule. The user's calendar information may be entered directly into the biometric monitoring device or it may be downloaded from a different device, such as a smartphone. This information can be used to automatically set an alarm or alarm characteristic. For example, if the user is about to take 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 and/or arrive at the meeting. The biometric monitoring device may determine the amount of time required for the user 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 reach the location of the meeting from the user's current location. Alternatively, historical data regarding the time it takes for a user to arrive at a meeting location and/or prepare to depart for the meeting (e.g., the time it takes to wake up in the morning, shower, eat breakfast, etc.) may be used to determine when to wake up the user. Similar functionality may be used for calendar events other than meetings, such as eating times, sleeping times, snooze times, and workout 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 may supplement the calendar information described herein. The user may have a goal of the approximate number of hours of sleep that they desire overnight or weekly. The biometric monitoring device may set the morning alarm at an appropriate time for the user to meet these sleep goals. In addition to the amount of sleep time the user desires overnight, other sleep goals that the user may 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 may be used by the biometric monitoring device to determine when to set an alarm in the morning. In addition, the user may be reminded at night when he should go to bed to meet his sleep goals. Further, the user may be reminded during the day when they should fall asleep to meet their sleep goals. The time at which the user is reminded that he should fall asleep may be determined by factors that optimize the quality of sleep of the user during the fall asleep, subsequent fall asleep, or night sleep. For example, if a user sleeps little early in the morning, the user may have a hard time to fall asleep at night. The user may also be advised to eat or avoid certain foods or beverages to optimize their sleep quality. For example, users may not be encouraged to drink near their bedtime because alcohol may reduce their sleep quality. The user may also be advised to perform certain activities or to avoid certain activities to optimize their sleep quality. For example, a user may be encouraged to exercise in the afternoon to improve their sleep quality. Users may not be encouraged to exercise or watch television near their bedtime to improve their sleep quality.

User interface with an auxiliary device

In some embodiments, the biometric monitoring device may transmit and/or receive data and/or commands to and/or from the secondary electronic device. The auxiliary electronic device may communicate directly or indirectly with the biometric monitoring device. Direct communication refers herein to data being transmitted between the first device and the auxiliary device without any intermediate device. For example, two devices may communicate with each other via a wireless connection (e.g., bluetooth) or a wired connection (e.g., USB). Indirect communication refers to the transmission of data between the first device and the auxiliary device by means of one or more intermediate third devices, which relay the data. The third device may include, but is not limited to, a wireless repeater (e.g., a WiFi repeater), a computing device such as a smartphone, laptop, desktop or tablet computer, cell phone tower, computer server, and other networked electronic devices. For example, the biometric device may send the 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 acting 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 serve as a user interface to the biometric monitoring device. The biometric monitoring device may send the biometric and other data to the smartphone in real time or with some delay. The smart phone may send one or more commands to the biometric monitoring device in real time or with some delay, e.g., to instruct it to send biometric and other data to the smart phone. For example, if the user enters a mode of tracking running in the application, the smartphone may send a command to the biometric device instructing it to send data in real-time. Thus, the user can track his run on his application as he proceeds without any delay.

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 a user launches or opens the application. On this page, a summary of the summary of data totals such as total steps, number of stairs climbed, miles traveled, calories burned, calories consumed, and water consumed may be shown. Other pertinent information may also be shown, such as the last time the application received data from the biometric monitoring device, metrics regarding sleep over the previous night (e.g., when the user went asleep, woken up, and the time of their sleep), and how many calories the user can eat during the day to maintain their calorie goals (e.g., a calorie deficit goal to achieve weight loss). The user may be able to select which of these and other metrics to present on the dashboard screen. The user may be able to see these and other metrics for the first few days on the dashboard. It may be able to access the first few days by pressing a button or icon on the touch screen. Alternatively, gestures such as a jog left or right may enable a user to navigate current and previous metrics.

The smartphone application may also have another page that provides a summary of the user's activities. Activities may include, but are not limited to, walking, running, cycling, cooking, sitting, working, swimming, business trips, lifting weights, commuting, and yoga. Metrics relevant to these activities may be presented on this page. For example, the bar graph may show the number of steps the user takes on different parts of the day (e.g., how many steps the user takes every 5 minutes or every 1 hour). In another example, the amount of time a user spends performing an activity and how many calories burned during that 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 over 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 for their activities over a shorter or larger span of time.

The smartphone application may also have an interface to log food that the user has eaten or will eat. This interface may have a keyword search feature to allow users to quickly find out the food they wish to type into their log. Alternatively or in addition to searching for food, the user may be able to find the food to be logged by navigating a menu or series of menus. For example, the user may select the following series of categories: breakfast/cereal/health/oatmeal to reach the food they wish to log (e.g., apple flavored oatmeal). At any of these menus, the user may be able to perform a keyword search. For example, the user may search for "oatmeal" after having selected the category "breakfast" to search for the keyword "oatmeal" within the category of breakfast food. After having selected the food that it will wish to log, the user may be able to modify or enter the serving size and nutritional content. After at least one food item has been logged, the application may display a summary of the logged food item and the nutritional content (individual and total calorie content, vitamin content, sugar content, etc.) of the food item for a period of time (e.g., days).

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

The smartphone application may also have a page that allows the user to type in how much water the user has consumed. Each time the user drinks some of the water, he may type the amount in units of his choice (e.g., ounces, cups, etc.). The application may display the total amount of all water that the user has logged over a certain period of time (e.g., one day). The application may allow the user to view previously logged water entries and the previous and total number of days of the day.

The smartphone application may also have a page that displays the user's online friends. This "friends" page may enable the user to add or request new friends (e.g., by searching for their names or by their email addresses). This page may also display a leader board (leaderboard) for the user and their friends. Users and their friends may be ranked based on one or more metrics. For example, the user and their friends may be ranked using the total number of steps over the last seven days.

The smartphone application may also have a page that shows metrics about the user's sleep in the previous night and/or the previous nights. This page may also enable the user to log when they have gone to bed and when they wake up. The user may also be able to subjectively measure about their sleep (e.g., poor night break, good night break, excellent night break, etc.). The user may be able to view these metrics for today or for a period of time in the past (e.g., two weeks). For example, the sleep page may default to a bar graph showing the amount of sleep time of the user every night for the last two weeks. The user may also be able to view a bar graph of the amount of sleep time of the user at each night of the last month.

The user may also be able to access the full capabilities of the smartphone application described herein (e.g., be able to enter a food log, view a dashboard, etc.) via an alternative or additional interface. 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 page may 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 device of the present invention may include a wireless communication mechanism 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, power line networking, and cellular network. These are provided as examples and should not be construed to exclude other existing wireless communication methods or protocols or wireless communication techniques or protocols not yet invented.

The wireless connection may be bidirectional. The biometric monitoring device may transmit, communicate, and/or push its data to other devices, such as a smart phone, a computer, etc., and/or the internet, such as a web server, etc. The biometric monitoring device may also receive, request and/or pull (pull) data from other devices and/or the internet.

The biometric monitoring device may act as a relay 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 a WLAN and equipped with an ANT radio. The ANT device may communicate with the biometric monitoring device to transmit its data to the internet (and vice versa) via the WLAN of the biometric monitoring device. As another example, the biometric monitoring device may be equipped with bluetooth. If a bluetooth enabled smart phone comes within range of the biometric monitoring device, the biometric monitoring device may transmit data to or receive data from the internet via the phone network of the smart phone. 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 network content for display on the biometric monitoring device. The following are typical examples of this:

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

2. Com, and/or historical profiles of user activity and/or consumed food and/or sleep data measured by other devices and/or stored remotely (e.g., at a website such as fitbit

3. Other users stored remotely track historical profiles of data. Examples include heart rate, blood pressure, arterial stiffness, blood glucose level, cholesterol, duration of watching television, duration of playing video games, mood, etc

4. Training and/or diet 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 progress toward heart rate, weight, activity, sleep, and/or other goals.

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

7. Comparison between the aforementioned data of a 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 (Facebook)

9. Other online content, such as newspaper articles, constellations, weather reports, RSS feeds, comic chains, crossword puzzles, classified advertisements, stock reports, and websites

10. Electronic mail message and calendar schedule

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 for the night before. In the evening, a daily summary of daytime activities may 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 so that when a user brings it into close proximity with a mobile phone, an application automatically launches on the mobile phone.

These examples are provided for illustration and are not intended to limit the range of data that may be transmitted, received, or displayed by a device or any intermediate processing that may occur during such communication and display. In view of the present disclosure/application, those skilled in the art can envision many other examples of data that may be streamed or transmitted via a biometric monitoring device.

Charging and data transmission

Some embodiments of the biometric monitoring device may use a wired connection to charge an internal rechargeable battery and/or to transfer data to a host device such as a laptop computer or mobile phone. In one embodiment similar to the embodiments discussed earlier in this disclosure, the biometric monitoring device may use a magnet to assist the user in aligning the biometric monitoring device to a mount or cable. The magnetic field of the magnet in the base or cable and the magnet in the device itself may be strategically oriented in order to force the biometric monitoring device to self-align with the base or cable (or more specifically, the connector on the cable) and provide a force to hold the biometric monitoring device in the base or to the cable. The magnet may also be used as a conductive contact for charging or data transmission purposes. In another embodiment, the permanent magnet 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 a magnet is present in the biometric monitoring device, the strong magnetic field of the nearby permanent magnet may make it significantly more difficult for the magnetometer to accurately measure the earth's magnetic field. In such embodiments, the biometric monitoring device may utilize ferrous materials in place of magnets, and magnets on the base or cable side may be attached to the ferrous materials.

In another embodiment, the biometric monitoring device may contain one or more electromagnets in the biometric monitoring device body. The charger or base for charging and data transmission may also contain electromagnets and/or permanent magnets. The biometric monitoring device may only turn on its electromagnet when it is close to the charger or cradle. The biometric monitoring device may detect proximity to the dock or charger by looking up the magnetic field signature of the charger or a permanent magnet in the dock using a magnetometer. Alternatively, the biometric monitoring device may detect the proximity of the charger by measuring a Received Signal Strength Indication (RSSI) of a wireless signal from the charger or base, or in some embodiments, by recognizing an NFC or RFID tag associated with the charger or base. When the device does not require charging, synchronization, or it has completed synchronization 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 (e.g., the charger or processor in the base may be configured via program instructions) to turn on the electromagnet when the biometric monitoring device is connected for charging (the electromagnet may generally remain on such that the biometric monitoring device placed on the charger is attracted against the charger by the electromagnet, or the electromagnet may remain off until the charger determines that the biometric monitoring device has been placed on the charger, such as via completion of a charging circuit, recognition of an NFC tag in the biometric monitoring device, etc., and then turn on to attract the biometric monitoring device against the charger) Upper), and the biometric monitoring device may cease to be drawn 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 a magnetic force generated by the electromagnet, the biometric monitoring device will drop from the charger or otherwise shift from a 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, the biometric monitoring device may include a communication interface that can switch between two or more protocols having 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 switch between using a bluetooth basic rate/enhanced data rate (BR/EDR) and a Bluetooth Low Energy (BLE) protocol in response to a determination made based on data from a sensor of the biometric monitoring device. For example, a lower power, slower BLE protocol may be used when sensor data from an accelerometer in the biometric monitoring device indicates that the wearer is asleep or otherwise sedentary. In contrast, when sensor data from an accelerometer in a biometric monitoring device indicates that the wearer is walking around, a higher power, faster BR/EDR protocol may be used. This adaptive data transmission technique and functionality is further discussed in U.S. provisional patent application No. 61/948,468, filed on 5/3/2014, which was previously incorporated by reference in the "cross-reference to related applications" section and again specifically incorporated by reference with respect to what is indicated at the adaptive data transfer rate in the biometric monitoring device.

Such communication interfaces may also serve as a form of sensor for a biometric monitoring device. For example, the wireless communication interface may allow the biometric monitoring device to determine the number and types of devices that are within range of the wireless communication interface. This data may be used to determine whether the biometric monitoring device is in a particular context, e.g., indoors, in an automobile, etc., and variously change its behavior in response to this determination. For example, as discussed in U.S. provisional patent application No. 61/948,468 (incorporated by reference above), such contexts may be used to drive the selection of a particular wireless communication protocol for wireless communication.

Configurable application functionality

In some embodiments, the biometric monitoring device of the present invention may comprise a watch-like form factor and/or a bracelet, armband or anklet form factor, and may be programmed with an "application" that provides particular functionality and/or displays particular information. The application may be started or closed by a variety of mechanisms, including but not limited to pressing a button, using a capacitive touch sensor, performing a gesture detected by an accelerometer, moving to a particular location or area detected by a GPS or motion sensor, compressing the biometric monitoring device body (thereby generating a pressure signal inside the device that is detectable by an altimeter inside the biometric monitoring device), or placing the biometric monitoring device in proximity to an NFC tag associated with an application or set of applications. The start or stop of an application may also be triggered automatically by certain environmental or physiological conditions, including but not limited to detecting a high heart rate, detecting water using a humidity sensor (to, for example, start a swimming application), certain time of day (to, for example, start a sleep tracking application at night, change in pressure and motion characteristics for a plane departure or landing to start and stop an "airplane" mode application, the application may also be started or stopped by simultaneously satisfying multiple conditions, for example, in another case where the accelerometer detects swimming and the user presses the same button, it may start a swimming one-way count application.

In some embodiments, the biometric monitoring device may have a swim tracking mode that may be initiated by launching a swim application. In this mode, the motion sensors and/or magnetometers of the biometric monitoring device may be used to detect swimming gestures, classify swimming gesture types, detect swimming one-way, and other relevant metrics such as stroke efficiency, one-way time, speed, distance, and calorie burn. The change in direction indicated by the magnetometer can be used to detect a variety of single turn methods. In a preferred embodiment, data from motion sensors and/or pressure sensors may be used to detect strokes.

In another embodiment, the cycling application may be launched by moving the biometric monitoring device into proximity with an NFC or RFID tag located on the bicycle, on a stand of the bicycle, or in a location associated with the bicycle, including but not limited to the bicycle frame or bicycle storage facility. (see, e.g., FIG. 10). The launched application may use a different algorithm than that typically used to determine metrics including, but not limited to, calories burned, distance traveled, and altitude attained. The application may also be launched upon detection of a wireless bicycle sensor (including but not limited to a wheel sensor, GPS, cadence sensor, or power meter). The biometric monitoring device may then display and/or record data from the wireless bicycle sensor or bicycle sensor.

Additional applications include, but are not limited to, programmable or customizable hand surfaces, stop-watch, music player controllers (e.g., MP3 player, remote control), text message and/or email displays or annunciators, navigation compasses, cycle computer displays (when in communication with a separate or integrated GPS device, wheel sensors, or power timing), weight lifting trackers, sit-up trackers, pull-up trackers, resistance training forms/fitness trackers, golf swing analyzers, tennis (or other racquet-like motion) swing/service analyzers, tennis game swing detectors, baseball swing analyzers, ball throwing analyzers (e.g., football, baseball), organized sports activity intensity trackers (e.g., football, baseball, basketball, tennis, rugby), throw disc analyzers, food bite detectors, music player controllers (e.g., MP3 player, remote control, power meters), weight lifting trackers, sit-up trackers, pull-up trackers, resistance training forms/fitness trackers, golf swing analyzers, tennis swings, tennis (or other racquet-like motion), and/service analyzers, Typing analyzers, tilt sensors, sleep quality trackers, alarm clocks, pressure gauges, stress/relaxation biofeedback games (e.g., potentially in conjunction with mobile phones that provide auditory and/or visual cues to train the user's breathing in relaxation training), tooth brushing trackers, eating rate trackers (e.g., counting or tracking the rate and duration of physical intake of appliances into a mouth), driving a motor vehicle intoxication or fitness indicators (e.g., via heart rate, heart rate variation, electrodermal response, gait analysis, puzzle solving, etc.), allergy trackers (e.g., using electrodermal response, heart rate, skin temperature, pollen sensing, etc. (possibly tracked in conjunction with external seasonal allergens from, e.g., the internet and possibly determining the user's response to a particular form of allergen (e.g., pollen), and alerting the user to the presence of such allergens, such as from seasonal information, pollen tracking databases, or local environmental sensors in a biometric monitoring device or used by the user), fever tracker (e.g., to measure the risk, onset, or progression of fever, cold, or other ailments, perhaps in conjunction with seasonal data, ailment databases, user location, and/or user-provided feedback to assess the spread of a particular ailment (e.g., flu) with respect to the user, and perhaps in response to indicate or suggest a restriction in work or activity), electronic games, caffeine impact tracker (e.g., to monitor activity such as heart rate, heart rate variability, galvanic skin response, skin temperature, blood pressure, stress, sleep, and/or in short-term or long-term responses to ingestion or restriction of coffee, tea, energy drinks, and/or other caffeine-containing drinks), medication impact tracker (e.g., similar to the previously mentioned caffeine tracker but with respect to other interventions, whether it is a medical or lifestyle medication, such as alcohol, tobacco, etc.), endurance exercise training (e.g., recommending or indicating an intensity, duration, or running/cycling/swimming fitness profile, or suggesting a abstinence or delay of fitness, goals tailored according to user-specified goals such as marathon, triathlon, or using data from, for example, historical exercise activities (e.g., running distance, stride), heart rate variation, health/illness/stress/fever status), weight and/or body composition, blood glucose, food intake, or calorie balance tracker (e.g., informing the user how many calories they can consume to maintain or achieve a certain weight), pedometer, and fingernail-biting detector. In some cases, the application may rely solely on the processing power and sensors of the present invention. In other cases, the application may fuse or only display information from an external device or set of external devices, including but not limited to a heart rate strap, a GPS distance tracker, a body composition scale, a blood pressure monitor, a blood glucose monitor, a watch, a smart watch, a mobile communication device such as a smartphone or tablet, or a server.

In one embodiment, the biometric monitoring device may control a music player on the secondary device. Aspects of the music player that can be controlled include, but are not limited to, volume, selection of tracks and/or playlists, fast forward or backward (skip forward or backward), fast forward or rewind of tracks, speed of tracks, and music player equalizer. The music player may be controlled via user input or automatically 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 via a user interface on the biometric monitoring device. In another example, the biometric monitoring device may automatically select an appropriate track based on the activity level of the user, which is calculated from the biometric monitoring device sensor data. This may be used to help motivate the user to maintain a certain activity level. For example, if the user is running continuously and wants to keep their heart rate in a certain range, the biometric monitoring device may play an amusing or higher speed track (if the user's heart rate is below their target range).

Automatic function triggered by user's activity

Sleep stage trigger functionality

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

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, it may be transmitted to a cloud system, a home server, or a host control unit that is wirelessly connected to the communication-enabled electrical device (via Wi-Fi, bluetooth, or NFC). Alternatively, the biometric monitoring device may communicate directly with an electrical device having a communication function. Such electrical equipment with communication functions may include, for example, kitchen electrical equipment, such as microwave ovens, coffee grinders/makers, ovens, and the like.

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

Reminder detection

An alert (e.g., a low alert) can be correlated to a person's drowsiness, the alert can also be detected from the biometrics listed above, and the alert can be used to trigger an electrical device, such as a coffee maker, to begin automatically brewing coffee.

Hydration action

The portable biometric monitoring device, in combination with the activity level tracker, can submit the activity level of the user directly to the cloud system, the home server, the master control unit, or the electrical device. This may trigger some actions of the electrical apparatus, in particular actions related to hydration, such as starting ice making in a refrigerator, or lowering the operating temperature of the water purifier.

Power saving

Many electrical devices are typically idle operating at low power that consumes power. Using aggregated information of a biometric signal of a user, an electrical device having a communication function may be caused to enter an ultra-low power mode. For example, a water dispenser in a home may shut itself off to an ultra-low power mode while a user is asleep or out for work, and may begin cooling/heating water once user activity in the home is anticipated.

Restaurant recommendation system based on location and activity

Aggregation of real-time biometric signals and location information may be used to generate educated guesses about the needs of one or more users at a given time (e.g., ionic beverages). Combining this presumed need with historical user data regarding the user's activity level, activity type, activity time and activity duration, and the user's logged food intake data, the application on the smart phone and/or smart watch may recommend restaurants that will meet the user's lifestyle and current needs.

For example, a user who has just completed a six mile tour may launch this application. The application may know that this person has maintained a high level of activity for the past hour, and thus determine that the person is likely to be dehydrated. From historical user data, the application may also know, for example, that there are too many vegetables in the user's diet but 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 may recommend, for example, a restaurant that offers smoothie (smoothie).

Swimming 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), location services sensors (e.g., GPS, wireless triangulation), and/or temperature sensors. The sensors may be embedded in a single device, such as a wrist. In other embodiments, additional sensor devices may be attached to the swimmer's forehead, back of head, goggles, back, hips, shoulders, thighs, legs, and/or feet.

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

● Stroke count detection-providing a stroke count per single pass, where a single pass is defined as a single pass from one end of the cell to the opposite end.

● stroke type classification-describing the user's swimming stroke type (e.g., crawling, breaststroke, backstroke, butterfly, sideways stroke, kick with stroke, body stroke, etc.), and may be any one or combination of:

a. classification of each stroke taken by a user

b. The classification of the primary stroke type used for each complete single pass.

c. Classification of stroke type used per fractional single pass (e.g. one-half single pass freestyle, one-half single pass breaststroke)

● one-way count-count the single pass a user passes through. One way to determine a single pass is by detecting when the user turns around in the pool.

Turn is defined as a 180 degree change in heading. Upon detection of a turn, the start and end of a single pass may be inferred. Pausing (no motion for a certain period of time) at a point in the pool (usually at one end or elsewhere) before swimming is started again is also considered a turn as long as the direction of progress following is opposite to that before pausing.

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

Algorithm structure

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

Sequential method (see FIG. 15A)

In one embodiment, the raw and/or preprocessed sensor signals may first be analyzed by a swipe detector algorithm. The stroke detector algorithm may use a time peak (local maximum and/or local minimum) in a motion sensor (e.g., accelerometer, gyroscope) as an indication that a stroke has been taken. Then, one or more heuristic rules may also be applied to remove peaks that do not represent strokes. For example, the magnitude of a peak, the temporal distance of two adjacent peaks, the peak-to-peak amplitude, and/or the morphological characteristics (e.g., sharpness) of the peaks may indicate that certain peaks do not represent a stroke. When the sensor provides more than one dimensional data, such as a 3-axis accelerometer, or a 3-axis motion sensor + altimeter (totaling 4-axis data), the timing and relative magnitude of the peaks on all axes may be considered to determine whether the peaks on one or more of the axes were generated by a stroke.

If a single peak representing a stroke or a cluster of peaks from multiple data axes representing strokes is observed, features may be extracted from the data segments obtained at the time 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 section, power measured in various metrics (e.g., L1 power and L2 power, standard deviation, mean), and so forth. The extracted features may then be subjected to a machine learning system, where system coefficients are calculated offline (supervised learning) or adapted when the user uses the biometric monitoring device (unsupervised learning). The machine learning system may then return stroke classifications for each detected stroke.

The turn-around detector algorithm may search for sudden changes in motion by calculating derivatives, moving averages, and/or using high-pass filtering of the signals of 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 differs from the next, then a turn-around may be determined to have occurred. Whole or partial coefficients of a transform, such as a Fast Fourier Transform (FFT), may also be used as a feature. Parametric models such as Autoregressive (AR) models may also be used. The time-varying model parameters may then be estimated using Linear Predictive Analysis (LPA), least mean square filtering (LMS), recursive least squares filtering (RLS), and/or kalman filtering. The estimated model parameters are then compared to determine if there is a sudden change in their values.

In one embodiment, the skill level and/or swimming style (e.g., speed) of the swimmer may be inferred from the sensor data and then used for turn detection. For example, advanced swimmers typically have more forceful strokes (i.e., large accelerometer peak magnitude), and take fewer strokes to complete a single pass. Thus, a metric that estimates the skill level or characteristics of the swimmer may be used in the turn detection algorithm. These metrics may include, but are not limited to, an average motion signal or an integrated motion signal in the motion signal, in particular, arm movement, estimated forward speed, and detection mode of advanced swimmers. The skill level or other characteristics of the swimmer may also be determined via user input. For example, the user may input that they are senior, middle or beginner swimmers.

One or many (combined) features from these analyses may be used to detect whether a given data sample and/or an adjacent data sample has turn-around characteristics. To obtain the best combination of features and decision boundaries, machine learning techniques such as logistic regression, decision trees, neural networks, etc. may be utilized.

In some embodiments, if a turn is detected, the swim data naturally increases from what can be summarized in previous turns, such as the number of strokes, the type of strokes used for each stroke and for a single pass, the segment time, and so forth. If a turn is not detected, the swipe counter and type can be updated. Unless the user stops swimming, the algorithm may revert to swipe count detection.

Parallel method (see FIG. 15B)

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

In such embodiments, two functional components may be implemented in a single algorithm that detects stroke types and turns simultaneously: stroke type and turn detection. For example, classifiers of a swim gesture type (e.g., detecting movement analysis of freestyle, breaststroke, backstroke, butterfly) and a turn type (e.g., rolling turn (turn), front rolling turn (flip turn), two-handed touch) may return a detected stroke type or a detected turn type. During detection, temporal as well as spectral features may be extracted. The moving window may be applied to multiple data axes first. Statistics for this window section may then be calculated, i.e., maximum and minimum values, number of ripples in the section, power measured in various metrics (e.g., L1 power and L2 power, standard deviation, mean). Independent Component Analysis (ICA) and/or Principal Component Analysis (PCA) may be applied as well to find any hidden signal that better represents the turn-around type and the swipe type characteristics. The temporal characteristics can then be calculated from this (potentially improved) signal representation. For temporal features, various non-parametric filtering schemes, low-pass filtering, band-pass filtering, high-pass filtering may be applied to enhance the desired signal characteristics.

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

Moving average windows of different lengths may run in parallel and provide the features listed above, and all or part of the features may also be used as features.

Machine learned coefficients (supervised learning) may then be applied to these extracted features. One or more machine learning techniques, i.e., multiple layers of binomial linear discriminant analysis (e.g., logistic regression), polynomial logistic regression, neural networks, decision trees/forests, or support vector machines, may be trained and then used.

As the window of interest moves, features may be extracted, and these newly extracted features will return stroke types or detected turns via the machine learning system.

The stroke detector algorithm may run in parallel independent of stroke type and turn detection. The time peaks of the raw or pre-filtered sensor signal may be detected and selected by heuristic rules.

In the generalization phase of the algorithm, where metrics on swimming can be determined, displayed and/or stored, post-processing can be applied to the sequence stroke type and turn detection. If a turn is confirmed with some confidence, the swim metric data from the previous turn may be summarized along with the detected stroke count. If a turn around is not confirmed, the moving average window may proceed. Until the user stops swimming, the algorithm may continue to update swimming metrics for the user's workout, including but not limited to the total number of turns, total number of single strokes, total number of strokes, average number of strokes per single stroke, number of strokes in the last single stroke, change in number of strokes per single stroke, etc.

Mixing method (see FIGS. 15C and 15D)

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

The stroke type detection may return the stroke type via machine learned coefficients. A segment of the sensor signal that is desirable for the first moving window. Features can then be extracted, either as a whole or as a subset of the moving window features listed herein. Machine learning coefficients, trained offline, may then be applied to the features to determine which stroke type produced a given segment of the sensor signal.

The stroke count detection may run simultaneously with the stroke type detection.

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

If a turn is detected, the completion of a lap may be recorded in the user's swimming summary metric. A post process may be applied to the detected stroke types to determine the most prominent stroke type of the completed circle. 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 circle until a turn is detected.

Blood glucose level and heart rate

Biometric monitoring devices that continuously measure biometric signals can provide meaningful information about the pre-disease condition, progression, and recovery of the 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, number of steps taken, calories burned, distance traveled, weight and body fat, activity intensity, activity duration and frequency, and the like. 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, particularly before and after a food intake event. Heart rate is known to be affected by blood glucose levels, and it is well known that high blood glucose levels are 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, wherein data is collected from normal, pre-diabetic and diabetic individuals to provide a corresponding mathematical model. By means of the mathematical model it can be predicted whether an individual with a specific heart rate pattern is healthy, pre-diabetic or suffering from diabetes.

With knowledge of many heart failures associated with pre-diabetic or diabetic conditions, it is possible to further inform the user of the biometric monitoring device of possible heart failures at such risks, such as coronary heart disease, cerebrovascular disease, and peripheral vascular disease, etc., based on the user's biometric data.

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

If a user has set his family members as his friends at a social networking site where biometric data is stored and displayed, the likelihood of the family members getting the disease may also be analyzed and the user informed of the results.

In addition to informing the user of the potential development of the disease, the user may be provided with a recommended lifestyle including exercise regimens and a recipe with healthier ingredients and transmission methods.

Unification of grocery shopping, cooking and food logging

Grocery store organization and menu identification system

Receipts from grocery shopping can contain rich information, particularly about the individual's eating habits. For example, presented herein is a novel system that combines information from grocery receipts with biometric data of an individual as collected by a biometric monitoring device. The system may collect and analyze data (information) about an individual and may then recommend options that may alter the individual's lifestyle in order to improve their health status. Implementation of such a 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 grocery list (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) that may also store the user's biometric data. When a user arrives home and organizes items in their refrigerator and/or pantry, an application on their smartphone/watch may recommend which items in the pantry or refrigerator to discard based on historical data about the food items (e.g., if the food items expire or may go bad). A reminder indicating that the food has expired or should be consumed in a short period of time to avoid spoilage may be automatically sent to the user independent of such interaction. For example, these alerts may be issued to the user whenever a certain threshold has been met (e.g., within two days when milk will expire). The reminder may also be sent to the user by a mechanism other than through a smartphone/watch. For example, the reminder may be presented to the user through a network interface, through email, through a reminder on a laptop computer, tablet computer, desktop computer, or any other electronic device in direct or indirect communication with a computer that maintains and/or analyzes the food database.

The application may recommend recipes to the user using the updated list of food items and based on the user's historical food consumption data. In one embodiment, a recipe using items that should be eaten first (e.g., before they expire, go bad, or become fresher than other ingredients) may be given priority. The application may also analyze the user's activity data in order to recommend an optimal recipe that is nutritionally balanced, correctly distributed, and tailored to the user's activity. For example, if the user lifts weight 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 strategies may 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 takes eggs from a grocery store and another member of the family takes milk, both the eggs and the milk will be represented in the food database. Similarly, nutritional preferences (e.g., vegetarians, allergies to certain foods, etc.), activities, basal metabolic rates, and total calorie burn may be used to form recommendations for what food/recipe to prepare and/or purchase.

Biometric signals, including but not limited to heart rate and heart rate variability, can provide an indication of the advance condition of the disease. This information can be used to recommend that a user buy, consume and/or prepare a particular food product in order to reduce their risk of getting a disease for which they have an advance condition. For example, if a user has advance conditions of heart problems, they may recommend that they buy more vegetables, consume less fat-containing food, and prepare the food in a process that requires less oil (e.g., not deep fried).

Control 'intelligent household electrical appliance'

In another embodiment, the various appliances may all be Wi-Fi enabled and may communicate with a server. Since an 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 temperature of the refrigerator depending on the food items. For example, if many food items are more sensitive to cold, such as vegetables, a refrigerator may be instructed to increase temperature. The application may also communicate directly with the refrigerator via bluetooth, BTLE or NFC as well.

Food record

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

When a user records a food item being cooked by following the recipe suggested by the application, the application may calculate nutritional information from the ingredients and cooking program. This may provide a more accurate estimate of calorie intake than the simple organization of the end product/diet, as many recipes exist to prepare a particular type of food, e.g., pasta-type meatballs may be made with beer, turkey, pork, etc., and the meatballs may contain varying degrees of carbohydrate.

Motion metric acquisition using sensor devices

In some embodiments, the sensor may be mounted on a racket, such as a tennis racket, to facilitate measuring the different strokes of the player. This is applicable to most, if not all, racquet sports, including but not limited to tennis, racquetball, squash, table tennis, badminton, lacrosse, etc., and sports played with a bat, such as baseball, softball, cricket, etc. Similar techniques may also be used to measure different aspects of golf. Such devices may be mounted on the bottom of the racquet, on the handle, or on the shock absorber, which is typically mounted on a string. Such a device may have various sensors, such as an accelerometer, gyroscope, magnetometer, strain sensor, and/or microphone. Data from these sensors may be stored locally or transmitted wirelessly to a host system or other wireless receiver on the smartphone.

In some embodiments of the biometric monitoring device, a wrist-mounted biometric monitoring device including an accelerometer, gyroscope, magnetometer, microphone, etc., may perform similar analysis of the user's game or motion. This biometric monitoring device may take the form of a watch or other band worn on the user's wrist. A bat or bat mounted sensor that can measure or detect the moment of impact between a bat or bat and a ball and wirelessly transmit this data to a wrist mounted biometric monitoring device can be used to improve the accuracy of such algorithms by accurately measuring the time of impact with the ball.

The wrist and paddle/bat mounted device may help measure different aspects of a user's game, including, but not limited to, stroke type (forward, backstroke, serve, angle), number of forward hits, number of backstroke, ball spin direction, spin, percentage serve, angular velocity of the head, kick, stroke energy, stroke consistency, etc. A microphone or strain sensor may be used in addition to an accelerometer to identify the moment at which the ball strikes the racket/bat. In cricket and baseball, such devices can measure kickback, angular velocity of the bat at impact, number of hits on the offside leg (baseball). The number of swings and lost balls and the number of defensive pairs of offensive strokes may also be measured. Such devices may also have a wireless transmitter to transmit this statistical data to the scoreboard in real time or to an individual device held by the audience.

The wrist or racket mounted device may have a small number of buttons (e.g., two) that can be used by the player to indicate when a tennis ball wins or when an unforced mistake has occurred. This would allow the algorithm to calculate the winner as well as the score of unforced mistakes as a positive-to-counter hit. The algorithm may also track the number of directly scored serve versus double serve misses in tennis balls. If two players use such a system, the system may also automatically track scores.

ECG based on bicycle handlebar

In some embodiments of the biometric monitoring device, the heart rate of the user may be monitored using electrodes in contact with the left hand and electrodes in contact with the right hand (e.g., ECG heart rate measurements). This particular activity is well suited to use ECG techniques to track the user's heart rate, since cycling requires the user to touch either side of the handlebar with the hands. By embedding the electrodes in the handle bar or handle bar grip or string ribbon, the heart rate of the user can be measured each time the user holds the handle bar. For bicycles having grips (as opposed to using a handle cord strap), the electrodes may be incorporated into special grips that can be used to replace existing grips (e.g., generally non-conductive factory-installed grips). The left and right grips may be electrically connected to electronics that measure the ECG signal, for example, using wires. Where the handle bars are themselves electrically conductive, the handle bars may be used to electrically connect one of the grips to the electronics that measure the ECG signal. The electronics that measure the ECG signal may be incorporated into one or both of the grips. Alternatively, the electronics that measure the ECG signal may be located in a separate housing. In one embodiment, this separate housing may be mounted on the bicycle handle bar or stem. It may have the functions and sensors (e.g., speed sensor, cadence sensor, GPS sensor) that a typical cycle computer has. 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 may use the techniques described in this disclosure to calculate activity metrics, including but not limited to calorie burn, and transmit these metrics to secondary and tertiary devices (e.g., smartphones and servers).

The electrodes of the ECG may be incorporated into portions or accessories of the bicycle, rather than into the grip straps and handle bar grips, such as into gloves, brake covers, brake levers, or the handle bars themselves. These electrodes or additional electrodes may be used to measure GSR, body fat, and hydration in addition to or instead of heart rate. In one example, the heart rate of the user may be measured using conductive filaments (used as ECG electrodes) sewn into a grip cord strap mounted on the handle bar. The grip cord strap electrodes may be connected to a central cycle computer unit containing electronics to measure GSR, hydration, and/or heart rate. The biometric monitoring device may display this information on the 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. Where the cycle computer measures only one or both of GSR, hydration, or heart rate, an algorithm may be used to estimate a metric that cannot be directly measured. For example, if the biometric monitoring device can only measure heart rate and exercise duration, a combination of heart rate and exercise duration can be used to estimate hydration and remind the user when they should drink water. Similarly, the heart rate and exercise duration may be used to alert the user when the user should eat or drink something other than water (e.g., sports drinks).

Indirect metric estimation

Cycle computers typically measure a variety of metrics, including but not limited to speed, cadence, power, and wind speed. These and other metrics may be inferred using sensors that the portable biometric monitoring device has in the event that the portable monitoring device does not measure these metrics or communicate with a device that may be capable of supplying these metrics. In one embodiment, the portable biometric monitoring device may measure heart rate. It may use this measurement to infer/estimate the amount of power that the user is outputting. Other metrics such as age, height, and weight of the user may help inform the power measurement. Additional sensor data such as GPS measured speed, elevation increase/decrease, bicycle attitude (to measure inclination or slope of a slope), and accelerometer signals may be used to further inform the power estimation. 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, a calibration phase (e.g., a power meter) may occur where a user takes data from a portable biometric monitoring device and a secondary device that may be used as a baseline during calibration but not used at a later time. This may allow for a determination of a relationship between sensor data measured by the portable monitoring device and sensor data measured by the secondary device. This relationship may then be used when there is no secondary device to calculate an estimate of the data that is provided by the secondary device, but not explicitly by the biometric monitoring device.

Activity-based automatic scheduling

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

In another embodiment, a walking/running/wellness route may be suggested to a user based (in whole or in part) on the user's proximity to the user. The 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 guided to a preferred busy route or quiet route. Knowing the heart rate and basic health information about other users may allow the system to suggest a route to match the user's fitness level and desired exercise/effort level. This information can again be used to plan/guide longer term activity/health goals to the user.

Position/background sensing and applications

By one or more methods, embodiments of biometric monitoring devices disclosed herein have sensors that can determine or estimate the location or context of the biometric monitoring device (e.g., in a bus, at home, in a car). Dedicated location sensors may be used, such as GPS, GLONASS or other GNSS (global navigation satellite system) sensors. Alternatively, a lower precision sensor may be used to infer, estimate, or guess a location. In some embodiments where it is difficult to know the user's location, the 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 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 may present a query to the user asking the user whether they are riding a bus or a car today. Similar queries may be made for locations other than the background of the vehicle. For example, if the sensor data indicates that the user has completed strenuous exercise, but there is no location data indicating that the user is going to a gym, the user may be asked whether they are going to the gym today.

Vehicle transport detection

In some embodiments, sensors of the 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 (if any) the user is or was in. 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 particular network protocols such as WiFi or bluetooth may be used in the following description, and one or more alternative protocols such as RFID, NFC, or cellular phones may also be used.

In one embodiment, detection of a bluetooth device associated with a vehicle may be used to infer that a 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 bluetooth identification of the multimedia system and assume that the user is in the car. Data from other sensors may be used to corroborate the user's assumption in the vehicle. Examples of users in a car that may be confirmed using data or signals from other sensors include GPS velocity measurements above 30mph and accelerometer signals as a characteristic in a car. Information inherent to the bluetooth ID may be used to determine that it is a Wi-Fi router of the vehicle or a vehicle type. For example, the bluetooth ID of a router in an automobile may be "multimedia in an audi car". The keyword "Audi" or "car" may 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, the database of bluetooth IDs and their associated vehicles may be created or updated by a user of the biometric monitoring device or by portable communication device data. This may be done with the aid of user input and/or without the aid of user input. In one embodiment, if the biometric monitoring device can determine whether it is in a vehicle, a vehicle type, or a particular vehicle without using a bluetooth ID and it encounters a bluetooth ID that moves with the vehicle, it can send the bluetooth ID and information about the vehicle to a central database to be catalogued as the bluetooth ID corresponding to the vehicle. Alternatively, if a user enters information about a vehicle they are or were in at a previous point in time and there is a bluetooth ID encountered during or near the time that the user indicated that they were in the vehicle, the bluetooth ID and vehicle information may be sent to a central database and associated with each other.

In another embodiment, detection of a Wi-Fi device associated with a vehicle can be used to infer the user is in that vehicle or type of vehicle. Some trains, buses, planes, cars, and other vehicles have Wi-Fi routers therein. The SSID of the router may 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 a user of the biometric monitoring device or through portable communication device data. This may be done with the aid of user input and/or without the aid of user input. In one embodiment, if the biometric monitoring device can determine whether it is in a vehicle, a vehicle type, or a particular vehicle without using an SSID and it encounters an SSID that moves with the vehicle, the biometric monitoring device can send the SSID and information about the vehicle to a central database to be catalogued as the SSID corresponding to the vehicle. Alternatively, if a user enters information about a vehicle they are or were in at a previous point in time and there is an SSID encountered during or near the time that the user indicated that they were in the vehicle, the SSID and vehicle information may be sent to a central database and associated with each other.

In another embodiment of the biometric monitoring device, a location sensor may be used to determine the trajectory of the user. This trajectory can then be compared to a database of routes for different traffic patterns. The transit mode may include, but is not limited to, walking, running, cycling, driving, riding a bus, riding a train, riding a tram, riding a subway, and/or riding a motorcycle. If the user's trajectory corresponds well with the route of a particular transit mode, it may be assumed that the user used that transit mode during the period of time it took to traverse that route. It should be noted that the speed of completing a route or segment of a route may improve guessing of traffic patterns. For example, both a bus and a car may be able to take the same route, but the additional stop of the bus at the bus stop may allow the device to determine that the user was riding the bus instead of the car. Similarly, the distinction between riding a bicycle and driving through a route may be aided by typical differences in speed between the two. This speed difference may also depend on the time of day. For example, some routes may be slower during peak hours due to automobiles.

In another embodiment, the biometric monitoring device may be capable of detecting that the user is in or near the vehicle based on a measurement of the magnetic field of the vehicle. In some embodiments, the magnetic field signature of a location (e.g., train station, subway station, bus station, garage) typically associated with a vehicle may also be used to infer that a user is currently, will be, or has been in the vehicle. The magnetic field signature may be non-time-varying or time-varying.

If it is determined that the user was in fact in the vehicle for a period of time, other metrics about the user may be modified to reflect the state. Where the biometric monitoring device and/or the portable electronic device may measure activity metrics such as number of steps taken, distance walked or run, altitude climbed, and/or calories burned, these metrics may be modified based on information about vehicle travel. If any steps taken or elevation climbed during the time the user is in the vehicle are incorrectly recorded, they may be removed from the recording of the metrics about the user. Metrics derived from incorrectly recorded number of steps taken or altitude climbed, such as distance traveled and calories burned, may also be removed from the recording 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 that should not be measured while in the vehicle (e.g., number of steps taken or stairs climbed) can be turned off, or algorithms for measuring these metrics can be turned off, thereby preventing incorrectly recorded metrics (and saving power). It should be noted that metrics relating to vehicle usage (e.g., the type of vehicle being ridden, the time of the ride, which route was taken, and how long the journey took) may be recorded and used later to present this data to the user and/or to correct other activity and physiological metrics relating to the user.

Position sensing using bluetooth

The biometric monitoring device may also use a method similar to that described above to determine when the user is approaching a static location. In one embodiment, a bluetooth ID from a computer (e.g., tablet) at a restaurant or store may be used to determine the user's location. In another embodiment, the user's location may be determined using a semi-permanent bluetooth ID from a portable communication device (e.g., a smartphone). In the case of a semi-fixed 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 colleagues of the user may be created. If the user is within range of several of these Bluetooth IDs during typical hours of operation, then the user may be assumed to be working. Detection of other bluetooth IDs may also be used to record when two users are met. For example, it may be determined that the user is running away with another user by analyzing pedometer data and bluetooth ID. Similar such concepts are discussed in further detail in united states provisional patent application No. 61/948,468, filed on 3/5/2014, and previously incorporated by reference with respect to such concepts.

Uncertainty metric for location-based GPS

When sensor signals are fused with GPS signals to estimate informative biometrics (e.g., number of steps, pace of life, speed, or trajectory of a journey), the quality of the GPS signals is often very informative. However, it is known that GPS signal quality is time-varying, and one of the factors affecting signal quality is the surrounding environment.

The location information may be used to estimate GPS signal quality. The server may store a map of the area type, which is predetermined by the number and kind of objects that deteriorate the GPS signal. The types may be, for example: large building areas, small building areas, open areas, water areas, and forest areas. These area types can be interrogated with their first several position estimates (which are expected to be coarser and incorrect) when the GPS sensor is turned on. With a coarse GPS estimate of location, possible region types can be returned, and these can be subsequently considered in calculating GPS signal quality and reliability.

For example, if the user is in or near an urban canyon (an area surrounded by tall buildings), such as the san francisco city area, then low certainty may be associated with any GNSS position measurements. This certainty may later be used by an algorithm that attempts to determine the user's trajectory, speed, and/or elevation based at least in part on the 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 a street map and looking at the characteristic of when the GNSS sensor shows that the user is traveling along the street (e.g., with a speed of 10mph or higher) but its trajectory is not on the road. A database of approximate location based GPS certainty can also be inferred from a map showing tall buildings, canyons or dense forests where there are.

Position sensing using vehicular GNSS and/or dead reckoning

Many vehicles have integrated GNSS navigation systems. Users of vehicles without integrated GNSS navigation systems often purchase GNSS navigation systems for their automobiles, which are typically installed non-permanently within the driver's field of view. In one embodiment, the portable biometric monitoring device may be capable of communicating with a GNSS system of the vehicle. In the case where the portable biometric monitoring device is also used to track location, it may receive location information from the vehicle GNSS. It may enable the biometric monitoring device to turn off its own GNSS sensor (if it has such a sensor), 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 may be used to perform dead reckoning to determine trajectory and/or position in the event that the vehicle does not have a GNSS system or the vehicle's GNSS system cannot obtain reliable position measurements. The dead reckoning location information may supplement the GNSS sensor data from the biometric monitoring device. For example, the biometric monitoring device may reduce the frequency at which it samples the GNSS data and fill in the gaps between the GNSS location data with locations determined by dead reckoning.

Step counter data fusion with satellite-based position determination

In some implementations of biometric monitoring devices, data from a variety of different sensors can be fused together to provide new insight about the activity 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 a peak detection analysis on accelerometer data from an accelerometer of the biometric monitoring device to determine when a wearer of the biometric monitoring device climbs a staircase or goes up a slope (as opposed to sitting an elevator or escalator or walking across flat ground), for example

In another example of sensor data fusion, data from a step counter such as discussed above may be combined with distance measurements derived from GPS data to provide a fine estimate of the total distance traveled within a given window. For example, GPS-based distance or velocity data may be combined with step counter-based distance or velocity (using the number of steps taken times the span) using a kalman filter in order to obtain a fine distance estimate, which may be more accurate than GPS-based distance or velocity measurements alone or step counter-based distance or velocity 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 implementations are further discussed in united states provisional patent application No. 61/973,614, filed on 4/1/2014, which was previously incorporated herein by reference in the section "cross-reference to related applications," and which is again incorporated herein by reference with respect to aligning content refined with respect to distance or velocity estimates using data from satellite-based positioning systems and step count sensors.

Biometric and environmental/exercise performance correlation

Some embodiments of the portable monitoring device described herein may detect a variety of data, including biometric data, environmental data, and activity data. All of this data may be analyzed or presented to the user to facilitate analysis of the degree of correlation between two or more types of data. In one embodiment, the user's heart rate may be related to car speed, cycling speed, running speed, swimming speed, or walking speed. For example, a chart plotting bicycle riding speed on the X-axis and heart rate on the Y-axis may be presented to the user. In another example, the user's heart rate may be related to music that the user listens to. The biometric monitoring device may receive data about what music the user was listening to over a wireless connection (e.g., bluetooth) to the car radio. In another embodiment, the biometric monitoring device may also itself act as a music player, and thus may record when which song is played.

Weight lifting aid

It may be difficult to properly complete a weight lifting routine without the assistance of a personal trainer or collaborator. The portable biometric monitoring device may assist the user in completing the weight lifting routine by communicating 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 each lift. The biometric monitoring device may use one or more EMG sensors or strain sensors to measure muscle contraction of the user. Muscle contraction of a user may also be inferred by measuring vibration of one or more body parts (e.g., using an accelerometer), perspiration of one or more body parts (e.g., using a GSR sensor), rotation (e.g., using a gyroscope), and/or temperature sensors on one or more body parts. Alternatively, sensors may be placed on the weight lifting device itself to determine when the use is being lifted, as well as the speed at which they are lifted or lowered, the duration of their lifting, and the number of repetitions that they have performed the lifting.

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

Blood glucose level monitoring assistance

In some embodiments, the portable biometric monitoring device may be configured to assist a user (e.g., a diabetic) who needs to monitor their blood glucose level. In one embodiment, the portable biometric monitoring device may indirectly infer a user's blood glucose level or a metric related to the user's blood glucose level. Sensors other than those typically used for monitoring blood glucose monitoring (using continuous or discrete finger-stick type sensors) may be used in addition to or instead of or in addition to typical blood glucose monitoring methods. For example, the biometric monitoring device may alert the user that they should check their blood glucose level based on data measured from sensors on the biometric monitoring device. If the user has performed a certain type of activity within a certain amount of time, their blood glucose level may have decreased, and thus, the biometric monitoring device may display a reminder, generate an audible reminder, or vibrate, reminding the user that their blood glucose may be low and that they should check for blood glucose using a typical blood glucose measuring device (e.g., a finger prick type blood glucose monitor). The biometric monitoring device may allow a user to input a blood glucose level measured from a blood glucose meter. Alternatively, the blood glucose measurement may be automatically transmitted to the biometric monitoring device and/or a third device (e.g., a smartphone or server) in direct or indirect communication with the biometric monitoring device. This blood glucose measurement may 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. The user may also be able to input into or communicate directly or indirectly with the biometric monitoring device what food they eat, are eating, or plan to eat. This information may also be used to determine when a user should be reminded to check their blood glucose level. Other metrics and sensor data described herein (e.g., heart rate data) may also be used, alone or in combination, to determine when a user should be reminded to check their blood glucose.

In addition to alerting when the blood glucose level should be checked, the biometric monitoring device may also display an estimate of the current blood glucose level. In another embodiment, data from the 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., by displaying the data on the smartphone, on a web page, and/or by transmitting the data via radio).

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

UV exposure detection

In one embodiment, the biometric monitoring device may be capable of monitoring exposure of the individual to UV radiation. UVA and UVB may be measured by one or more sensors. For example, a photodiode with a band pass filter that passes only UVA may detect UVA exposure and a photodiode with a band pass filter that passes only UVB may detect UVB exposure. A camera or reflectometer (light emitter and light detector that determine the efficiency of light reflection off the skin) may also be used to measure skin pigmentation of a user. Using UVA, UVB, and skin pigmentation data, the biometric monitoring device can provide information to the user about the amount of UV exposure that he has been subjected to. The biometric monitoring device may also provide an estimate or alarm regarding the likelihood of overexposure to UV, sunburn and increasing the likelihood of its skin cancer risk.

Screen power savings using user presence sensors

The portable biometric monitoring device may have one or more displays to present information to the 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 having a PPG sensor may use the PPG sensor as a proximity sensor to determine when the biometric monitoring device is worn by a user. If the user is wearing a biometric monitoring device, the state of the screen (e.g., a color LCD screen) may change from its typical state of being off to "on" or "inactive".

Power saving relative to satellite-based position determination systems

In some implementations, certain systems included in the biometric monitoring device may consume a relatively larger amount of power than 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 a population of GPS satellites, it uses the power drawn from the biometric monitoring device battery. The biometric monitoring device may be configured to alter the frequency at which the satellite-based position determination system obtains a position fix based on data from one or more sensors of the biometric monitoring device. This adaptive positioning frequency functionality may help conserve power while still allowing the satellite-based position determination system to provide position fixes at useful intervals (as 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 the lighting conditions indicate that the biometric monitoring device may be indoors rather than outdoors. If indoors, the biometric monitoring device may cause the locating frequency to be set to a level that is lower than the locating frequency that would be usable if the lighting conditions appeared to indicate that the biometric monitoring device was outdoors. This has the effect of reducing the number of fixes attempted while the biometric monitoring device is indoors, and thus is less likely to obtain a good fix using a satellite-based position determination system.

In another example, if the motion sensor of the biometric monitoring device indicates that the wearer of the biometric monitoring device is substantially stationary, such as sleeping or not substantially moving more than a few feet per minute, the positioning frequency of the satellite-based position determination system may be set to a lower level than if the motion sensor indicates that the wearer of the biometric monitoring device is in motion (e.g., walking or running from one location to another, such as 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 worn by a person, and if not, the biometric monitoring device may set the positioning frequency to a lower level than if the biometric monitoring device was actually worn. Such determinations as to whether the biometric monitoring device is worn may be made, for example, when motion data collected from a motion sensor of the biometric monitoring device indicates that the biometric monitoring device is substantially immobile (e.g., not immobile even when the biometric monitoring device experiences small movements when indicating that the wearer is sleeping or sedentary) or when data from a heart rate sensor indicates that a heart rate is not detected, for example. 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 pressed against the person's skin and concludes that the biometric monitoring device is not worn. This adaptive satellite-based position determination system positioning frequency concept is discussed in greater detail in U.S. provisional patent application No. 61/955,045, filed 3/18 2014, which was previously incorporated by reference herein in the "cross-reference to related applications" section and again hereby incorporated by reference with regard to what is noted at power savings in the context of a satellite-based position determination system.

It should be understood that in addition to including features discussed in more detail below, the biometric monitoring device may also include one or more features or functionalities discussed above or in the various applications incorporated by reference into the above discussion. 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 functionalities that may be included in a biometric monitoring device, the following discussion below focuses in more detail on some particular embodiments (some of which may also be discussed above).

Heart rate monitor capable of automatically detecting non-wearing and wearing states

Some embodiments provide methods for operating a heart rate monitor of a wearable fitness monitoring device to measure one or more characteristics of a heartbeat waveform. "heartbeat waveform" as used herein refers to any change in a measurement signal caused by or related to blood flow driven by the user's heartbeat. In some embodiments, the measurement signal is related to blood circulation caused by the heart pumping blood through the circulatory system, which causes a change in the cardiovascular drive in capillary volume or other parameters. In some embodiments, the heartbeat waveform is measured by a photoplethysmogram (PPG). In such embodiments, the heartbeat waveform reflects a blood volume change in the capillaries that is related to the user's heartbeat and pulse (the arterial pulse caused by the heartbeat). In some embodiments, the measurement signal is related to muscle activity of the heart or an electrocardiogram signal. In some embodiments, the heart activity or signal may be measured by an ECG to obtain a heartbeat waveform.

The heartbeat waveform represents information for one or more heart cycles (which correspond to a complete heartbeat from the generation of a heartbeat to the start of the next heartbeat). The frequency of the heart cycle is described by the heart rate, which is usually expressed as beats per minute. The heartbeat waveform typically includes information about various phases of the heartbeat, such as the amplitude, frequency, and/or shape of the waveform within one or more cardiac cycles. In many embodiments, the heartbeat waveform is used to obtain a heart rate of the user.

In some embodiments, an optical heart rate monitor may be used in a wearable device, implementing different modes of operation by emitting pulses of light and detecting the light after it interacts with the user's skin or other tissue, thereby capturing data that may be used to obtain the user's heartbeat waveform, wearing status, user characteristics, and the like.

In some embodiments, the present invention provides methods for operating a wearable fitness monitoring device having a Heart Rate Monitor (HRM) in a low power state when the device is determined not to be worn by a user (or "out of the wrist" when implemented as a wrist-worn device). This feature of the HRM is also referred to as an "automatic off" function. In some embodiments, the auto-off function is implemented by operating the HRM in an "unworn" (or "out-of-wrist") detection mode, and the auto-off function automatically turns off the heart rate monitoring operation of the HRM to conserve energy if the device determines that it is not being worn by the user. Other benefits of the auto-off function include providing a more accurate heart rate estimate. For example, the heart rate detection algorithm may reset when an auto-off or auto-on (described below) is performed. In one embodiment, the algorithm stops running when out of the wrist is detected and restarts when on the wrist is detected. When the heart rate monitor restarts, it resets.

In some embodiments, the present invention provides methods for operating a wearable fitness monitoring device having a heart rate monitor in a normal power state when the device is determined to be worn by a user (or "on-wrist" when implemented as a wrist-worn device). This feature of the HRM is also referred to as the "auto-on" function. In some embodiments, the auto-on function is implemented by operating the HRM in a "wear" (or "on-wrist") detection mode. The auto-on function automatically exits the HRM from the low power state and turns on the HRM's heart rate monitoring operation if the device detects motion and determines that it is worn by the user.

In some embodiments, unworn (or extracarpal) and worn (or supracarpal) detection may be implemented by light (e.g., LED) detection, which emits pulses of light and detects signals after the pulses of light interact with the user's skin and tissue. In some embodiments, the unworn and worn probes may share some hardware, firmware, software, and/or parameters for light emission, light detection, and analysis of detected signals. In other embodiments, the two detection modes use different hardware, firmware, software and/or parameters that may be used for light emission, light detection and analysis for unworn and worn detection.

In some embodiments, the wearable fitness monitoring device enters and exits a low power state regulated by probe light (e.g., LEDs) and motion detectors, implementing auto-off and on functions. In the low power state, the heart rate monitor saves power by turning off or scaling down the operation of its LED light source and its photodetector. In some embodiments, other light sources and light detectors (e.g., photodiodes, photomultiplier tubes, CCD or CMOS) may be used to implement the auto-off or on functions. Fig. 17A shows this implementation, with the same optical detection mechanism used to determine whether the heart rate monitor is worn ("on-wrist") or not worn ("off-wrist"). In other embodiments described below, two different probe light mechanisms may be used to determine the on-wrist and off-wrist state of the monitor. As will be appreciated by those skilled in the art, although the heart rate monitor described herein is described as being worn on the wrist of the user, other types of implementations may allow the user to wear the heart rate monitor in alternative forms, such as on the ankle or chest of the user. In such implementations, the worn and unworn states correspond to the "on-wrist" and "off-wrist" states, respectively, of the implementations described below.

As shown in fig. 17A, an LED or another light source emits light pulses that can be detected by a photodetector of a heart rate monitor of a wearable fitness monitoring device (also referred to as a wearable device). Typically, when the device is worn by a user, the emitted light will interact with the user's skin and/or other tissue, and then be detected by the photodetector of the heart rate monitor. If the device is not worn by the user, the emitted light will not be detected by the photodetector or will be detected at a different intensity or pattern. The difference in detected light provides data that the heart rate monitor can analyze to determine whether the device is worn by the user. See block 1702. In the embodiment depicted here, the wearable fitness monitoring device remains in the light detection mode of operation because it is determined that it is worn on the wrist. If the heart monitor determines that the wearable device is not being worn by the user, e.g., detects an out-of-wrist state, the heart rate monitor switches to a low power state in which it does not emit or detect light pulses, thereby conserving power of the wearable device. See block 1704. In the embodiments depicted herein, the wearable device may remain in a low power state or return to a light detection state, which may be accommodated by the user's motion. In the implementations depicted herein, if the heart rate monitor is in a low power state, it remains in such a state if no motion is detected. However, when motion is detected, indicating that the user may be wearing the wearable fitness monitoring device, it returns to the light detection state. This low power state of motion adjustment avoids the need to use light sources and light detectors to adjust the heart rate monitor, thereby enabling further power savings. The motion detection of the wear detection mode may be implemented by any of the motion detectors suitable for the wearable device as described above. For example, motion detection may be implemented by an algorithm that detects stillness or motion from data generated by a motion sensor. Another example may use an inherent interrupt of a motion sensor for waking up the sensor, which may trigger wear detection.

In the implementation depicted in fig. 17A, the heart rate monitor may measure heart rate or other heartbeat waveform related signals while it performs the light detection function depicted at block 1702. 1702 the depicted light detection mechanism determines whether the heart rate monitor should remain in light detection mode and continue to perform the cardiac monitoring function. The same light detection mechanism also determines whether the heart rate monitor should exit light detection and heart rate monitoring. As shown in fig. 17B, separate light detection mechanisms may be used for extracranial detection (to exit PPG heart rate monitoring) and on-wrist detection (to enter PPG heart rate monitoring).

In some embodiments, as depicted in block 1708 of fig. 17B, the light detection mode for detecting the "on-wrist" state may emit a light pulse at a relatively low intensity for a percentage of time. If the light emitted by the light source interacts with the skin or other tissue of the user, the reflected or scattered light will be detected by the photodetector of the heart rate monitor. When this occurs, the heart rate monitor enters a normal power state as depicted by block 1706. When this does not occur, the heart rate monitor continues to photo detect for a certain period of time. If a particular time period has elapsed and no user interaction is detected by light detection, the wearable device enters a low power state as depicted in block 1710.

The heart rate monitor in the normal power state performs both the heart rate monitoring function and the wear detection function, as shown in block 1706. In some embodiments, the light detection mechanism for unworn detection in block 1706 may operate at a relatively higher power than the light detection mechanism for worn detection in block 1708. In some embodiments, the function of 1708 is implemented in two states: one function when entering from the out-of-wrist state and a different function when entering from the over-wrist state. This embodiment will be described in more detail below. In other embodiments, similar light pulses may be used to detect unworn transitions and to detect worn transitions. In some embodiments, different analyses may be applied to unworn detection or to worn detection. For example, unworn detection may be triggered by a particular pattern of intensity variations of detected light pulses, while wearing detection may be triggered by the intensity of detected light pulses. Examples of emission and detection of light pulses for unworn/worn detection are described further below.

The light detection mechanism of 1706 may determine whether the BMD is out-of-wrist (or not worn). If not, it remains in a normal power state to perform cardiac signal monitoring. If an extracranial state is detected, it stops PPG heart rate monitoring, exits normal power mode, and returns to light detection mode at block 1708. After the wearable device has been in the light detection mode of 1708 for a certain period without detecting wearing, the wearable device enters a lower power state of block 1710. The wearable device remains in the lower power state if no user motion is detected. If user motion is detected, it returns to the light detection state of 1708.

FIG. 18A shows a process flow diagram in which a wearable fitness monitoring device having a heart rate monitor operates in different modes in an energy-efficient manner, according to some embodiments of the invention. In various embodiments as described further below, the heart rate monitor operates in different modes by emitting pulses of light using a light source, such as one or more LEDs, and detecting the light after it interacts with the user's skin or other tissue. The characteristics of the emitted and detected light pulses are described further below.

In the embodiment depicted herein, the wearable fitness monitoring device begins by detecting motion of the device. If no motion is detected, the device remains in the motion detection mode. See block 1802. If the device detects motion, it begins operating the heart rate monitor in a "wear detection mode" configured to detect that the device has transitioned from unworn to a worn state. Operation in the second mode may include pulsing light by a light source (e.g., an LED) and detecting light after the light interacts with the user's skin and/or tissue. See block 1804. Within a defined time after entering the second mode, the device determines whether the heart rate monitor detects that the device has transitioned to a worn state. See block 1806. If not, the device ends the wear detection mode, see block 1807, and returns to the motion detection operation of block 1802. If the device detects a transition to a worn state, it begins operating the heart rate monitor in a first mode configured to measure the user's heartbeat waveform or other heart related signals. See block 1808.

The second mode of block 1804 corresponds to a wear detection mode as described elsewhere herein. In one implementation of fig. 18A, the wear detection mode may be implemented in the same manner as the second mode configured to detect a transition of an unworn state as shown in block 1810. The alternative embodiment described below in relation to fig. 18B implements the wear detection mode and the unworn detection mode as two different modes.

When the heart rate monitor is operating in the first mode of block 1808, it detects light pulses interacting with the user's tissue, capturing signal changes caused by heartbeat-related blood flow in the user's capillaries. In some embodiments, the heart rate monitor may measure other information related to the heartbeat waveform as described above. At the same time, the device periodically and temporarily operates the heart rate monitor in the second mode to detect that the device has transitioned from a worn state to an unworn state while also operating in the first mode to detect the heart rate. See block 1810. When the heart rate monitor is operating in the second mode, the device determines whether the device has transitioned to an unworn state, see block 1812. If not, it continues to operate in both the first mode and the second mode, as depicted in block 1810. If so, the apparatus ends the first mode of monitoring the heartbeat waveform signal. See block 1814. At this point, the device returns the heart rate monitor to operating in the motion detection mode of block 1802.

FIG. 18B shows a process flow diagram according to some embodiments of the invention. The process of fig. 18B is similar to the process of fig. 18A, but with slightly different emphasis, beginning with simultaneous operation of a first mode for detecting cardiac signals and a second mode for detecting an unworn state. See block 1822, which may correspond to block 1810 of fig. 18A. The process of fig. 18B implements two different modes for unworn detection that automatically turns off heart rate monitoring (see second mode in block 1822) and wearable detection that turns on heart rate monitoring (see wearable detection mode in block 1832). In some implementations, the process of fig. 18A may use the same mechanism to detect proximity to the user's body to turn on (see wear detection mode of block 1804) and turn off heart rate monitoring (see second mode of block 1810).

In the process shown in fig. 18B, the wearable device operates the heart rate monitor in a first mode to detect heart rate or other heartbeat waveform characteristics. Meanwhile, the heart rate monitor operates in a second mode to detect user proximity. See block 1822. In the embodiment depicted herein, the second mode is implemented to determine whether the heart rate monitor has transitioned to an unworn (or out-of-wrist) state. See block 1824. If the device has not transitioned to the unworn state, the device continues to operate the heart rate monitor in both the first mode and the second mode in the operations of block 1822. If the second mode determines that the heart rate monitor is not worn, the apparatus automatically stops the heart rate monitor operation in the first mode. See block 1826. This operation helps to save energy by automatically turning off the heart rate monitor when the device is not being worn by the user. In some embodiments, the device enters a low power state that does not monitor heart rate or detect user proximity (the second mode operation is also stopped to further save energy, not shown in the flow chart).

In the process depicted in fig. 18B, the wearable device performs motion detection after stopping the first mode of cardiac signal monitoring, which provides a trigger to automatically turn on the heart rate monitoring function when necessary. See block 1828. As mentioned elsewhere herein, motion detection may be implemented by different hardware and software. If no motion is detected, the device remains in a low power state. See block 1830. If motion is detected, which indicates a likelihood of user interaction with the device, the device begins operating in a wear detection mode designed to determine whether the device is likely to be worn by the user. See block 1832. If the wear detection mode of the heart rate monitor determines that it is not worn, the device returns to the motion detection mode. However, if the heart rate monitor determines that the device is worn by the user, the device returns to the operation of block 1822 to simultaneously operate the heart rate monitor in the first mode to detect heartbeats and in the second mode to detect user proximity (transition to an unworn state). See block 1834. It should be understood that although the description of fig. 18A and 18B presents start and end points of device operation, the process may be continuously implemented and may begin and end at any time in the disclosed flow diagrams. In some implementations, the start and end points are entered when the user manually turns the device on and off.

As mentioned above, in some embodiments, the heart rate monitor of the wearable device includes an optical heart rate monitor. When the heart rate monitor operates in different modes, all modes involve emitting pulses of light and detecting the same pulses after they interact with the user's tissue. Different modes may use different light pulses and/or different processes for interpreting detected pulses. Fig. 18C shows a sketch of light pulses used in some embodiments to provide data of heart rate and light pulses for detecting proximity to a user's body. As in the shown embodiment, the heart rate monitor emits light pulses for both the heart rate data stream and the detection data stream. The heart rate data stream as depicted in this embodiment has a lower frequency of, for example, 25Hz (or 40ms between two consecutive pulses), while the probe stream has a higher frequency of, for example, 100Hz (or 10ms between two consecutive pulses). Those skilled in the art understand that different frequencies and amplitudes may be used for the two data streams while keeping the two data streams separable. In certain embodiments, the higher frequency is about 2 to 5 times, or about 2 to 4 times, greater than the lower frequency. The two different frequencies of the light pulses allow the heart rate monitor to emit and detect two different signals simultaneously. The heart rate data stream allows the heart rate monitor to determine the heart rate signal of the user. The probe data stream allows the heart rate monitor to determine whether the device is in close proximity to the user's body. In some embodiments, the heart rate data stream may be continuously operated in the first mode while the probe stream is periodically operated in the second mode. For example, the probe data stream may operate for 120ms every 1000ms (one second). In some embodiments, the heart rate monitor operates to detect flow for less than about 50% of the time when it operates on the heart rate data stream. In other embodiments, the percentage may be less than about 40%, 30%, 20%, 10%, or 5%.

In some embodiments, as shown in the examples herein, the heart rate data stream is in phase with the detection data stream such that some pulses from one stream coincide with some pulses in the other stream. This arrangement may help to reduce the number of light pulses, thereby saving energy. In other embodiments, as shown in the examples below, the two data streams are out of phase, which may provide for easier separation of the data streams.

Fig. 18D shows another sketch of light pulses that may be used in some embodiments to provide data of heart rate versus light pulses for detecting proximity of a user's body. The light pulses used in the heart rate data stream depicted here are similar to those in fig. 18C. However, here the probe data stream has variable intensity, while the probe data stream in fig. 18C has fixed intensity. The probe data streams presented herein may also be used in some embodiments to detect user skin characteristics, as described further in the following section. In the example shown here, the probe data stream includes 8 pulses at 100Hz, four of which have variable intensity. In some embodiments not shown here, fewer pulses may have variable intensity, e.g., only two pulses have variable intensity. In some embodiments, even one pulse may be sufficient for detection. In this example, two of the pulses are of relatively low intensity, and are different from each other. For example, it may have a value of about 0.02 to 0.04mW for lower intensity pulses and a value of about 0.025 to 0.05mW for higher intensity pulses. The difference between the two pulses is indicated in the figure as D1 and may approach about 0.002 to 0.005 mW. Two of the pulses have relatively high intensities and are different from each other. For example, it may have a value of about 0.1 to 0.11mW for lower intensity pulses and a value of about 0.14 to 0.15mW for higher intensity pulses. In other embodiments, the intensity values may be adjusted. As an additional example, a pair of lower intensity pulses may have an intensity, in mW, of about 0.01 to 0.03 (e.g., about 0.021) and 0.025 to 0.035 (e.g., about 0.027), while a pair of higher intensity pulses may have a pulse intensity of about 0.15 to 0.17 (e.g., about 0.16) and about 0.17 to 0.19 (e.g., about 0.175).

The difference between the two high intensity pulses is indicated as D2. The difference between the two pairs is labeled D3. Furthermore, four of the eight pulses have intermediate and constant intensities. The particular number and intensity of the different pulses can be adjusted to provide a good signal to be measured and analyzed. In some embodiments, the values of D1 and D2 are set to allow the photodetector to detect different responses, which in turn allows for analysis of light-user interaction and user skin characteristics. The lower and higher intensities and the value of D3 are set to allow light reflections within a range of possible conditions to be captured when the device is worn by a user: different skin tones, sports, sweat levels and composition, ambient light conditions, etc. The intermediate intensity is set to allow capture of the baseline signal. In some embodiments, for example, lower intensity pulses provide good detection intensity for light-colored skin; high intensity pulses provide good detection for dark skin; and the intermediate intensity pulses provide a good default value for baseline detection.

In some embodiments, the intensity of light measured for any of the pulses may be used to determine the worn and/or unworn state, as well as the user skin characteristics. If the user is wearing the device and the light pulses interact with the user and are then detected, the device may infer the proximity of the user from the strength of the detected signal. For example, if the device is worn, the detection intensity may be higher than unworn, or vice versa. Also, if the device is worn, a pulse frequency consistent with the emitted pulses may be detected. The detected intensity difference between a pair of pulses may be correlated to the difference in the emitted pulses (e.g., D1, D2, or D3) if the device is worn. Such differences may provide an inference that the device is worn. In some embodiments, the detected difference between two pulses (e.g., a pair of D1) may be normalized by the difference between two constant pulses, then the normalized difference may be compared to a threshold to determine whether the device is worn. This analysis helps to eliminate the inherent noise in the detected light. In other embodiments, the device determines a change in response. When the change is high, the device concludes that it is worn, and when the change is low, the device concludes that it is not worn. In some embodiments, further described in the next section, the slope and/or trend of data points (e.g., intensity or power) against the emitted light values of detected light may be used to obtain user skin response characteristics, which may then be used to calibrate the emitted power and/or detection gain. In some embodiments, the device may have both a wear/no wear detection function and a skin characteristic calibration function, as further described in the following section.

Some embodiments provide a method of operating a heart rate monitor of a wearable fitness monitoring device having a plurality of sensors. The method comprises the following steps: (a) operating the heart rate monitor in a first mode while also operating in a second mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin, wherein the first mode is configured to determine one or more characteristics of a user's heartbeat waveform when the wearable fitness monitoring device is in close proximity to the user; (b) from information collected in the second mode, determining that the heart rate monitor is not proximate to the user's skin; and (c) in response to determining that the heart rate monitor is not proximate to the user's skin, ending operation of the heart rate monitor in the first mode. In some embodiments, the one or more characteristics of the user's heartbeat waveform include a user's heart rate.

In some embodiments, the wearable device includes a motion sensor, and the method further involves: prior to (c), determining from information output by the motion detection sensor that the wearable fitness monitoring device has been quiet for at least a defined period; and in response to detecting that the wearable fitness monitoring device has been quiet for at least a defined period, performing (c). In some embodiments, when not operating in the first mode prior to (a), the device: (i) detecting motion of the wearable fitness monitoring device using a motion detection sensor and/or detecting proximity of the heart rate monitor to the user's skin by operating the heart rate monitor in a third mode; and (ii) initiate operation of the first mode of the heart rate monitor when the wearable fitness monitoring device is determined to be in close proximity to the user.

In some embodiments, the heart rate monitor operates in the second mode for no more than about 50% of the time. In other embodiments, the percentage is no more than about 40%, 30%, 20%, or 10%.

In some embodiments, operating the heart rate monitor in the second mode involves: pulsing a light source in the heart rate monitor at a second mode frequency and detecting light from the light source at the second mode frequency; and operating the heart rate monitor in the first mode involves pulsing a light source in the heart rate monitor at a first mode frequency and detecting light from the light source at the first mode frequency. In some embodiments, operating the heart rate monitor in the second mode involves: emitting a series of light pulses with variable intensity; and determining whether the detected light corresponding to the train of light pulses has a variable response corresponding to the variable intensity of the emitted light pulses.

Some embodiments provide a method of operating a heart rate monitor of a wearable fitness monitoring device having a plurality of sensors. The method comprises the following steps: (a) detecting motion of the wearable fitness monitoring device using the motion detection sensor; (b) in response to detecting the motion in (a), operating the heart rate monitor in a wear detection mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin; and (c) upon determining that the wearable fitness monitoring device is proximate to the user's skin via the wear detection mode, operating the heart rate monitor in a first mode configured to determine one or more characteristics of the user's heartbeat waveform. In some embodiments, when the heart rate monitor is not operating or operating in a low power mode, (a) is performed. In some embodiments, the method involves, prior to (a): (i) operating the heart rate monitor in the first mode while also operating in a second mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin; (ii) from information collected in the second mode, determining that the heart rate monitor is not proximate to the user's skin; and (iii) in response to determining that the heart rate monitor is not proximate to the user's skin, ending operation of the heart rate monitor in the first mode.

In some embodiments, the heart rate monitor operates in the wear detection mode for no more than about 50% of the time. In other embodiments, the percentage is no more than about 40%, 30%, 20%, or 10%.

In some embodiments, operating the heart rate monitor in the wear detection mode includes: emitting pulses of light from a light source in the heart rate monitor having a second frequency and/or phase; detecting light from the light source at the second frequency and/or phase; and determining whether the light detected at the second frequency and/or phase has an intensity and/or pattern indicating that the light from the light source has interacted with the user's skin. In some embodiments, the emitted light pulses have a variable intensity. In some embodiments, the emitted light pulses include a series of light pulses having variable intensities. In some embodiments, a first one of the series of light pulses has an intensity that is at least 5 times greater than a second one of the series of light pulses. In some embodiments, the lower intensity pulses are emitted at about 0.05 to 0.5mW (e.g., about 0.012mW), and the higher intensity pulses are emitted at about 0.5 to 2mW (e.g., about 1.19 mW). In other embodiments, the intensity may be adjusted depending on the application and implementation. In some embodiments, the train of light pulses includes a first set of pulses having an intensity that provides a variable response when interacting with light skin, and the train of light pulses also includes a second set of pulses having an intensity that provides a variable response when interacting with dark skin.

Heart rate monitor with automatic calibration

In some embodiments, the present invention provides methods and devices for accurately measuring heartbeat waveforms for different user characteristics, such as skin tone, motion, sweat, location, and physiological state (e.g., skin thickness, body fat, etc.) of a user. Fig. 19A shows two relationships between the intensity of light emitted by a light source of a heart rate monitor and a signal detected by a photodetector of the heart rate monitor. Because darker skin has a lower reflectance of light, the relationship between photodetector readings and light pulse intensity (e.g., DAC) tends to have a lower slope than white skin. Figure 19B depicts temporal modulation of a TIA signal, where the pattern of modulation reflects a heartbeat that modulates reflection and refraction of the user's capillary system. In some embodiments, the skin-characterized signal may be operated intermittently at a higher frequency than the first mode of light pulses for heart rate monitoring.

Fig. 19C shows a flow chart of a process for operating the heart rate monitor of the wearable fitness monitoring device by adjusting the light emission power and/or light detection gain of the heart rate monitor. In various embodiments, the adjustment may be implemented by software, firmware, or hardware. The process begins by emitting and detecting light pulses in a skin characterization mode. See block 1902. In some embodiments, the light pulses for the skin characterization mode may be similar or identical to those for the wear and/or unworn detection mode as shown in fig. 18D. In other embodiments, the light pulses used for the skin characterization mode may have a ramped intensity, as shown in fig. 19D. In some embodiments, the light pulses for the skin characterization mode are in phase with the light pulses for the first mode configured to measure the heartbeat waveform information. In other embodiments, the light pulses for the skin characterization mode are out of phase with the pulses for the first mode, as shown in fig. 19D. Fig. 19D shows a light pulse signal pattern that may be used to adjust the light source intensity and/or light detection gain of a heart rate monitor in order to accurately measure the heart rate signal of a user with different characteristics, such as skin tone.

As depicted in fig. 19D, light pulses having a constant intensity and a lower frequency may be applied for generating pulses in a first mode for measuring heart rate in a manner similar to that depicted in fig. 18B. At the same time, a second stream of optical signals of various intensities may be used to detect skin reflectance properties. When the reflection characteristic is detected and determined by the heart rate monitor, the light intensity and/or detection gain may be adjusted by the heart rate monitor so that the optimal signal level and pattern may be measured by the heart rate monitor.

The process for adjusting the heart rate monitor further involves determining the slope of the detected light values relative to the emitted light values for some of the initial light pulses. See block 1904. In some embodiments, various emission light levels are provided for different slopes, which provide good heart rate monitoring performance for skin conditions with comparable slopes. The process then selects a new emitted light value based on the determined slope, the corresponding previously provided slope, and the light intensity. See block 1906. The new emission light value is used to operate a first mode for heartbeat waveform measurement. See block 1908.

The process further involves collecting more data points in the skin characterization mode (see block 1910) and fitting a mathematical relationship to the collected more data points (see block 1912). Various mathematical relationships may be applied depending on the data pattern between emission and detection intensities for different skin characteristics. In some embodiments, the mathematical relationship is linear, as shown in fig. 19E. In other embodiments, the mathematical relationship is polynomial, as shown in fig. 19F. In other embodiments, alternative mathematical relationships may be applied. The process further involves applying a preset detection light value (e.g., light intensity or power) known to provide a good detection reading to the fitted relationship to obtain a corresponding emission light value. The process then involves emitting light at the obtained emitted light value corresponding to the preset detected light value for operating the heart rate monitor in a first mode. See block 1914. In some embodiments, the calibration process ends after emitting light at the obtained value. See block 1916. In other embodiments, the calibration process may continuously and dynamically adjust the emitted light intensity. In some embodiments, the calibration process may adjust the gain of the photodetector.

Some embodiments provide a method for adjusting at least one setting for operating a heart rate monitor in a wearable fitness monitoring device. The method involves: (a) pulsing a light source in the heart monitor in a skin characterization mode by emitting a series of light pulses, at least some of the light pulses having variable intensities relative to each other; (b) detecting a change in intensity of light from the light pulses emitted in the skin characterization mode after the light has interacted with the user's skin; (c) determining a response characteristic of the user's skin from the intensity change of the light detected in (b); and (d) adjusting a gain and/or light emission intensity of the heart rate monitor operating in a first mode for detecting one or more characteristics of a heart beat waveform of the user using the response characteristic of the user's skin.

In some embodiments, the response characteristic is dependent on an opacity of the user's skin. In some embodiments, operating in the first mode and operating in the skin characterization mode are performed simultaneously. In some embodiments, simultaneously operating in the first mode and operating in the skin characterization mode involves periodically determining a response characteristic of the user's skin while continuously operating in the first mode. In some embodiments, operation in the skin characterization mode occurs no more than about 50%, e.g., no more than about 40% of the time, or no more than about 30% of the time, no more than about 20% of the time, no more than about 10% of the time, no more than about 5% of the time.

In some embodiments, operating in the first mode involves pulsing a light source in a heart rate monitor at a first frequency, and detecting light from the light source at the first frequency after the light has interacted with the skin of the user. Further, operating in the skin characterization mode involves pulsing the light source in the heart rate monitor at a second frequency, and detecting light from the light source at the second frequency.

There are many concepts and embodiments described and illustrated herein. While certain embodiments, features, attributes and advantages have been described and illustrated herein, it should be understood that many other, as well as different and/or similar embodiments, features, attributes and advantages that are apparent from the description and illustrations. Thus, the above embodiments are provided as examples only. It is not intended to be exhaustive or to limit the invention to the precise form, technique, material, and/or configuration disclosed. Many modifications and variations are possible in light of the above teaching. 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 disclosure. Thus, the scope of the present invention is not limited to the above description, since the description of the above embodiments has been presented for purposes of illustration and description.

It is important that the present invention is not limited to any single aspect or embodiment, nor to any combination and/or arrangement of such aspects and/or embodiments. Further, aspects of the present disclosure and/or embodiments thereof may be used alone or in combination with one or more of the other aspects and/or embodiments of the present disclosure. Many of those permutations and combinations will not be discussed and/or illustrated herein separately for the sake of brevity.

Claims

1. A method of operating a heart rate monitor of a wearable fitness monitoring device comprising a plurality of sensors including the heart rate monitor and a motion detection sensor, the method comprising:

(a) detecting motion of the wearable fitness monitoring device using the motion detection sensor;

(b) in response to detecting the motion in (a), operating the heart rate monitor in a wear detection mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin; and

(c) upon determining that the wearable fitness monitoring device is proximate to the user's skin via the wear detection mode, operating the heart rate monitor in a first mode configured to determine one or more characteristics of the user's heartbeat waveform.

2. The method of claim 1, wherein

Operating the heart rate monitor in the wear detection mode comprises pulsing a light source in the heart rate monitor at a wear detection mode frequency and detecting light from the light source at the wear detection mode frequency; and is

Operating the heart rate monitor in the first mode includes pulsing a light source in the heart rate monitor at a first mode frequency and detecting light from the light source at the first mode frequency.

3. The method of claim 1, wherein operating the heart rate monitor in the wear detection mode comprises:

emitting pulses of light from a light source in the heart rate monitor having a second frequency and/or phase;

detecting light from the light source at the second frequency and/or phase; and

determining whether the light detected at the second frequency and/or phase has an intensity and/or pattern that indicates that the light from the light source has interacted with the user's skin.

4. The method of claim 3, wherein emitting a pulse of light from the light source comprises emitting a series of pulses of light having variable intensities.

5. The method of claim 4, wherein the series of light pulses includes a first set of pulses having an intensity that provides a variable response when interacting with light skin, and wherein the series of light pulses includes a second set of pulses having an intensity that provides a variable response when interacting with dark skin.

6. The method of claim 1, wherein operating the heart rate monitor in the wear detection mode comprises:

emitting a series of light pulses with variable intensity; and

determining whether the detected light corresponding to the series of light pulses has a variable response corresponding to the variable intensity of the light pulses.

7. The method of claim 1, further comprising:

determining from information output by the motion detection sensor that the wearable fitness monitoring device has been quiet for at least a defined period; and

in response to determining that the wearable fitness monitoring device has been quiet for at least a defined period, powering down the device.

8. The method of claim 1, wherein (a) is performed when the heart rate monitor is not operating or is operating in a low power mode.

9. The method of claim 1, wherein (a) comprises detecting an output from the motion detection sensor, wherein the output exceeds a defined threshold.

10. The method of claim 1, further comprising, prior to (a):

(i) operating the heart rate monitor in the first mode while also operating in a second mode configured to detect close proximity of the wearable fitness monitoring device to a user's skin;

(ii) Determining, from information collected in the second mode, that the heart rate monitor is not proximate to the user's skin; and

(iii) in response to determining that the heart rate monitor is not proximate to the user's skin, ending operation of the heart rate monitor in the first mode.

11. The method of claim 1, wherein the heart rate monitor comprises an optical heart rate monitor.

12. A wearable fitness monitoring device, comprising:

a motion detection sensor configured to provide an output corresponding to a motion performed by a user wearing the fitness monitoring device;

a heart rate monitor including a photoplethysmography sensor, the photoplethysmography sensor comprising: (i) a periodic light source; (ii) a light detector positioned to receive periodic light emitted by the periodic light source after interaction with a user's skin; and (iii) circuitry to determine a heart rate of the user from the output of the light detector; and

control logic configured to perform a method comprising:

13. The apparatus of claim 12, wherein

14. The apparatus of claim 12, wherein operating the heart rate monitor in the wear detection mode comprises:

detecting light from the light source at the second frequency and/or phase; and

15. The apparatus of claim 14, wherein the light pulses emitted from the light source comprise emitting a series of light pulses having variable intensities.

16. The device of claim 15, wherein the series of light pulses includes a first set of pulses having an intensity that provides a variable response when interacting with light skin, and wherein the series of light pulses includes a second set of pulses having an intensity that provides a variable response when interacting with dark skin.

17. The apparatus of claim 12, wherein operating the heart rate monitor in the wear detection mode comprises:

emitting a series of light pulses with variable intensity; and

18. The apparatus of claim 12, further comprising:

19. The apparatus of claim 12, wherein (a) is performed when the heart rate monitor is not operating or operating in a low power mode.

20. The device of claim 12, wherein (a) comprises detecting an output from the motion detection sensor, wherein the output exceeds a defined threshold.

21. The apparatus of claim 12, further comprising, prior to (a):