WO2018145250A1

WO2018145250A1 - Ergonomic feedback

Info

Publication number: WO2018145250A1
Application number: PCT/CN2017/073071
Authority: WO
Inventors: Nicolas J-C SCHMITT
Original assignee: Microsoft Technology Licensing LLC
Current assignee: Microsoft Technology Licensing LLC
Priority date: 2017-02-07
Filing date: 2017-02-07
Publication date: 2018-08-16
Anticipated expiration: 2019-08-07

Abstract

Techniques for monitoring seating positions and movements of a user are described. Sensor data associated with sensors located on one or more seat pads or cushions is received. The data is indicative of movements or positions of a user as the user is seated on a chair on which the one or more seat pads or cushions are situated. The sensor data is compared to a set of baseline data that is indicative of a target seating position. The target seating position is specific to the user and determined during a calibration process for collecting the baseline data using the sensors located on the one or more seat pads or cushions. Feedback data is provided to the user that is indicative of a deviation of a current movement or position of the user from the target seating position.

Description

ERGONOMIC FEEDBACK

FIELD

The present disclosure relates to monitoring and detecting ergonomic data associated with movements and positions of subjects.

BACKGROUND

Workplaces and daily activities may require various physical movements and positions. For example， desk jobs often necessitate that a subject (e.g.， a worker) remain seated for extended periods of time and perform repetitive tasks. Repetitive tasks and improper posture can lead to physical discomfort resulting from pain， injuries， or the like and can result in lost productivity， lost revenue， added health care， and other costs.

Various ergonomics solutions can be implemented to improve comfort， health， safety， and productivity. These solutions typically involve an analysis of postures of a subject while performing certain tasks. For example， an ergonomist can be deployed to visually observe the postures of subjects to determine ergonomic conditions associated with performing the tasks and to suggest alternatives based on the ergonomic conditions.

SUMMARY

Methods and systems for monitoring seating positions and movements of a user are described. Sensor data associated with sensors located on one or more seat pads or cushions is received. The data is indicative of movements or positions of a user as the user is seated on a chair on which the one or more seat pads or cushions are situated. The sensor data is compared to a set of baseline data that is indicative of a target seating position. The target seating position is specific to the user and determined during a calibration process for collecting the baseline data using the sensors located on the one or more seat pads or cushions. Feedback data is provided to the user that is indicative of a deviation of a current movement or position of the user from the target seating position.

The features， functions， and advantages can be achieved independently in various embodiments or may be combined in yet other embodiments， further details of which can be seen with reference to the following description and illustrations.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of techniques in accordance with the present disclosure are described in detail below with reference to the following illustrations：

FIG. 1 is an illustration of an example system for monitoring seating positions and movements of a user in accordance with the disclosure；

FIG. 2 is an illustration of an example system for monitoring seating positions and movements of a user；

FIG. 3 is an illustration of an computing system that may be used in various embodiments； and

FIG. 4 is an illustration of operations for monitoring seating positions and movements of a user in accordance with the disclosure.

DESCRIPTION

Incorrect ergonomics and improper posture at offices and other places of work are contributing to increasing numbers of health problems and repetitive stress injuries. Some of the risk factors that can cause such problems include improper postures， improperly designed workspaces， and poor seating. Improper posture can lead to a rounded and dysfunctional spinal posture， for example a C-Spine position. An ergonomically proper posture typically includes proper placement of the posterior against the back of the seat pad and a correct sitting posture with the shoulders in line with the pelvis. Because of the latency before repetitive stress injuries may manifest， it is important to encourage the development and maintenance of correct posture habits on a regular basis.

Some tools may be used to improve the seating posture but it would be advantageous to provide tools that have communications capability， collect data， and provide recommendations to improve seating conditions. The present disclosure describes an ergonomic seat pad or cushion that includes sensors that provide data that can be analyzed to provide user feedback and recommendations.

Figure 1 illustrates an example chair or seat 100 that includes a backrest 110 and a seating area cushion 120. In an embodiment， an ergonomic seat pad or cushion can include at least one sensor and means for wireless connectivity such as Wifi， Bluetooth， or other wireless communications technologies. The ergonomic seat pad or cushion may be designed to be placed on the seating position， the back position， or other positions. The sensors may be configured to detect body positions of the user. The sensor data may be analyzed by software executing on a configured computing device that may be a desktop or laptop computer， smartphone， tablet， or any other device with computing capability. The software may perform functions that determine that the user is properly sitting， including determining：

● leaning forward/backward/right/left

● rolling the shoulder/crouching

● length of time sitting

● whether the back is on the back of the seat

● straightness

The ergonomic seat pad or cushion can be designed to fit a large number of chairs and chair types， and in some embodiments can be adjustable. Sensors may be embedded in the ergonomic seat pad or cushion. Sensors may include pressure sensors， motion sensors， and force sensors. The data from these sensors can be analyzed by software executing on a computing device. Based on various detected conditions， a computing device may， for example， provide a notification to alert the user ifhe or she moves out of a proper sitting position， thus giving the user continuous feedback on their posture. In some embodiments， the sensors may be included in a cushion or seat pad that is part of a seat assembly.

In an embodiment， the ergonomic seat pad or cushion may include a computing device configured to control the sensors， gather data from the sensors， and transmit data to a second computing device. In turn， the second computing device may be configured to record the data， analyze the data， and display results of the analysis on a display device. The second computing device may further be configured to provide visual， audio， or tactile feedback to the user to aid the user in maintaining an ergonomically correct posture when seated.

The ergonomic seat pad or cushion can thus assist in helping users learn and maintain healthier sitting habits and thus reduce risk of future injury. The ergonomic seat pad or cushion may also be useful for employers to encourage proper seating habits to their employees. The ergonomic seat pad or cushion may further be useful for physical therapists and various ergonomics professions to help clients with ergonomic plans and recommendations.

The ergonomic seat pad or cushion can in some embodiments， send user presence information to one or more computing devices， and can provide information to determine which seats are being occupied.

In an embodiment， the ergonomic seat pad or cushion may be formed of any suitable material， such as fabric， foam， leather， or plastic. The ergonomic seat pad or cushion may be formed as a single unit or with a separate seat pad and back pad， as well as additional pads. In some embodiments， for example， a lumbar pad may also be included.

The sensors may be embedded in the ergonomic seat pad or cushion or attached to the surface of the ergonomic seat pad or cushion. Each ergonomic seat pad or cushion may include more than one sensor. For example， a first sensor may be located on the ergonomic seat pad or cushion in such a way that the sensor can determine when a user is seated in the ergonomic seat pad or cushion. The first sensor can be used for a variety of purposes， for example to determine whether a user is seated in the chair. When it is determined that a user is seated in the chair， then other functions can be activated. In this way， the overall system need not consume energy by monitoring for sensor data and running analysis functions when the user is not in the seating position.

The ergonomic seat pad or cushion can also include a second sensor that can be situated to determine whether the user is seated in such a way that the user’s posterior is against the back of the seat portion of the chair and touching the backrest portion of the chair. Additional sensors may be provided to further increase the amount of data that is collected， allowing for greater fidelity in the ergonomic analysis.

When an ergonomic seat pad or cushion is used for the lumbar area， the sensor can be situated so as to determine whether the user is seated so that the lumbar region of her back is properly positioned.

In an embodiment， the ergonomic seat pad or cushion may include an additional sensor situated so as to determine the distance between the user′s upper back or shoulder blades and the surface of the ergonomic seat pad or cushion. This distance can be used to determine whether the user is seated in such a way that their shoulder blades or upper back is properly positioned.

In an embodiment， the ergonomic seat pad or cushion may include an additional sensor situated so as to determine the distance between the sensor and the back of the user′s neck or the position of the user′s neck when the user is seated.

In an embodiment， a sufficient number of sensors may be included to allow for a higher fidelity analysis and determination of a user’s position. For example， sensors may be placed on a seat cushion so as to determine if the user has a straightened back， if the user is too far forward， or if the user rolling back. By using a sufficient number and placement of sensors， the user’s seating posture may be determined beyond only leaning. For example， it can be determined if the user is rolling his/her shoulder， or if the user has a straight back. In an embodiment， the sensors may be configured to determine the breaking of the S-curve or S-spine position which indicates an ergonomically correct posture. The ergonomically correct posture may include proper placement of the posterior against the back of the seat pad， a supported lumbar region， sitting straight up with the shoulders in line with the pelvis and slightly apart from the seat back， and an erect neck posture in line with the shoulders and pelvis.

The above-described sensors may be communicatively coupled to at least a computing device. The computing device may be configured to receive and measure signals from the sensors. For example， if the sensor is a pressure sensor， then the sensor may provide signals indicative of a sensed pressure that may， for example， correspond to the location of a user′s back and can be used to evaluate whether a user is sitting in the chair with an ergonomically correct posture. The sensor may communicate with the computing device using a wired connection such as USB， or a wireless connection such as WiFi or Bluetooth.

The computing device may be configured to generate a user interface to display information pertaining to the user’s position. The user interface may also provide other information such as the status of the ergonomic seat pad or cushion. As the position of the user changes， the sensors may send updated information and the updated information may be analyzed to provide updated information regarding the user’s position on the display. This may be performed in real-time. The user interface may provide this information graphically or in tabular form.

In some embodiments， the information may provide feedback on whether the user is following established recommendations for ergonomic posture. Additionally， the user interface may be configured to receive feedback from the user， for example， that indicates the user’s feedback on a particular position. This feedback may be used to adjust an ergonomic profile for the user.

In an embodiment， the sensors may provide information that can uniquely identify a user based on a signature posture profile or weight profile， or other sensor information.

In an embodiment， the ergonomic analysis function may perform calibration to establish a baseline user position. Updated information from the sensors may be analyzed and used to determine， based on comparison to the baseline user position， whether the user has changed positions and/or is in an improper position.

In an embodiment， the ergonomic analysis may be performed by one or more functions implemented as an application executing on a mobile device such as a smartphone. The smartphone may also send the sensor data and/or results of the analysis to a remote system such as a cloud-based system. The cloud-based system may include functionality to store the received information. The cloud-based system may also track the information for each user， for example， based on user accounts.

In an embodiment， notifications may be provided to the user via a computing device executing the ergonomic analysis application. Additionally， the notifications maybe provided on multiple devices， such as a smartphone and a desktop computer. The cloud-based system may analyze information based on current and accumulated infonnation for the user. The cloud-based system may also perform analysis based on crowdsourced information or information from other users providing similar information or executing the ergonomic analysis application on their respective computing devices.

The cloud-based system may be configured to receive， store， and process the data transmitted from one or more computing devices that are receiving sensor information from one or more units ergonomic seat pads or cushions. The ergonomic seat pad or cushion can analyze the data in real-time or near real-time such that the users may receive timely information and feedback.

To identify potential ergonomic issues， the cloud-based system can analyze the data received from the plurality of ergonomic seat pads or cushions against ergonomic data. If the analysis reveals an ergonomically unacceptable condition， the cloud-based system can associate the associated information with the condition. For example， the cloud-based system may performed specified actions when the analysis indicates that a set of sensor information for a user is indicative of an ergonomically unacceptable condition.

The ergonomic data at the cloud-based system can be based on various parameters that include input provided by an ergonomist， historical ergonomic information， human factors considerations (e.g.， age of the user， user of the subject， etc. ) ， and/or industry practices. Further， the cloud-based system can update and refine data based on the updated information， such that the processes of defining and identifying ergonomic are iterative.

In addition to providing notifications to users of incorrect posture and other ergonomic issues， the cloud-based system can provide additional functionality to forecast ergonomic issues and generate corrective actions for user action.

Turning to FIG. 2， an example of an ergonomic cushion or seat 200 is illustrated. The ergonomic cushion or seat 200 is configured to be attached or place on a chair such that the ergonomic cushion or seat 200 is in turn contacted by a subject when seated. The ergonomic cushion or seat 200 is configured to collect data associated with positions and movements of various body parts of the subject， and to transmit the collected data to the computing device 220. In an embodiment， the ergonomic cushion or seat 200 includes a number of sensors 202A-N (which may be referred herein singularly as “sensor 202” or in the plural as “sensors 202” ) connected to a computing device 204. The sensors 202 are configured to measure and transmit data indicative of the positions and movements of the subject to the computing device 204， which in turn， processes and transmits the data to the computing device 220.

Each of the sensors 202 is typically attached to various locations in or on ergonomic cushion or seat 200. Although FIG. 2 shows nine sensors 202 located in various positions， this number can be larger or smaller depending on the size and purpose of the ergonomic cushion or seat 200.

The computing device 204 of the ergonomic cushion or seat 200 may interface with the computing device 220 by way of the access point 206 or a wired connection. However， in an example， an intermediary device can also be used. The computing device 220 may be a smartphone， a tablet， or another computing device that is capable of communicating directly with the computing device 206 via， for example， Bluetooth， or communicate with access point 206 to communicate with computing device 204.

Various data can be measured with regard to the user’s position and movements， including， for example， posture， motion， orientation， position， force， velocity， acceleration， jerk， vibration， noise， etc. Thus， each sensor 202 can include various types of sensors and devices such as a combination of an accelerometer such as a three-dimensional accelerometer， gyroscope， inclinometer， location sensor， position sensor， tilt sensor， rotation sensor， motion sensor， environmental sensor， temperature sensor， barometric pressure sensor， compass/gravity sensor， magnetic sensor， etc. The sensor 202 can also be implemented as a virtual sensor that combines measurement and functionalities of the various types of sensors and devices.

To illustrate a use of a sensor to measure a certain type of data， an accelerometer attached to a node can be used to measure acceleration data at the node. The amplitude of the acceleration data is indicative of a force applied to the node. If the acceleration data indicates that the force alternates directions， this alternation is indicative of a vibration at the node. Changes in the acceleration data also indicate a jerk movement at the node. Additionally or alternatively， the accelerometer can measure position data such as an origin or neutral position and travelled positions. The travelled positions can be processed to determine movements and distances that the node travelled relative to the neutral position. Likewise， frequencies and directions associated with changes between travelled positions and the neutral position can be processed to determine vibrations and directions of the forces associated with the vibrations. Those skilled in the art will appreciate that various data can be measured by the sensors 202 depending on the node that each sensor is attached to and the movements that the node is experiencing.

In one embodiment， the sensors 202 may be configured to transmit the measured data to the computing device 204， which may be configured to process the data to determine the movements and positions of the subject. In some embodiments， the computing device 204 may not perform processing and may collect and transmit data to computing device 220， which may in turn execute functionality to process the received data.

In one embodiment， the computing device 204 may add time and location information to the processed data. The time may be measured by way of a clock operated by the computing device 204， while the location may be determined from the measured data or from a location sensor of the computing device 204 (e.g.， circuitry that determines coordinates based on global positioning system (GPS) ， location triangulation techniques， etc. ) .

Additionally， the computing device 204 may label the processed data to identify the corresponding nodes. For example， a label of “LLB” may be added to data measured by the sensor 202A to indicate that this data is associated with the lower left part of the back of the ergonomic cushion or seat 200. Furthermore， the computing device 204 may categorize the data into a type of movement such as twisting of the shoulders. This categorization can combine the data from several sensors 202.

Moreover， the computing device 204 may correlate the data (e.g.， the time and location stamped， labeled， and categorized data) with a particular subject. For example， the computing device 204 can receive information that identifies the current subject， or may be able to detect or infer the identity of the subject based on sensed data. Identity information can be provided by the computing device 220 or can be locally stored at the computing device 204. For example， the computing device 204 can add an identifier of the subject (e.g.， name， title， employee number， etc. ) to the data to allow the computing device 220 and/or the ergonomist (not shown) to identify the subject in order to provide him or her with a service (e.g.， the computing device 220 may send an SMS alert to a mobile device of the subject to alert of the subject ofa potential ergonomic issue) . The data can also be made anonymous， in which case the computing device 204 may remove any data that identifies the subject before transmission to the computing device 220. In order to receive a service back from the processing center (e.g.， an alert) ， the computing device 204 generates an identifier (e.g.， a random number) that identifies it to the computing device 220. Thus， the computing device 220 may track the data collected over time and provides a service to the computing device 204 based on an identifier of the computing device 204 rather than the subject.

As explained above， the computing device 220 can notify the subject of potential ergonomic issues through various messages and sensory signals (e.g.， text message， audible sound， a text， a flashing light， a vibration， etc. ) . Thus， in addition to processing and transmitting the data， the computing device 204 may directly notify the user without depending on computing device 220. For example， the computing device 204 may be configured with various circuitries to generate sensory signals (e.g.， a speaker， a monitor， a LED light， a vibration mechanism， etc. ) .

To provide the various functionalities of the computing device 204 and the computing device 220， some or all elements of these devices may be implemented using system 300 of FIG. 3. More particularly， FIG. 3 illustrates an example of circuitry for implementing the monitoring and detecting techniques in accordance with the present disclosure. As used herein， the term “circuitry” includes hardware components (e.g.， microprocessors， application specific integrated circuits， processors， etc. ) configured using firmware and software that implement the monitoring and detecting techniques described herein. For example， a processor can be configured by instructions loaded from memory， e.g.， random access memory (RAM) ， read-only memory (ROM) ， firmware， and/or mass storage， embodying logic operable to configure the processor to perform the functionalities disclosed herein. In another example， these functionalities can be implemented on a single-board microcontroller designed around a reduced instruction set computing (RISC) single chip microcontroller， for example an 8-bit RISC

or a 32-bit

ARM commercially available from

microcontroller by using a programming language compiler and a boot loader that executes on the microcontroller.

FIG. 3 illustrates an example of the system 300 that may include at least a processor 302， a system memory 304， a storage device 306， input/output peripherals 308， communication peripherals 310， and an interface bus 312. The interface bus 312 may be configured to communicate， transmit， and transfer data， controls， and commands among the various components of the system 300. The system memory 304 and the storage device 306 may comprise computer readable storage media. A non-transitory computer-accessible storage medium may include any volatile or non-volatile media， such as RAM (e.g. SDRAM， DDR SDRAM， RDRAM， SRAM， etc. ) ， ROM， etc.， that may be included in some embodiments of computing device 700 as system memory 720 or another type of memory. Further， a computer-accessible medium may include transmission media or signals such as electrical， electromagnetic or digital signals， conveyed via a communication medium such as a network and/or a wireless link， such as may be implemented via network interface 740. Portions or all of multiple computing devices， such as those illustrated in FIG. 7， may be used to implement the described functionality in various embodiments； for example， software components running on a variety of different devices and servers may collaborate to provide the functionality. In some embodiments， portions of the described functionality may be implemented using storage devices， network devices， or special-purpose computer systems， in addition to or instead of being implemented using general-purpose computer systems. The term “computing device， ” as used herein， refers to at least all these types of devices and is not limited to these types of devices. For purposes of this specification and the claims， the phrase “computer-readable storage medium” and variations thereof， does not include waves， signals， and/or other transitory and/or intangible communication media.

Further， the system memory 304 may comprise an operation system and applications. The processor 302 may be configured to execute the stored instructions and can comprise， for example， a logical processing unit， a microprocessor， a digital signal processor， and the like. The input and output peripherals 308 may include user interfaces such as a keyboard， screen， microphone， speaker， other input/output devices， and computing components such as digital-to-analog and analog-to-digital converters， graphical processing units， serial ports， parallel ports， universal serial bus， signal generators， filters， signal processors， and the like. The input/output peripherals may be connected to the processor 302 through any of the ports coupled to the interface bus 312. The communication peripherals 310 may be configured to facilitate communication between the system 300 and other computing devices over a communications network and may include， for example， a network interface controller， modem， various modulators/demodulators and encoders/decoders， wireless and wired interface cards， antenna， transmitters， receivers， and the like.

Once the computing device 220 and the computing device 204 are configured to perform the monitoring and detecting techniques and once ergonomic seat or cushion 200 including sensors 202 are attached on a chair and in use， the various data indicative of the user position and movements can be collected and processed as shown in FIG. 4. The data can be analyzed in real-time to provide real-time feedback and can be also stored for further analysis or sent to a cloud-based service.

Turning to FIG. 4， an example operation for monitoring seating positions and movements of a user is illustrated. Referring to FIG. 4， operation 400 begins the operational procedure. Operation 400 may be followed by operation 402. Operation 402 illustrates receiving， by a computing device， sensor data associated with sensors located on one or more seat pads or cushions. In an embodiment， the data may be indicative of movements or positions of a user as the user is seated on a chair on which the one or more seat pads or cushions are situated.

Operation 402 may be followed by operation 404. Operation 404 illustrates analyzing， by the computing device， the received sensor data by comparing the sensor data to a set of baseline data. In an embodiment， the baseline data may be indicative of a target seating position. Additionally， the target seating position may specific to the user and determined during a calibration process for collecting the baseline data using the sensors located on the one or more seat pads or cushions.

Operation 404 may be followed by operation 406. Operation 406 illustrates based on the comparing， providing feedback data indicative of a deviation of a current movement or position of the user from the target seating position.

In some embodiments， the feedback data may be indicative of an ergonomic issue when the data corresponds to the current movement or position of the user exceeding an ergonomic threshold. The ergonomic threshold may be presented as a numerical value， in some examples. Additionally， the feedback data may be usable to generate a notification to the user via the computing device.

In some embodiments， information indicative of an acknowledgement of the feedback from the user may be received.

In some embodiments， the computing device may be located remotely from the user and the sensor data may received from a remote device via a communications network. Additionally， sensor data may be received from a plurality of remote devices and associated with a plurality of users. The feedback data may determined in view of the sensor data associated with the plurality of users.

In some embodiments， the sensors may comprise one or more of a pressure sensor， motion sensor， or a force sensor.

In some embodiments， the ergonomic issue may include a twisting of the shoulders of the user. Additionally， the baseline data may be indicative of an S-curve.

In some embodiments， an identity of the user may be determined based on the received sensor data.

In some embodiments， a feature that can be implemented at the computing device 220 is the visualization of the processed data and their comparisons to thresholds (e.g.， posture or movement thresholds) to detect ergonomic issues. This feature allows the user to obtain visual snapshots representative of the cumulative effect of the forces， motions， postures， etc.， that the user experiences over a specified time period. However， those skilled in the art will appreciate that other visualizations of the data may be implemented. For example， the visualized data can be associated with a plurality of subjects， over longer or shorter time periods. Also the visualized data can be shown by body part (e.g.， neck， back) ， etc. The data can be rendered on a monitor or a display accessible to the user.

In some embodiments， the cloud-based service may analyze collected data for multiple users to determine statistical information and to identify trends and patterns related to user ergonomic habits， patterns， and health. In one embodiment， the device driver assessment system may be configured to analyze the data.

The various features and processes described above may be used independently of one another， or may be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. In addition， certain method or process blocks may be omitted in some implementations. The methods and processes described herein are also not limited to any particular sequence， and the blocks or states relating thereto can be performed in other sequences that are appropriate. For example， described blocks or states may be performed in an order other than that specifically disclosed， or multiple blocks or states may be combined in a single block or state. The example blocks or states may be performed in serial， in parallel， or in some other manner. Blocks or states may be added to or removed from the disclosed examples. The example systems and components described herein may be configured differently than described. For example， elements may be added to， removed from， or rearranged compared to the disclosed examples.

Conditional language used herein， such as， among others， ″can， ″ ″could， ″ ″might， ″ ″may， ″ “e.g.， ” and the like， unless specifically stated otherwise， or otherwise understood within the context as used， is generally intended to convey that certain examples include， while other examples do not include， certain features， elements， and/or steps. Thus， such conditional language is not generally intended to imply that features， elements and/or steps are in any way required for one or more examples or that one or more examples necessarily include logic for deciding， with or without author input or prompting， whether these features， elements and/or steps are included or are to be performed in any particular example. The terms “comprising， ” “including， ” “having， ” and the like are synonymous and are used inclusively， in an open-ended fashion， and do not exclude additional elements， features， acts， operations， and so forth. Also， the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used， for example， to connect a list of elements， the term “or” means one， some， or all of the elements in the list. Similarly， the term “at least one of” is used in its inclusive sense (and not in its exclusive sense) so that when used， for example， in reference with a list of elements， the term “at least one of” means one， some， or all of the elements in the list rather than one of each element and/or one from each category or type of elements.

While certain examples have been described， these examples have been presented by way of example only， and are not intended to limit the scope of the inventions disclosed herein. Thus， nothing in the foregoing description is intended to imply that any particular feature， characteristic， step， module， or block is necessary or indispensable. Indeed， the novel methods and systems described herein may be embodied in a variety of other forms； furthermore， various omissions， substitutions and changes in the form of the methods and systems described herein may be made without departing from the spirit of the inventions disclosed herein. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of certain of the inventions disclosed herein.

Claims

A method for monitoring seating positions and movements of a user， the method comprising：

receiving， by a computing device， sensor data associated with sensors located on one or more seat pads or cushions， the data being indicative of movements or positions of a user as the user is seated on a chair on which the one or more seat pads or cushions are situated；

analyzing， by the computing device， the received sensor data by comparing the sensor data to a set of baseline data， the baseline data indicative of a target seating position， wherein the target seating position is specific to the user and determined during a calibration process for collecting the baseline data using the sensors located on the one or more seat pads or cushions； and

based on the comparing， providing feedback data indicative of a deviation of a current movement or position of the user from the target seating position.
The method of claim 1， wherein the feedback data is indicative of an ergonomic issue when the data corresponds to the current movement or position of the user exceeding an ergonomic threshold.
The method of claim 1， wherein the feedback data is usable to generate a notification to the user via the computing device.
The method of claim 1， further comprising receiving information indicative of an acknowledgement of the feedback from the user.
The method of claim 1， wherein the computing device is located remotely from the user and the sensor data is received from a remote device via a communications network.
The method of claim 5， further comprising receiving sensor data from a plurality of remote devices and associated with a plurality of users， wherein the feedback data is determined in view of the sensor data associated with the plurality of users.
A system comprising at least one memory having stored therein computer instructions that， upon execution by one or more processors of the system， at least cause the system to：

receive sensor data associated with sensors located on one or more seat pads or cushions， the data being indicative of movements or positions of a user as the user is seated on a chair on which the one or more seat pads or cushions are situated；

analyzing the received sensor data by comparing the sensor data to a set of baseline data， the baseline data indicative of a target seating position， wherein the target seating position is specific to the user and determined during a calibration process for collecting the baseline data using the sensors located on the one or more seat pads or cushions； and

based on the comparing， send feedback data indicative of a deviation of a current movement or position of the user from the target seating position.
The system of claim 7， wherein the feedback data is indicative of an ergonomic issue when the data corresponds to the current movement or position of the user exceeding an ergonomic threshold.
The system of claim 7， wherein the feedback data is usable to generate a notification to the user via a display device.
The system of claim 7， further comprising receiving information indicative of an acknowledgement of the feedback from the user.
The system of claim 7， wherein the system is located remotely from the user and the sensor data is received from a remote device via a communications network.
The system of claim 11， further comprising receiving sensor data from a plurality of remote devices and associated with a plurality of users， wherein the feedback data is determined in view of the sensor data associated with the plurality of users.
The system of claim 7， wherein the sensors comprise one or more of a pressure sensor， motion sensor， or a force sensor.
The system of claim 8， wherein the ergonomic issue is a twisting of a shoulder of the user.
The system of claim 7， wherein the baseline data is indicative of an S-curve.
The system of claim 7， further comprising determining an identity of the user based on the received sensor data.
A computing device comprising at least one memory having stored therein computer instructions that， upon execution by one or more processors of the computing device， at least cause the computing device to：

receive sensor data associated with sensors located on one or more seat pads or cushions， the data being indicative of movements or positions of a user as the user is seated on a chair on which the one or more seat pads or cushions are situated；

send， via a communications network the sensor data to a remote computing device， the remote computing device configured to analyze the received sensor data based on a set of baseline data， the baseline data indicative of a target seating position， wherein the target seating position is specific to the user and determined during a calibration process for collecting the baseline data using the sensors located on the one or more seat pads or cushions； and

receive， from the remote computing device， feedback data indicative of a deviation of a current movement or position of the user from the target seating position.
The computing device of claim 17， wherein the feedback data is indicative of an ergonomic issue when the data corresponds to the current movement or position of the user exceeding an ergonomic threshold.
The computing device of claim 17， wherein the feedback data is usable to generate a notification to the user via the computing device.
The computing device of claim 17， wherein the sensors comprise one or more of a pressure sensor， motion sensor， or a force sensor.