Portable computing device and analyses of personal data captured therefrom

• US 9,171,201 B2
• Filed: 07/30/2014
• Issued: 10/27/2015
• Est. Priority Date: 05/30/2013
• Status: Active Grant

• Current Assignee: Peloton Interactive, Inc.
• Original Assignee: Atlas Wearables, Inc. (Peloton Interactive, Inc.)
• Inventors: Lake, Thomas Lee II, Kasparian, Michael, Li, Peter
• Primary Examiner(s): Thai, Xuan
• Assistant Examiner(s): GRANT, MICHAEL CHRISTOPHER
• Application Number:
  US14/447,562
• Publication Number:
  US 20150005911A1
• Time in Patent Office: 454 Days
• Field of Search: 434/247
• US Class Current: 1/1
• CPC Class Codes: A63B 21/06 User-manipulated weights
• CPC Class Codes: A63B 2220/17 Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed
• CPC Class Codes: A63B 2220/34 Angular speed
• CPC Class Codes: A63B 2220/40 Acceleration
• CPC Class Codes: A63B 2230/06 heartbeat rate only
• CPC Class Codes: A63B 2230/40 respiratory characteristics
• CPC Class Codes: A63B 2230/50 temperature
• CPC Class Codes: A63B 2230/65 skin conductivity
• CPC Class Codes: A63B 71/06 Indicating or scoring devices for games or players , or for other sports activities
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G09B 19/0038 Sports
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A personal computing device comprising: a processor, an onboard memory, an accelerometer, a gyroscope, and a display; a computer program to create an exercise analysis application comprising: a software module configured to receive data from the accelerometer and the gyroscope that are associated with the bodily motion of a user in three dimensions; a software module configured to place the device in a learning mode, the learning mode comprising recording the data of the user performing a defined exercise to generate a statistical model for the exercise; a software module configured to place the device in a normal mode, the normal mode comprising applying a probabilistic analysis to the bodily motion data to identify an exercise event, classify the exercise by comparison to a recorded model; and a software module configured to apply an analysis to the bodily motion data to score the user'"'"'s exercise form.

Claims

1. A personal computing device comprising:

a. a processor, an onboard memory, an accelerometer, a gyroscope, and a display;

b. a computer program including instructions executable by the personal computing device to create an exercise analysis application comprising;

i. a software module configured to receive acceleration data from the accelerometer and angular velocity data from the gyroscope, the acceleration data and the angular velocity data associated with the bodily motion of a user in three dimensions;

ii. a software module configured to place the device in a learning mode, the learning mode comprising recording the acceleration data, the angular velocity data, and repetitions of the user performing a defined exercise to generate a statistical exercise model for the exercise;

iii. a software module configured to place the device in a normal mode, the normal mode comprising applying a probabilistic analysis to the acceleration data and the angular velocity data to identify an exercise event, classify the exercise by comparison to a recorded exercise model, and simultaneously identify repetitions of the exercise; and

iv. a software module configured to apply a statistical analysis to the acceleration data and the angular velocity data to score the user'"'"'s exercise form.

2. The device of claim 1, wherein the device is adapted to be user-wearable.

3. The device of claim 2, wherein the device is adapted to be wrist-wearable by the user.

4. The device of claim 2, wherein the device comprises a wearable adapter, the wearable adapter reversibly connectable from the device to form a modular design.

5. The device of claim 1, further comprising a biosensor to measure a physiological parameter of the user.

6. The device of claim 5, wherein the biosensor is selected from the group consisting of:

a heart rate monitor, a thermometer, a respirometer, a glucose monitor, an electrolyte sensor, and an electrical conductance meter.

7. The device of claim 5, wherein the biosensor is an optical biosensor.

8. The device of claim 5, wherein the physiological parameter is selected from the group consisting of:

heart rate, skin temperature, breathing rate, galvanic skin response, and hydration.

9. The device of claim 5, wherein the application further comprises a software module configured to present a graphic user interface comprising a heart rate-based rest timer.

10. The device of claim 1, further comprising a geolocation element.

11. The device of claim 1, further comprising a wireless communications element.

12. The device of claim 11, wherein the wireless communications element is a Bluetooth module or an ANT+module.

13. The device of claim 1, wherein the probabilistic analysis to classify the exercise comprises utilization of a neural network, context tree weighting, hidden markov models, or a combination thereof.

14. The device of claim 1, wherein the score is derived, at least in part, from the user'"'"'s body stability, the user'"'"'s motion consistency, or a combination thereof.

15. The device of claim 1, wherein the score is derived, at least in part, from comparison of the acceleration data and the angular velocity data to an exercise model generated from other users'"'"' data.

16. The device of claim 1, wherein the score is derived, at least in part, from comparison of the acceleration data and the angular velocity data to an exercise model generated from one or more qualified fitness professionals.

17. The device of claim 1, wherein the exercise is a unilateral weight training exercise or a bilateral weight training exercise.

18. The device of claim 1, wherein the application further comprises a software module configured to present a user interface on the display, the user interface comprising the score, the exercise, the repetitions, exercise-specific messages, or a combination thereof.

19. The device of claim 18, wherein the exercise is a weight training exercise and the exercise-specific messages are suggestions selected from the group consisting of:

weight too heavy, weight too light, weight changed too much, motion too fast, body too unstable, form incorrect, form too inconsistent, and corrective advice regarding form.

20. An exercise analysis platform comprising:

a. a personal computing device comprising a processor, an onboard memory, an accelerometer, a gyroscope, a display, and a communications element, the device configured to provide an personal exercise analysis application comprising;

i. a software module configured to receive acceleration data from the accelerometer and angular velocity data from the gyroscope, the acceleration data and the angular velocity data associated with the bodily motion of a user in three dimensions;

ii. a software module configured to transmit the acceleration data and angular velocity data to an exercise analysis server application;

b. a server processor configured to provide the exercise analysis server application comprising;

i. a database of statistical exercise models, the exercise models generated from acceleration data, angular velocity data, and repetitions of the user performing a defined exercise;

ii. a software module configured to receive acceleration data and angular velocity data from the personal computing device;

iii. a software module configured to apply a probabilistic analysis to the acceleration data and the angular velocity data to identify an exercise event, classify the exercise by comparison to one or more of the statistical exercise models, and simultaneously identify repetitions of the exercise;

iv. a software module configured to apply a statistical analysis to the acceleration data and the angular velocity data to score the user'"'"'s exercise form.

21. The platform of claim 20, comprising at least 100, at least 1000, or at least 10,000 personal computing devices.

22. The platform of claim 20, wherein the personal exercise analysis application further comprises a software module configured to place the personal computing device in a learning mode, the learning mode comprising recording the acceleration data and the angular velocity data of the user performing a defined exercise to generate a statistical exercise model for the exercise.

23. The platform of claim 20, wherein the database of statistical exercise models comprises at least 10, at least 50, at least 100, or at least 500 exercise models, each exercise model associated with a particular exercise.

24. The platform of claim 20, wherein each exercise model is generated from averaged data from a plurality of users performing a defined exercise.

25. The platform of claim 20, wherein one or more exercise models are generated from data from one or more qualified fitness professionals performing a defined exercise.

26. The platform of claim 25, wherein one or more exercise models are generated from data from a qualified fitness professional performing a defined exercise incorrectly to mimic common exercise form problems.

27. The platform of claim 25, wherein one or more exercise models are generated from data from a qualified fitness professional performing a defined exercise correctly to illustrate exemplary exercise form.

28. The platform of claim 20, wherein the personal exercise analysis application or the server application further comprises a software module configured to present an interface allowing the user to create a personal profile, the profile comprising weight, height, gender, wingspan, and fitness expertise.

29. Non-transitory computer-readable storage media encoded with instructions executable by a processor to perform personal exercise analysis, the instructions comprising:

a. a software module configured to receive data from a personal computing device comprising an accelerometer and a gyroscope, the data comprising acceleration data from the accelerometer and angular velocity data from the gyroscope, the acceleration data and the angular velocity data associated with the bodily motion of a user in three dimensions;

b. a software module configured to place the device in a learning mode, the learning mode comprising recording the acceleration data, angular velocity data, and repetitions of the user performing a defined exercise to generate a statistical exercise model for the exercise;

c. a software module configured to place the device in a normal mode, the normal mode comprising applying a probabilistic analysis to the acceleration data and the angular velocity data to identify an exercise event, classify the exercise by comparison to a recorded exercise model, and simultaneously identify repetitions of the exercise; and

d. a software module configured to apply a statistical analysis to the acceleration data and the angular velocity data to score the user'"'"'s exercise form.

30. Non-transitory computer-readable storage media encoded with a computer program including instructions executable by a processor to create an exercise analysis server application comprising:

a. a database of statistical exercise models, the exercise models generated from acceleration data, angular velocity data, and repetitions transmitted by a personal computing device associated with a user performing a defined exercise, the acceleration data and the angular velocity data each comprising data for X, Y, and Z-axes, the device in a learning mode;

b. a software module configured to receive acceleration data and angular velocity data transmitted by the personal computing device associated with a user performing a defined exercise, the device in a normal mode;

c. a software module configured to apply a probabilistic analysis to the received acceleration data and angular velocity data to identify an exercise event, classify the exercise by comparison to one or more of the statistical exercise models, and simultaneously identify repetitions of the exercise;

d. a software module configured to apply a statistical analysis to the received acceleration data and angular velocity data to score the user'"'"'s exercise form.