Creating Personalized Athletic-Performance Models

• US 20170189756A1
• Filed: 12/30/2015
• Published: 07/06/2017
• Est. Priority Date: 12/30/2015
• Status: Active Grant

• Current Assignee: IAM Sports & Entertainment
• Original Assignee: IAM Sports & Entertainment
• Inventors: Brothers, Raymond Joseph, Gdoutos, Eleftherios E., Mulliken, Adam, Pye, John
• Granted Patent: US 10,010,753 B2
• Time in Patent Office: Days
• CPC Class Codes: A63B 2024/0012 Comparing movements or motion sequences with a registered reference
• CPC Class Codes: A63B 2209/10 with adhesive type surfaces, i.e. hook and loop-type fastener
• CPC Class Codes: A63B 2220/12 Absolute positions, e.g. by using GPS
• CPC Class Codes: A63B 2220/13 Relative positions
• CPC Class Codes: A63B 2220/24 Angular displacement
• CPC Class Codes: A63B 2220/40 Acceleration
• CPC Class Codes: A63B 2220/53 Force of an impact, e.g. blow or punch
• CPC Class Codes: A63B 2220/803 Motion sensors
• CPC Class Codes: A63B 2220/805 Optical or opto-electronic sensors
• CPC Class Codes: A63B 2220/806 Video cameras
• CPC Class Codes: A63B 2220/836 Sensors arranged on the body of the user
• CPC Class Codes: A63B 2225/50 Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/10 electroencephalographic signals
• CPC Class Codes: A63B 2230/60 muscle strain, i.e. measured on the user
• CPC Class Codes: A63B 2243/0037 Basketball
• CPC Class Codes: A63B 24/0006 Computerised comparison for qualitative assessment of motion sequences or the course of a movement
• CPC Class Codes: A63B 24/0075 Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases
• CPC Class Codes: A63B 43/008 with means for improving visibility, e.g. special markings or colours illuminating or reflecting means A63B43/06
• CPC Class Codes: A63B 69/0071 for basketball
• CPC Class Codes: A63B 69/3623 for driving A63B69/3608, A63B69/3658, A63B69/3661, A63B69/3667 and A63B69/3691 take precedence
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06V 10/245 : by locating a pattern; Special marks for positioning
• CPC Class Codes: G06V 20/20 : in augmented reality scenes
• CPC Class Codes: G06V 40/23 : Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G06V 40/28 : Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113

Abstract

In one embodiment, a method includes receiving, from one or more sensors, biomechanical data of a first user performing a plurality of actions of a first action-type and outcome data of a plurality of outcomes corresponding to the respective plurality of actions. A plurality of sets of action-parameter values may be determined based on the received biomechanical data, where each of the sets of action-parameter values corresponds to a respective action of the plurality of actions. An athletic-performance model of the first user may be generated based on the plurality of sets of action-parameter values. The athletic-performance model may be a statistical model including probabilities computed with respect to the sets of action-parameter values and the outcome data.

Claims

1. A method comprising, by one or more computing devices:

receiving, from one or more sensors, biomechanical data of a first user performing a plurality of actions of a first action-type and outcome data of a plurality of outcomes corresponding to the respective plurality of actions;

determining, based on the received biomechanical data, a plurality of sets of action-parameter values corresponding to the respective plurality of actions; and

generating an athletic-performance model of the first user based on the plurality of sets of action-parameter values, wherein the athletic-performance model is a statistical model comprising probabilities computed with respect to the sets of action-parameter values and the outcome data.

2. The method of claim 1, further comprising:

receiving, from the one or more sensors in real time, biomechanical data of the first user performing a first action of the first action-type and outcome data of an outcome corresponding to the first action;

determining, based on the received biomechanical data, a first set of action-parameter values; and

dynamically updating the athletic-performance model based on the first set of action-parameter values.

3. The method of claim 2, wherein dynamically updating the athletic-performance model comprises comparing the first set of action-parameter values to the plurality of sets of action-parameter values.

4. The method of claim 2, wherein dynamically updating the athletic-performance model comprises determining an updated current skill level of the first user.

5. The method of claim 4, wherein the updated current skill level is based on one or more measures of variance computed with respect to the plurality of sets of action-parameter values and the first set of action-parameter values.

6. The method of claim 2, further comprising generating a report of athletic-performance feedback for the first user based on the updated athletic-performance model.

7. The method of claim 6, wherein the report is further based on a comparison of the athletic-performance model to the updated athletic-performance model.

8. The method of claim 2, further comprising generating an athletic-performance model of a second user based on a plurality of sets of action-parameter values of the second user, the plurality of sets of action-parameter values of the second user being determined based on biomechanical data of the second user performing a plurality of actions of the first action-type and outcome data of a plurality of outcomes corresponding to the respective plurality of actions.

9. The method of claim 8, further comprising:

generating a report of athletic-performance feedback for the first user based on a comparison of the updated athletic-performance model to the athletic-performance model of the second user.

10. The method of claim 9, wherein the comparison comprises comparing the first set of action-parameter values and the plurality of sets of action-parameter values of the first user to the plurality of sets of action-parameter values of the second user.

11. The method of claim 1, wherein the one or more sensors comprise a point-of-view camera positioned on the first user, and wherein the first action-type is a basketball shot.

12. The method of claim 11, wherein the action parameters comprise a release angle of a basketball, the basketball being used by the first user in performing an action of the basketball shot.

13. The method of claim 12, wherein the action parameters comprise a release velocity of a basketball.

14. The method of claim 1, further comprising generating an n-dimensional plot based on the updated athletic-performance model, each of the n dimensions corresponding to an action parameter.

15. The method of claim 14, wherein the n-dimensional plot comprises a plurality of probability contours, and wherein each probability contour represents a likelihood of a positive outcome for particular sets of action-parameter values.

16. The method of claim 15, wherein the n-dimensional plot comprises a target area, the target area being defined based on a probability contour representing a high likelihood of a positive outcome for particular sets of action-parameter values.

17. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

receive, from one or more sensors, biomechanical data of a first user performing a plurality of actions of a first action-type and outcome data of a plurality of outcomes corresponding to the respective plurality of actions;

determine, based on the received biomechanical data, a plurality of sets of action-parameter values corresponding to the respective plurality of actions; and

generate an athletic-performance model of the first user based on the plurality of sets of action-parameter values, wherein the athletic-performance model is a statistical model comprising probabilities computed with respect to the sets of action-parameter values and the outcome data.

18. The media of claim 17, wherein the software is further operable when executed to:

receive, from the one or more sensors in real time, biomechanical data of the first user performing a first action of a first action-type and outcome data of an outcome corresponding to the first action;

determine, based on the received biomechanical data, a first set of action-parameter values; and

dynamically update the athletic-performance model based on the first set of action-parameter values.

19. A system comprising:

one or more processors; and

a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;

receive, from one or more sensors, biomechanical data of a first user performing a plurality of actions of a first action-type and outcome data of a plurality of outcomes corresponding to the respective plurality of actions;

determine, based on the received biomechanical data, a plurality of sets of action-parameter values corresponding to the respective plurality of actions; and

generate an athletic-performance model of the first user based on the plurality of sets of action-parameter values, wherein the athletic-performance model is a statistical model comprising probabilities computed with respect to the sets of action-parameter values and the outcome data.

20. The system of claim 19, wherein the processors are further operable when executing the instructions to:

receive, from the one or more sensors in real time, biomechanical data of the first user performing a first action of a first action-type and outcome data of an outcome corresponding to the first action;