Monitoring Performance and Generating Feedback with Athletic-Performance Models

• US 20170189757A1
• 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 10,004,949 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 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 41/08 Ball covers; Closures therefor
• 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: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• 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 accessing an athletic-performance model of a first user, where the model is based on a plurality of sets of action-parameter values of action parameters of the first user. The action-parameter values may be determined based on biomechanical data of the first user performing a plurality of actions of a first action-type and outcome data of each action. A current skill level of the first user may be determined based on a measure of variances associated with action parameters of the first action-type. Target ranges of action-parameter values may be calculated for action parameters based on the athletic-performance model. Each target range may be based on a measure of probability with respect to the particular action parameters and the outcome data. A report of athletic-performance feedback may be generated and may include current skill level information and the target ranges of action-parameter values.

Claims

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

accessing an athletic-performance model of a first user, wherein the athletic-performance model is based on a plurality of sets of action-parameter values of action parameters of the first user, the plurality of sets of action-parameter values being determined based on biomechanical data of the 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 a current skill level of the first user based on the athletic-performance model of the first user, wherein the current skill level is determined based on a measure of one or more variances associated with one or more action parameters of the first action-type;

calculating one or more target ranges of action-parameter values for one or more action parameters based on the athletic-performance model, wherein each target range is based on a measure of probability with respect to the particular action parameters and the outcome data; and

generating a report of athletic-performance feedback, the report comprising information related to the current skill level of the first user and the one or more target ranges of action-parameter values.

2. The method of claim 1, wherein the report comprises trend information of the first user, the trend information being determined based on an analysis of the plurality of sets of action-parameter values of the first user.

3. The method of claim 1, wherein the report comprises at least one suggested modification of a biomechanical act of the first user, the biomechanical act being associated with the first user performing an action of the first action-type.

4. The method of claim 3, wherein the at least one suggested modification of a biomechanical act of the first user is based on a comparison between the athletic-performance model of the first user and an athletic-performance model of a second user.

5. The method of claim 4, wherein the athletic-performance model of the second user comprises a plurality of sets of action-parameter values of action parameters of the second user, the plurality of sets of action-parameter values 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, wherein the outcome data of the second user corresponds to positive outcomes.

6. The method of claim 1, wherein the first action-type is a basketball shot.

7. The method of claim 6, 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 first action-type.

8. The method of claim 7, wherein the action parameters comprise a release velocity of a basketball.

9. The method of claim 8, wherein the report of athletic-performance feedback comprises a target range of release angle values and a target range of release velocity values for the first user performing an action of the basketball-shot type.

10. The method of claim 1, wherein determining the current skill level of the first user comprises calculating a measure of consistency for the first action-type for the first user, the measure of consistency being computed based on one or more variance values, each variance value being determined based on action-parameter values of an action parameter.

11. The method of claim 1, further comprising sending the report to a client system of the first user for display to the first user.

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

access an athletic-performance model of a first user, wherein the athletic-performance model is based on a plurality of sets of action-parameter values of action parameters of the first user, the plurality of sets of action-parameter values being determined based on biomechanical data of the 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 a current skill level of the first user based on the athletic-performance model of the first user, wherein the current skill level is determined based on a measure of one or more variances associated with one or more action parameters of the first action-type;

calculate one or more target ranges of action-parameter values for one or more action parameters based on the athletic-performance model, wherein each target range is based on a measure of probability with respect to the particular action parameters and the outcome data; and

generate a report of athletic-performance feedback, the report comprising information related to the current skill level of the first user and the one or more target ranges of action-parameter values.

13. The media of claim 12, wherein the report comprises trend information of the first user, the trend information being determined based on an analysis of the plurality of sets of action-parameter values of the first user.

14. 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;

access an athletic-performance model of a first user, wherein the athletic-performance model is based on a plurality of sets of action-parameter values of action parameters of the first user, the plurality of sets of action-parameter values being determined based on biomechanical data of the 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 a current skill level of the first user based on the athletic-performance model of the first user, wherein the current skill level is determined based on a measure of one or more variances associated with one or more action parameters of the first action-type;

calculate one or more target ranges of action-parameter values for one or more action parameters based on the athletic-performance model, wherein each target range is based on a measure of probability with respect to the particular action parameters and the outcome data; and

generate a report of athletic-performance feedback, the report comprising information related to the current skill level of the first user and the one or more target ranges of action-parameter values.

15. The system of claim 14, wherein the report comprises trend information of the first user, the trend information being determined based on an analysis of the plurality of sets of action-parameter values of the first user.