SYSTEM AND METHOD FOR USING PERFORMANCE SIGNATURES

• US 20170182360A1
• Filed: 12/22/2016
• Published: 06/29/2017
• Est. Priority Date: 12/27/2015
• Status: Active Grant

• Current Assignee: Lumo LLC
• Original Assignee: LUMO BodyTech, Inc.
• Inventors: Chang, Andrew Robert, Wang, Chung-Che Charles
• Granted Patent: US 10,463,909 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A63B 2024/0009 Computerised real time comparison with previous movements or motion sequences of the user
• CPC Class Codes: A63B 2024/0015 Comparing movements or motion sequences with computerised simulations of movements or motion sequences, e.g. for generating an ideal template as reference to be achieved by the user
• CPC Class Codes: A63B 2024/0093 the load of the exercise apparatus being controlled by performance parameters, e.g. distance or speed
• CPC Class Codes: A63B 2069/0008 for batting
• CPC Class Codes: A63B 2071/0625 Emitting sound, noise or music
• CPC Class Codes: A63B 2071/0655 Tactile feedback
• CPC Class Codes: A63B 21/06 User-manipulated weights
• CPC Class Codes: A63B 22/0242 with speed variation
• CPC Class Codes: A63B 22/025 electrically, e.g. D.C. motors with variable speed control
• CPC Class Codes: A63B 22/04 with movable multiple steps , i.e. more than one step per limb, e.g. steps mounted on endless loops, endless ladders steppers with cantilevered support elements pivoting about an axis A63B22/0048
• CPC Class Codes: A63B 22/0605 performing a circular movement, e.g. ergometers
• CPC Class Codes: A63B 22/0664 performing an elliptic movement
• CPC Class Codes: A63B 2220/10 Positions
• CPC Class Codes: A63B 2220/12 Absolute positions, e.g. by using GPS
• 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 2220/18 Inclination, slope or curvature
• CPC Class Codes: A63B 2220/20 Distances or displacements
• CPC Class Codes: A63B 2220/30 Speed
• CPC Class Codes: A63B 2220/34 : Angular speed
• 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 2220/70 : Measuring or simulating ambient conditions, e.g. weather, terrain or surface conditions
• CPC Class Codes: A63B 2220/73 : Altitude
• CPC Class Codes: A63B 2220/75 : Humidity
• CPC Class Codes: A63B 2220/76 : Wind conditions
• CPC Class Codes: A63B 2220/78 : Surface covering conditions, e.g. of a road surface
• CPC Class Codes: A63B 2220/803 : Motion sensors
• CPC Class Codes: A63B 2220/806 : Video cameras
• CPC Class Codes: A63B 2220/807 : Photo cameras
• CPC Class Codes: A63B 2220/833 : Sensors arranged on the exercise apparatus or sports implement
• 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 2244/09 : Weight-lifting
• CPC Class Codes: A63B 24/0087 : Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load
• CPC Class Codes: A63B 69/00 : Training appliances or apparatus for special sports training of parachutists B64D23/00
• CPC Class Codes: A63B 69/0002 : for baseball
• CPC Class Codes: A63B 69/002 : for football American-football dummies A63B69/34
• CPC Class Codes: A63B 69/0022 : for skating
• CPC Class Codes: A63B 69/0024 : for hockey
• CPC Class Codes: A63B 69/0028 : for running, jogging or speed-walking movable endless bands A63B22/02
• CPC Class Codes: A63B 69/0071 : for basketball
• CPC Class Codes: A63B 69/06 : for rowing or sculling
• CPC Class Codes: A63B 69/16 : for cycling , i.e. arrangements on or for real bicycles
• CPC Class Codes: A63B 69/18 : for skiing
• CPC Class Codes: A63B 69/3608 : Attachments on the body, e.g. for measuring, aligning, restraining A63B69/0059, A63B69/3673 take precedence
• CPC Class Codes: A63B 69/38 : for tennis A63B61/006, A63B69/0073 and A63B69/0097 take precedence
• CPC Class Codes: A63B 71/0622 : Visual, audio or audio-visual systems for entertaining, instructing or motivating the user
• CPC Class Codes: G06F 18/251 : of input or preprocessed data
• CPC Class Codes: G06F 21/32 : using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 2218/12 : Classification; Matching
• CPC Class Codes: G06F 7/02 : Comparing digital values G06F7/06, G06F7/22, G06F7/38 take precedence
• CPC Class Codes: G06V 40/23 : Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: H04L 63/0861 : using biometrical features, e.g. fingerprint, retina-scan cryptographic mechanisms or cryptographic arrangements for entity authentication using biological data H04L9/3231

Abstract

A system and method for using performance signatures can include generating a set of performance features during an activity of the first participant by collecting kinematic data from at least one inertial measurement unit and generating a set of biomechanical signals; combining the set of performance features into the performance signature; generating at least a second performance signature from a set of participants; comparing the performance signature of the first participant to at least the second performance signature; and applying a result of the comparison to an interaction with at least one participant.

Claims

1. A method comprising:

for a first participant performing an activity, generating a set of performance features during an activity of the first participant;

wherein at least a subset of the performance features are generated by collecting kinematic data from at least one inertial measurement unit, and generating a set of biomechanical signals that are, at least in part, based on the kinematic data, and

combining the set of performance features into a performance signature.

2. The method of claim 1, further comprising:

generating at least a second performance signature from a set of participants;

comparing the performance signature of the first participant to at least the second performance signature; and

applying a result of the comparison to an interaction with at least one participant.

3. The method of claim 2, further comprising selecting the second performance signature as a target performance signature of the first participant;

and wherein applying the result of the comparison comprises generating training recommendations to transition the first performance signature towards the target performance signature according to the comparison.

4. The method of claim 2, wherein applying the result of the comparison to an interaction with at least one participant comprises authenticating a user account of the at least one participant.

5. The method of claim 1, wherein the kinematic data is collected from a first inertial measurement unit positioned at a first location and a second inertial measurement unit positioned at a second location.

6. The method of claim 1, wherein the performance signature is for a repetitive action;

and wherein generating a set of biomechanical signals comprises segmenting the kinematic data into a consecutive sequence of actions.

7. The method of claim 6, wherein the performance signature is generated for sequences of actions when a participant pace metric is within a pace range.

8. The method of claim 6, wherein the activity is running and wherein segmenting the kinematic data into a consecutive sequence of actions comprises segmenting the kinematic data into a consecutive sequence of running steps.

9. The method of claim 8, wherein the biomechanical signals are ground contact time, braking, pelvic rotation, pelvic tilt, pelvic drop, vertical oscillation of the pelvis, and forward velocity properties of the pelvis.

10. The method of claim 8, further comprising collecting location information during the activity;

mapping the location information to a terrain classification; and

wherein generating a performance signature comprises generating at least a first signature of the participant for a first terrain classification and a second signature of the participant for a second terrain classification.

11. The method of claim 6, wherein the activity is swimming and wherein segmenting the kinematic data into a consecutive sequence of actions comprises segmenting the kinematic data into a consecutive sequence of swimming strokes.

12. The method of claim 6, wherein the activity is biking and wherein segmenting the kinematic data into a consecutive sequence of actions comprises segmenting the kinematic data into a consecutive sequence of pedal strokes.

13. The method of claim 1, wherein the performance signature is an isolated action;

and wherein generating a set of biomechanical signals comprises selecting a window of kinematic data associated with an action.

14. The method of 13, wherein the activity is golfing and wherein the window of kinematic data is associated with a golf swing.

15. The method of 13, wherein the activity is weight lifting and wherein the window of kinematic data is associated with a weight lifting action.

16. A system comprising:

an activity monitor device coupled to a location affected by the action of the participant; and

a processor communicatively coupled to the activity monitor, wherein the processor is configured to;

isolate kinematic data associated with an action of an activity,

generate a set of biomechanical signals, and

assemble a set of performance features, of which at least a subset of the performance features are based on the set of biomechanical signals, and generate a performance signature with the set of performance features.

17. The system of claim 16, further comprising:

a computing platform configured to;

collect multiple performance signatures from a plurality of participants,

receive performance signature queries, and

generate a comparison between a performance signature query and at least a second performance signature.

18. The system of claim 16, wherein the performance signature is for a repetitive action;

and wherein the processor is configured to segment the kinematic data into a consecutive sequence of actions when generating a set of biomechanical signals.

19. The method of claim 18, wherein the activity is running and wherein the processor configured to segment the kinematic data into a consecutive sequence of actions is further configured to segment the kinematic data into a consecutive sequence of running steps.

20. The method of claim 16, wherein the performance signature is an isolated action;

and wherein the processor is configured to select a window of kinematic data associated with an action when generating a set of biomechanical signals.

21. The system of 20, wherein the activity is golfing and wherein the window of kinematic data is associated with a golf swing.