SYSTEM FOR MOVEMENT SKILL ANALYSIS AND SKILL AUGMENTATION AND CUEING

• US 20170061817A1
• Filed: 08/25/2016
• Published: 03/02/2017
• Est. Priority Date: 08/28/2015
• Status: Active Grant

• Current Assignee: icuemotion, LLC
• Original Assignee: icuemotion, LLC
• Inventors: METTLER MAY, Berenice
• Granted Patent: US 10,854,104 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/1107 Measuring contraction of parts of the body, e.g. organ, muscle apparatus for measuring work or force in general G01L
• CPC Class Codes: A61B 5/1122 of movement trajectories
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/1124 Determining motor skills
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: G09B 19/0015 Dancing
• CPC Class Codes: G09B 19/003 Repetitive work cycles; Sequence of movements

Abstract

A movement augmentation system that provides various forms of feedback, including real-time and post-performance for training and rehabilitation is disclosed. The systems, devices and methods provide augmentation to human movement behavior in order to accelerate acquisition of complex movement skills by a user and to mitigate injuries from poor movement technique (e.g., joint damage or muscle fatigue). An environment is provided that enables systematic and comprehensive capabilities through integration of its components.

Claims

1. A motion analysis system comprising:

one or more sensors configured to obtain motion data;

a processor in communication with one or more sensors, the processor configured to;

collect a motion data from the one or more sensors;

parse the collected motion data into one or more movement units;

model the one or more movement units;

analyze the one or more movement units;

compare the one or more movement units to one or more of a prior movement measurement and a library of movement measurements to generate a comparison;

identify one or more aspects of the motion data for change based on the comparison; and

provide feedback to a user.

2. The motion analysis system of claim 1 wherein the motion data is a movement within a repertoire of user movements.

3. The motion analysis system of claim 1 wherein the one or more sensors is selected from one or more of each of a wearable sensor and a remote sensor.

4. The motion analysis system of claim 3 wherein the wearable sensor is configurable to sense one or more of a velocity of the motion, an orientation for the motion, a gravitational force, and an electrical activity of a muscle.

5. The motion analysis system of claim 1 wherein the feedback provided to the user is one or more of visible, haptic, and audible.

6. The motion analysis system of claim 1 wherein one or more movement features are computed that impact a quality or outcome of the feedback.

7. The motion analysis system of claim 6 wherein the one or more movement features are one of a phase transition or phase profile attribute.

8. The motion analysis system of claim 1 wherein the feedback provided to the user is one or more of a phase transition cue, a phase profile cue, an alert, and an outcome validation cue.

9. The motion analysis system of claim 1 wherein the feedback is provided real-time.

10. The motion analysis system of claim 8 wherein the alert is computed from the map of movement feature to biomechanical features (e.g. to protect from injury or wear).

11. The motion analysis system of claim 1 wherein the one or more movement units is segmented into two or more movement phases.

12. The motion analysis system of claim 10 further comprising decomposing the two or more movement phases into two or more synergies wherein the two or more synergies are one or more of each of biomechanical relationships and neuro-muscular relationships.

13. The motion analysis system of claim 1 wherein a local host device communicates one or more of a training information and a cuing information to the user.

14. The motion analysis system of claim 1 further comprising prioritizing one or more parsed motion data for change based on whether a feature of the movement unit is changeable and will impact a quality or outcome of a repeated movement.

15. The motion analysis system of claim 1 wherein the collected motion data is one or more of a user motion data and a user controlled device motion data.

16. A cue processor wherein the cue processor is configured to:

generate a movement phase estimation which provides a prediction of a movement phase and an associated feature,

extract a movement phase feature,

apply a movement cuing law; and

generate a movement cue,

wherein the movement phase estimation and the movement phase feature extraction includes one or more of a phase initiation predictor, an initial phase state extractor, a phase profile parameter extractor, and an outcome extractor.

17. The cue processor of claim 16 wherein the cuing law at least one of compares a reference timing, compares a target state value, compares a reference profile, and compares a target outcome.

18. The cue processor of claim 16 wherein the cue generator generates one or more of a phase transition cue, a phase profile cue, an alert and an outcome validation cue.

19. The cue processor of claim 16 wherein the cue processor is incorporated into a stand-alone device.

20. The cue processor of claim 16 wherein the cue processor is part of a cuing system having a phase state estimator, a cue encoder and a transducer.

21. The cue processor of claim 16 wherein the cue is prioritized based on one or more of decomposed motion data for change based on whether a movement feature is changeable and will impact a quality of a repeated movement.

22. A motion training program comprising:

one or more sensor inputs configurable to collect motion data;

a computerized motion training program, wherein the computerized motion training program is configured to;

parse the collected motion data into one or more movement units, compare the one or more movement units to one or more of a prior movement measurement and a library of movement measurements to generate a comparison, and present at least one of a training assessment and training instruction to the user about one or more of a repertoire movement, a motion phase segment, and a movement synergy.

23. The motion training program of claim 22 wherein the program is configurable to provide a plurality of prompts to a user during a sensed motion.

24. The motion training program of claim 22 wherein the computerized motion training is iterative.

25. The motion training program of claim 22 wherein an output of the program is a training schedule and one or more instructions.

27. The motion training program of claim 22 wherein the collected motion data is one or more of a user motion data and a user controlled device motion data.

26. The motion training program of 25 wherein the training schedule identifies one or more patterns in a repertoire movement to be practiced by a user.

28. A method of training comprising:

collecting a motion data from one or more sensors;

parsing the collected motion data into one or more movement units;

modelling the one or more movement units;

analyzing the one or more movement units;

comparing the one or more movement units to one or more of a prior movement measurement and a library of movement measurements; and

identifying one or more aspects of the motion data for change based on the comparison.

29. The method of claim 28 further comprising the step of providing feedback to the user.

30. The method of claim 29 wherein the feedback is provided real-time.

31. The method of claim 28 wherein the feedback is at least one of an instruction and a cue.

32. The method of claim 31 wherein the cue is one or more of visible, haptic, and audible.

33. The method of claim 28 further comprising the step of generating a training schedule based on the one or more parsed motion data identified for change.

34. The method of claim 28 wherein the one or more movement units is segmented into two or more movement phases.

35. The method of claim 34 further comprising decomposing the two or more movement phases into two or more synergies wherein the two or more synergies are one or more of each of biomechanical relationships and neuro-muscular relationships.

36. The method of claim 28 wherein a local host device communicates one or more of a training information and a cuing information to the user.

37. The method of claim 28 wherein the collected motion data is one or more of a user motion data and a user controlled device motion data.