SYSTEMS AND METHODS FOR REAL-TIME ADAPTIVE THERAPY AND REHABILITATION

• US 20140287389A1
• Filed: 03/14/2014
• Published: 09/25/2014
• Est. Priority Date: 03/14/2013
• Status: Abandoned Application

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Han, Jay, Kallmann, Marcelo, Camporesi, Carlo
• Application Number:
  US14/214,483
• Publication Number:
  US 20140287389A1
• Time in Patent Office: Days
• US Class Current: 434/247
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Virtual reality-based adaptive systems and methods are disclosed for improving the delivery of physical therapy and rehabilitation. The invention comprises an interactive software solution for tracking, monitoring and logging user performance wherever sensor capability is present. To provide therapists with the ability to observe and analyze different motion characteristics from the exercises performed by patients, novel visualization techniques are provided for specific solutions. These visualization techniques include color-coded therapist-customized visualization features for motion analysis.

Claims

1. A real-time adaptive virtual therapy and rehabilitation system, comprising:

(a) a computer;

(b) a sensor operably connected to the computer and configured for sensing one or more users'"'"' motion; and

(c) programming in a non-transitory computer readable medium and executable on the computer for performing steps comprising;

(i) acquiring and storing one or more discrete motions of a first user, said motions corresponding to an exercise;

(ii) mapping the acquired one or more discrete motions of the first user as a first avatar comprising a virtual representation of one or more anatomical features of the first user corresponding to said exercise;

(iii) acquiring and storing one or more discrete motions of a second user, said motions corresponding to said exercise;

(iv) mapping the acquired one or more discrete motions of the second user as a second avatar comprising a virtual representation of one or more anatomical features of the second user corresponding to said exercise; and

(v) comparing motion of the second avatar with respect to the second avatar.

2. A system as recited in claim 1, wherein comparing the motion of the second avatar with respect to the second avatar comprises displaying the second avatar overlapped with the first avatar.

3. A system as recited in claim 1, wherein comparing the motion of the second avatar with respect to the second avatar comprises providing visual feedback of the motion of the second avatar.

4. A system as recited in claim 3, wherein providing visual feedback comprises displaying a trajectory trail of at least one of the one or more anatomical features, said trajectory trail comprising a plurality of locations of an anatomical feature over time.

5. A system as recited in claim 3, wherein providing visual feedback comprises displaying an angle measurement corresponding to a joint relating to the one or more anatomical features.

6. A system as recited in claim 3, wherein providing visual feedback comprises displaying a distance measurement between an anatomical feature of the first avatar and an anatomical feature of the second avatar.

7. A system as recited in claim 3, wherein providing visual feedback comprises displaying a range of motion density map, said density map comprising data relating to the frequency of an anatomical feature passing over a series of points in space over a period of time.

8. A system as recited in claim 1, wherein mapping the acquired one or more discrete motions comprises:

generating a single character hierarchical skeleton representation corresponding to said first avatar; and

storing said one or more discrete motions in memory as a time-series M_i, iε

{1, . . . , n}, where each frame M_i is a vector with all joint angles defining one posture of the skeleton representation.

9. A system as recited in claim 8, wherein said programming further performs steps comprising, automatically analyzing the skeleton representation, and determining if the exercise can be parameterized based on analysis of the skeleton representation.

10. A system as recited in claim 9, wherein determining if the exercise can be parameterized comprises:

automatic detection of a first and second apices corresponding to points of maximum amplitude that are at intersection points between initial and return phases of the exercise; and

determining that the exercise can be parameterized if initial and return phases of the exercise can be segmented.

11. A system as recited in claim 10, wherein said programming further performs steps comprising, performing a run-time motion re-parameterization algorithm to change a motion characteristic of the exercise motion in real-time according to new parameters.

12. A system as recited in claim 10, wherein performing a run-time motion re-parameterization algorithm comprises:

segmenting the exercise into at least an initial phase and a return phase; and

re-parameterizing an amplitude characteristic with respect to the initial or return phase of the exercise.

13. A system as recited in claim 10, wherein performing a run-time motion re-parameterization algorithm comprises:

segmenting the exercise into at least an initial phase and a return phase; and

re-parameterizing a velocity characteristic with respect to the initial or return phase of the exercise.

14. A system as recited in claim 10, wherein performing a run-time motion re-parameterization algorithm comprises:

segmenting the exercise into at least an initial phase and a return phase; and

re-parameterizing a hold time characteristic with respect to the initial and return phase of the exercise.

15. A system as recited in claim 1, the wherein said programming further performs steps comprising:

(vi) providing a graphical user interface for the first user to select and group previously acquired exercises from the library of exercises and to create a therapy program for a patient; and

(vii) providing a set of automatic exercise delivery adaptation strategies for automatically adapting parameterized exercises to a therapy program.

16. A method for real-time adaptive virtual therapy and rehabilitation, comprising:

acquiring and storing one or more discrete motions of a first user, said motions corresponding to an exercise;

mapping the acquired one or more discrete motions of the first user as a first avatar comprising a virtual representation of one or more anatomical features of the first user corresponding to said exercise;

acquiring and storing one or more discrete motions of a second user, said motions corresponding to said exercise;

mapping the acquired one or more discrete motions of the second user as a second avatar comprising a virtual representation of one or more anatomical features of the second user corresponding to said exercise; and

comparing motion of the second avatar with respect to the second avatar and outputting the comparison for evaluation of said exercise by said second user.

17. A method as recited in claim 16, wherein comparing the motion of the second avatar with respect to the second avatar comprises displaying the second avatar overlapped with the first avatar.

18. A method as recited in claim 16, wherein comparing the motion of the second avatar with respect to the second avatar comprises providing visual feedback of the motion of the second avatar.

19. A method as recited in claim 18, wherein providing visual feedback comprises displaying a trajectory trail of at least one of the one or more anatomical features, said trajectory trail comprising a plurality of locations of an anatomical feature over time.

20. A method as recited in claim 19, wherein providing visual feedback comprises displaying an angle measurement corresponding to a joint relating to the one or more anatomical features.

21. A method as recited in claim 19, wherein providing visual feedback comprises displaying a distance measurement between an anatomical feature of the first avatar and an anatomical feature of the second avatar.

22. A method as recited in claim 19, wherein providing visual feedback comprises displaying a range of motion density map, said density map comprising data relating to the frequency of an anatomical feature passing over a series of points in space over a period of time.

23. A method as recited in claim 22, wherein density map is color coated to reflect varying colors corresponding to varying frequency values.

24. A method as recited in claim 16, wherein mapping the acquired one or more discrete motions comprises:

generating a single character hierarchical skeleton representation corresponding to said first avatar; and

storing said one or more discrete motions in memory as a time-series M_i, iε

{1, . . . , n}, where each frame M_i is a vector with all joint angles defining one posture of the skeleton representation.

25. A method as recited in claim 24, the method further comprising:

automatically analyzing the skeleton representation, and determining if the exercise can be parameterized based on analysis of the skeleton representation.

26. A method as recited in claim 25, wherein determining if the exercise can be parameterized comprises:

automatic detection of a first and second apices corresponding to points of maximum amplitude that are at intersection points between initial and return phases of the exercise; and

determining that the exercise can be parameterized if initial and return phases of the exercise can be segmented.

27. A method as recited in claim 25, the method further comprising:

performing a run-time motion re-parameterization algorithm to change a motion characteristic of the exercise motion in real-time according to new parameters.

28. A method as recited in claim 25, wherein performing a run-time motion re-parameterization algorithm comprises:

segmenting the exercise into at least an initial phase and a return phase; and

re-parameterizing an amplitude characteristic with respect to the initial or return phase of the exercise.

29. A method as recited in claim 25, wherein performing a run-time motion re-parameterization algorithm comprises:

segmenting the exercise into at least an initial phase and a return phase; and

re-parameterizing a velocity characteristic with respect to the initial or return phase of the exercise.

30. A method as recited in claim 25, wherein performing a run-time motion re-parameterization algorithm comprises:

segmenting the exercise into at least an initial phase and a return phase; and

re-parameterizing a hold time characteristic with respect to the initial and return phase of the exercise.

31. A method as recited in claim 16, the method further comprising:

providing a graphical user interface for the first user to select and group previously acquired exercises from the library of exercises and to create a therapy program for a patient; and

providing a set of automatic exercise delivery adaptation strategies for automatically adapting parameterized exercises to a therapy program.