SYSTEMS, DEVICES, AND METHODS FOR INTERPRETING MOVEMENT

• US 20080045804A1
• Filed: 05/24/2006
• Published: 02/21/2008
• Est. Priority Date: 05/02/2005
• Status: Active Grant

• Current Assignee: University of Virginia Patent Foundation (University of Virginia)
• Original Assignee: University of Virginia
• Inventors: Williams, Mark E.
• Granted Patent: US 8,007,450 B2
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• CPC Class Codes: A61B 5/112 Gait analysis
• 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 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/4076 Diagnosing or monitoring particular conditions of the nervous system A61B5/4821, A61B5/4824 take precedence
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/4519 Muscles A61B5/389, A61B5/224 take precedence
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G06V 40/25 Recognition of walking or running movements, e.g. gait recognition

Abstract

According to some exemplary embodiments, a method or system can involve associating a plurality of biokinetographic comparison results with a first specific dysfunction from a group of specific dysfunctions, each of the biokinetographic comparison results obtained from a comparison of a biokinetographic value to a standard for a corresponding biokinetographic variable.

Claims

1. A method, comprising:

automatically associating a plurality of biokinetographic comparison results with a first specific dysfunction from a group of specific dysfunctions comprising;

Parkinson'"'"'s disease, hemiparesis, cerebellar disease, frontal lobe disease, arthritis, orthopedic pain, orthopedic injury, motor neuropathy, and myopathy, each of the biokinetographic comparison results obtained from an automatic comparison of a biokinetographic value to a standard for a corresponding biokinetographic variable, each biokinetographic value automatically determined from a biokinetographic data set comprising a plurality of scalar sums of acceleration values in each of three orthogonal directions, each scalar sum corresponding to a particular point in time.

2. The method of claim 1, further comprising obtaining the biokinetographic data set.

3. The method of claim 1, further comprising for each biokinetographic variable, determining a biokinetographic value from said biokinetographic data set.

4. The method of claim 1, further comprising comparing each biokinetographic value to the standard for the corresponding biokinetographic variable.

5. The method of claim 1, further comprising rendering the biokinetographic data.

6. The method of claim 1, further comprising rendering the plurality of biokinetographic comparison results.

7. The method of claim 1, further comprising diagnosing the first specific dysfunction.

8. The method of claim 1, further comprising assessing the first specific dysfunction.

9. The method of claim 1, further comprising determining a treatment for the first specific dysfunction.

10. The method of claim 1, further comprising providing a prognosis regarding the first specific dysfunction.

11. The method of claim 1, further comprising predicting a likelihood of falling.

12. The method of claim 1, further comprising identifying a second specific biokinetographic pattern.

13. The method of claim 1, further comprising associating the second specific biokinetographic pattern with a second specific dysfunction.

14. The method of claim 1, further comprising assessing the second specific dysfunction.

15. The method of claim 1, further comprising assessing an overall health status based on the plurality of biokinetographic comparison results.

16. The method of claim 1, wherein the first specific dysfunction is Parkinson'"'"'s disease.

17. The method of claim 1, wherein the first specific dysfunction is hemiparesis.

18. The method of claim 1, wherein the first specific dysfunction is cerebellar disease.

19. The method of claim 1, wherein the first specific dysfunction is frontal lobe disease.

20. The method of claim 1, wherein the first specific dysfunction is orthopedic pain.

21. The method of claim 1, wherein the first specific dysfunction is arthritis.

22. The method of claim 1, wherein the first specific dysfunction is orthopedic injury.

23. The method of claim 1, wherein the first specific dysfunction is motor neuropathy.

24. The method of claim 1, wherein the first specific dysfunction is myopathy.

25. The method of claim 1, wherein the first specific dysfunction is a psychological dysfunction.

26. The method of claim 1, wherein data for the biokinetographic data set is generated by a plurality of sensors adapted to sense different moving parts of a subject;

a recording device adapted to acquire motion data generated from the plurality of sensors when the subject moves;

a memory which stores the motion data acquired by the recording device; and

a processor configured to convert the motion data into biokinetographic data.

27. The method of claim 26, wherein the conversion of the motion data into biokinetographic data comprises graphing the scalar sums of acceleration from each sensor over time.

28. A system configured to perform the method according to claim 1.

29. A machine-readable medium comprising machine instructions for activities comprising:

automatically associating a plurality of biokinetographic comparison results with a first specific dysfunction from a group of specific dysfunctions comprising;

Parkinson'"'"'s disease, hemiparesis, cerebellar disease, frontal lobe disease, arthritis, orthopedic pain, orthopedic injury, motor neuropathy, and myopathy, each of the biokinetographic comparison results obtained from an automatic comparison of a biokinetographic value to a standard for a corresponding biokinetographic variable, each biokinetographic value automatically determined from a biokinetographic data set comprising a plurality of scalar sums of acceleration values in each of three orthogonal directions, each scalar sum corresponding to a particular point in time.

30. A signal embodied in an electromagnetic wave, said signal adapted to cause an information device to:

automatically associate a plurality of biokinetographic comparison results with a first specific dysfunction from a group of specific dysfunctions comprising;

Parkinson'"'"'s disease, hemiparesis, cerebellar disease, frontal lobe disease, arthritis, orthopedic pain, orthopedic injury, motor neuropathy, and myopathy, each of the biokinetographic comparison results obtained from an automatic comparison of a biokinetographic value to a standard for a corresponding biokinetographic variable, each biokinetographic value automatically determined from a biokinetographic data set comprising a plurality of scalar sums of acceleration values in each of three orthogonal directions, each scalar sum corresponding to a particular point in time.

31. A method, comprising:

automatically;

obtaining biokinetographic data;

analyzing said biokinetographic data; and

identifying a first specific biokinetographic pattern; and

associating the first specific biokinetographic pattern with a first specific dysfunction from a group of specific dysfunctions comprising;

Parkinson'"'"'s disease, hemiparesis, cerebellar disease, frontal lobe disease, arthritis, orthopedic pain, orthopedic injury, motor neuropathy, and myopathy.