Method and system for assessing locomotive bio-rhythms

• US 20070021689A1
• Filed: 07/19/2005
• Published: 01/25/2007
• Est. Priority Date: 07/19/2005
• Status: Active Grant

• Current Assignee: Board of Regents of the University of Nebraska
• Original Assignee: The University of Nebraska Medical Center
• Inventors: Kurz, Maximilian, Stergiou, Nikolaos
• Granted Patent: US 9,179,862 B2
• Time in Patent Office: Days
• US Class Current: 600/595
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method, system, and article of manufacture for detecting, recording, quantifying, and classifying gait data. In one embodiment, the method of the invention includes a step of detecting foot contact events using a portable sensor device. Stride data is collected by processing the detected foot contact events to obtain and record gait pattern data. At least one gait stability value is determined from the gait pattern data using a specified gait stability metric. The gait stability value is preferably a mathematical chaos value which is processed utilizing clustering correlation to classify the gait pattern data in association with a neuromuscular status. In a preferred embodiment, fuzzy logic is utilized to correlate the determined gait stability value with a plurality of gait stability values each associated with a distinct neuromuscular status.

Claims

1. A method for analyzing gait comprising:

determining a gait stability value from gait pattern data using a specified gait stability metric; and

processing the gait stability value utilizing clustering correlation to classify the gait pattern data in association with a neuromuscular status.

2. The method of claim 1, further comprising collecting the gait pattern data, said collecting comprising detecting and recording foot contact events.

3. The method of claim 2, said collecting further comprising processing the recorded foot contact events to obtain multiple stride interval data.

4. The method of claim 1, wherein said specified gait stability metric is a chaos metric.

5. The method of claim 4, wherein the chaos metric is included among the group including Lyapunov exponent and a correlation dimension.

6. The method of claim of claim 4, wherein processing the gait stability value comprises using a fuzzy logic clustering technique to correlate the gait stability value with a plurality of gait stability values each associated with a distinct neuromuscular status.

7. The method of claim 6, wherein the fuzzy logic clustering technique is included in the group comprising fuzzy subtractive clustering and fuzzy c-means clustering.

8. A system for analyzing gait comprising:

an analyzer module for determining a gait stability value from gait pattern data using a specified gait stability metric; and

a clustering correlation module for processing the gait stability value utilizing clustering correlation to classify the gait pattern data in association with a neuromuscular status.

9. The system of claim 8, further comprising a gait monitor device that collects the gait pattern data, wherein said collecting comprises detecting and recording foot contact events.

10. The system of claim 8, wherein said specified gait stability metric is a chaos metric.

11. The system of claim 10, wherein the chaos metric is included among the group including Lyapunov exponent and a correlation dimension.

12. The system of claim of claim 10, wherein said cluster correlation module utilizes a fuzzy logic clustering technique to correlate the gait stability value with a plurality of gait stability values each associated with a distinct neuromuscular status.

13. The system of claim 12, wherein the fuzzy logic clustering technique is included in the group comprising fuzzy subtractive clustering and fuzzy c-means clustering.

14. A computer-readable medium having encoding thereon computer-executable instructions for analyzing gait, said computer-executable instructions performing a method comprising:

determining a gait stability value from gait pattern data using a specified gait stability metric; and

processing the gait stability value utilizing clustering correlation to classify the gait pattern data in association with a neuromuscular status.

15. The computer-readable medium of claim 14, wherein said method further comprises collecting the gait pattern data, said collecting comprising detecting and recording foot contact events.

16. The computer-readable medium of claim 15, said collecting further comprising processing the recorded foot contact events to obtain multiple stride interval data.

17. The computer-readable medium of claim 14, wherein said specified gait stability metric is a chaos metric.

18. The computer-readable medium of claim 17, wherein the chaos metric is included among the group including Lyapunov exponent and a correlation dimension.

19. The computer-readable medium of claim of claim 18, wherein processing the gait stability value comprises using a fuzzy logic clustering technique to correlate the gait stability value with a plurality of gait stability values each associated with a distinct neuromuscular status.

20. The computer-readable medium of claim 19, wherein the fuzzy logic clustering technique is included in the group comprising fuzzy subtractive clustering and fuzzy c-means clustering.