METHOD AND APPARATUS FOR QUANTITATIVE ASSESSMENT OF NEUROMOTOR DISORDERS

• US 20100113979A1
• Filed: 03/07/2008
• Published: 05/06/2010
• Est. Priority Date: 03/09/2007
• Status: Active Grant

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Edgerton, Victor R., Jafari, Roozbeh, Jindrich, Devin L., Sarrafzadeh, Majid, Dabiri, Foad
• Granted Patent: US 8,845,554 B2
• Time in Patent Office: Days
• US Class Current: 600/587
• CPC Class Codes: A61B 2562/043 in a linear array
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• 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 5/225 of the fingers, e.g. by monitoring hand-grip force
• CPC Class Codes: A61B 5/6825 Hand

Abstract

The present invention relates to methods and apparatus for quantitative assessment of neuromotor disorders using sensors and analyzing the data collected from the sensors to determine if a patient suffers any neuromotor disorders. In one embodiment, the present invention is a system for assessing neuromotor disorders in a body including a plurality of pressure sensors adapted for attachment to the body and measuring pressure, a med node connected to the plurality of pressure sensors for generating data corresponding to the plurality of pressure sensors, and an analysis unit connected to the med node for analyzing the data generated by the med node to determine the existence of a nueromotor disorder in the body.

Claims

1. A system for assessing neuromotor disorders in a body, comprising:

a plurality of pressure sensors adapted for attachment to the body and measuring pressure;

a med node connected to the plurality of pressure sensors for generating data corresponding to the plurality of pressure sensors; and

an analysis unit connected to the med node for analyzing the data generated by the med node to determine the existence of a nueromotor disorder in the body.

2. The apparatus of claim 1 wherein the analysis unit analyzes the data generated by the med node using frequency matching.

3. The apparatus of claim 1 wherein the analysis unit analyzes the data generated by the med node using time-domain cross-correlation.

4. The apparatus of claim 1 wherein the analysis unit analyzes the data generated by the med node using variance measure.

5. The apparatus of claim 1 wherein the analysis unit analyzes the data generated by the med node using force sharing.

6. The apparatus of claim 1 wherein the analysis unit analyzes the data generated by the med node using frequency matching, time-domain cross-correlation, variance measure, and force sharing.

7. The apparatus of claim 1 wherein the analysis unit is a handheld device.

8. A method of determining abnormal pressure pattern in a body, the steps of the method, comprising:

sensing pressure from the body;

generating data from the sensed pressure; and

analyzing the generated data.

9. The method of claim 8 further comprising analyzing the generated data from the sensed pressure to determine the existence of a nueromotor disorder in the body.

10. The method of claim 9 wherein analyzing the generated data from the sensed pressure includes using frequency matching.

11. The method of claim 9 wherein analyzing the generated data from the sensed pressure includes using time-domain cross-correlation.

12. The method of claim 9 wherein analyzing the generated data from the sensed pressure includes using variance measure.

13. The method of claim 9 wherein analyzing the generated data from the sensed pressure includes using force sharing.

14. The method of claim 9 wherein analyzing the generated data from the sensed pressure includes using frequency matching, time-domain cross-correlation, variance measure, and force sharing.