SYSTEMS AND METHODS FOR PROVIDING A NEURAL-MACHINE INTERFACE FOR ARTIFICIAL LEGS

• US 20120310370A1
• Filed: 07/24/2012
• Published: 12/06/2012
• Est. Priority Date: 01/25/2010
• Status: Abandoned Application

• Current Assignee: Rhode Island Board of education (STATE OF RHODE ISLAND)
• Original Assignee: Rhode Island Board of education (STATE OF RHODE ISLAND)
• Inventors: Huang, He, Yang, Qing K., Sun, Yan
• Application Number:
  US13/556,527
• Publication Number:
  US 20120310370A1
• Time in Patent Office: Days
• US Class Current: 623/25
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61F 2/60 Artificial legs or feet or parts thereof
• CPC Class Codes: A61F 2/72 Bioelectric control, e.g. myoelectric
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06V 40/25 Recognition of walking or running movements, e.g. gait recognition
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A neural-machine interface system is disclosed for providing control of a leg prosthesis. The system includes a plurality of input channels for receiving electromyographic signals from a subject, a feature vector formation unit for processing the electromyographic signals, and a pattern classification unit for identifying the intended movement of the subject'"'"'s leg prosthesis.

Claims

1. A neural-machine interface system for providing control of a leg prosthesis, said system comprising a plurality of input channels for receiving electromyographic signals from a subject, feature vector formation means for processing the electromyographic signals, and pattern classification means for identifying the intended movement of the subject'"'"'s leg prosthesis.

2. The system as claimed in claim 1, wherein said system further includes a sensor trust evaluation system that provides a trust valuation representative of the reliability of a sensor output signal.

3. The system as claimed in claim 2, wherein said trust evaluation system includes a detection system for identifying whether a sensor output includes a disturbance.

4. The system as claimed in claim 1, wherein said system is provided in an embedded controller.

5. The system as claimed in claim 1, wherein said pattern classification means includes a graphics processing unit.

6. The system as claimed in claim 5, wherein said graphics processing unit is a multi-core processing unit

7. The system as claimed in claim 1, wherein said system includes training means for training the system to recognize patterns of electromyographic signals as being associated with different motions.

8. A neural-machine interface system for providing control of a lower limb, said system comprising a plurality of input channels for receiving a plurality of sensor output signal from a subject, processing means for processing the plurality of sensor output signals, pattern classification means for identifying the intended movement of a subject'"'"'s leg, and sensor trust evaluation means for providing a trust valuation representative of the reliability of each of the plurality of sensor output signals.

9. The system as claimed in claim 8, wherein said trust evaluation system includes a detection system for identifying whether a sensor output includes a disturbance.

10. The system as claimed in claim 8, wherein said system is provided in an embedded controller.

11. The system as claimed in claim 10, wherein said system includes a graphics processing unit.

12. The system as claimed in claim 11, wherein said graphics processing unit is a multi-core processing unit.

13. The system as claimed in claim 8, wherein said system includes training means for training the system to recognize patterns of electromyographic signals as being associated with different motions.

14. A method of providing control of a leg prosthesis, said method comprising the steps of:

receiving a plurality of electromyographic signals at a plurality of input channels;

processing the plurality of electromyographic signals; and

identifying the intended movement of a subject'"'"'s leg prosthesis.

15. The method as claimed in claim 14, wherein said method further includes the step of providing a trust valuation representative of a reliability of each of the sensor output signals.

16. The method as claimed in claim 15, wherein said step of providing a trust valuation includes the step of identifying whether a sensor output includes a disturbance.

17. The method as claimed in claim 14, wherein said method is provided in an embedded controller.

18. The method as claimed in claim 17, wherein said method is further provided using a graphics processing unit.

19. The method as claimed in claim 18, wherein said graphics processing unit is a multi-core processing unit.

20. The method as claimed in claim 14, wherein said method includes the step of training a system to recognize patterns of electromyographic signals as being associated with different motions.