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A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition.

Xiaorong Zhang1, He Huang2,3

  • 1School of Engineering, San Francisco State University, 1600 Holloway Ave, San Francisco, CA, USA. xrzhang@sfsu.edu.

Journal of Neuroengineering and Rehabilitation
|April 19, 2015
PubMed
Summary
This summary is machine-generated.

A new sensor fault-tolerant module (SFTM) improves surface electromyography (EMG) pattern recognition (PR) for prosthetic control. This practical SFTM is fast, automatic, and robust, ensuring reliable prosthesis function even with signal disturbances.

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Engineering
  • Signal Processing

Background:

  • Surface electromyography (EMG) signal unreliability poses challenges for clinical application of EMG pattern recognition (PR)-controlled prostheses.
  • Previous sensor fault-tolerant module (SFTM) designs were impractical due to speed, weight, and automation limitations.

Purpose of the Study:

  • To develop a real-time, practical SFTM for robust EMG PR in prosthetic control.
  • To address limitations of previous SFTM designs by enhancing speed, automation, and robustness.

Main Methods:

  • Developed a novel fast linear discriminant analysis (LDA) retraining algorithm.
  • Implemented a fully automatic sensor fault detector using outlier detection.
  • Integrated SFTM components with an EMG PR module for real-time testing.

Main Results:

  • Significantly reduced LDA retraining time from ~1s to <4ms on an embedded system, enabling near "zero-delay" operation.
  • Demonstrated SFTM's ability to handle various disturbances and significantly improve classification performance.
  • Maintained system classification performance even in the absence of signal disturbances.

Conclusions:

  • Presented a real-time, lightweight, and automatic SFTM for reliable EMG PR.
  • Paved the way for robust EMG PR essential for advanced prosthesis control.