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Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface

G C Chang1, W J Kang, J J Luh

  • 1Department of Electrical Engineering, National Taiwan University, Taipei, ROC.

Medical Engineering & Physics
|October 1, 1996
PubMed
Summary
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This study developed a real-time electromyogram (EMG) system for man-machine interfaces. The system accurately discriminates body motions using advanced signal processing, offering potential for assistive technologies.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Human-Computer Interaction

Background:

  • Man-machine interfaces (MMIs) require reliable control signals.
  • Electromyogram (EMG) signals offer a non-invasive method for detecting muscle activity.
  • Real-time processing of EMG is crucial for responsive MMI applications.

Purpose of the Study:

  • To develop a real-time electromyogram (EMG) discrimination system.
  • To provide control commands for man-machine interface (MMI) applications.
  • To achieve high accuracy and low response time in EMG-based motion discrimination.

Main Methods:

  • Utilized a host computer with a TMS320 C31 DSP for real-time EMG classification.
  • Collected two-channel EMG signals from neck and shoulder muscles using surface electrodes.

Related Experiment Videos

  • Employed zero-crossing rate for contraction onset detection and cepstral coefficients for feature extraction.
  • Implemented a modified maximum likelihood distance classifier for motion discrimination.

    • Main Results:

    • Achieved a mean recognition rate of 95% in discriminating five specific neck and shoulder motions.
    • The system demonstrated a total response time of approximately 0.17 seconds.
    • Successful discrimination was achieved in both able-bodied and C5/6 quadriplegic subjects.

    Conclusions:

    • The developed real-time EMG discrimination system shows significant potential for MMI applications.
    • The system's fast response time and high recognition reliability are key advantages.
    • This technology could enhance control capabilities for individuals with motor impairments.