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Related Experiment Videos

Real-time intelligent pattern recognition algorithm for surface EMG signals.

Mahdi Khezri1, Mehran Jahed

  • 1Sharif University of Technology, Electrical Engineering Department, Biomedical Engineering Group, Tehran, Iran. mahdi_khezri_ee@yahoo.com

Biomedical Engineering Online
|December 7, 2007
PubMed
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This study introduces an intelligent system for recognizing hand movements using adaptive neuro-fuzzy inference system (ANFIS) and real-time learning. The system achieves 96.7% accuracy for prosthetic hand control, improving upon previous methods.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Machine Learning

Background:

  • Electromyography (EMG) studies muscle electrical signals for applications like prosthetic hand control.
  • Current prosthetic control systems have limitations in function, movement simplicity, or electrode usage.
  • This work proposes an intelligent system to recognize hand movements and includes user assessment for improved control.

Purpose of the Study:

  • To develop an intelligent system for recognizing hand motion commands using adaptive neuro-fuzzy inference system (ANFIS).
  • To enhance system capability through real-time learning and user vision feedback for movement correctness assessment.
  • To optimize the fuzzy system training and feature set dimensionality for effective EMG pattern recognition.

Main Methods:

Related Experiment Videos

  • An adaptive neuro-fuzzy inference system (ANFIS) with a real-time learning scheme was employed.
  • A hybrid training method combining back-propagation (BP) and least mean square (LMS) was used for the fuzzy system.
  • Subtractive clustering optimized fuzzy rules, while Principle Component Analysis (PCA) reduced feature set dimensions (time domain and time-frequency representation).

    • Main Results:

    • The system classified six unique hand movements using surface electromyogram (sEMG) signals.
    • The real-time ANFIS approach combined with user evaluation achieved an average accuracy of 96.7%.
    • This accuracy surpasses previously reported results using artificial neural networks (ANN) for real-time methods.

    Conclusions:

    • The ANFIS real-time learning method is effective for sEMG pattern recognition.
    • Integrating time and time-frequency EMG features enhances system performance.
    • This approach is suitable for developing advanced surface electromyogram pattern recognition systems for hand prosthesis control.