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Related Experiment Videos

Fuzzy EMG classification for prosthesis control.

F H Chan1, Y S Yang, F K Lam

  • 1Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong.

IEEE Transactions on Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
|September 23, 2000
PubMed
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This study introduces a fuzzy approach for classifying electromyograph (EMG) signals, enhancing prosthetic control. This method offers improved reliability and recognition rates compared to artificial neural networks (ANNs).

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Myoelectric control systems for prostheses rely on accurate electromyograph (EMG) signal classification.
  • Existing methods, such as artificial neural networks (ANNs), face challenges with overtraining and output consistency.
  • Developing robust and reliable EMG classification is crucial for advancing multifunctional prosthesis control.

Purpose of the Study:

  • To propose and evaluate a novel fuzzy approach for classifying single-site EMG signals.
  • To enhance the performance and reliability of myoelectric control systems.
  • To compare the proposed fuzzy method against traditional artificial neural network approaches.

Main Methods:

  • Time-segmented EMG signal features were extracted.

Related Experiment Videos

  • Unsupervised clustering using the Basic Isodata algorithm was employed for initializing fuzzy system parameters.
  • Fuzzy rules were trained using the back-propagation algorithm.
  • The fuzzy approach was compared with an artificial neural network (ANN) on four subjects.

    • Main Results:

    • The fuzzy approach achieved classification results comparable to the ANN method.
    • The fuzzy approach demonstrated a slightly higher recognition rate.
    • The fuzzy system exhibited insensitivity to overtraining, unlike ANNs.
    • Consistent outputs from the fuzzy approach indicated higher reliability.

    Conclusions:

    • The proposed fuzzy approach offers a reliable and effective method for EMG signal classification in prosthetic control.
    • Fuzzy systems present potential advantages over ANNs, including better generalization and stability.
    • This work contributes to the development of more sophisticated and dependable myoelectric prostheses.