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Related Experiment Videos

Classification of motor commands using a modified self-organising feature map.

F Sebelius1, L Eriksson, H Holmberg

  • 1Department of Electrical Measurements, Lund Institute of Technology, Lund, Sweden.

Medical Engineering & Physics
|May 3, 2005
PubMed
Summary

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A new control system for advanced prosthetics was tested in rats and humans. A modified Self-Organising Feature Map (SOFM) network demonstrated superior performance for classifying prosthetic movements.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Artificial Intelligence

Background:

  • Advanced prosthetics require sophisticated control systems.
  • Existing control systems face challenges in real-time adaptation and accuracy.
  • Biomimetic approaches offer potential for improved prosthetic functionality.

Purpose of the Study:

  • To propose and investigate a novel control system for advanced prostheses.
  • To evaluate the system's performance in both animal models and human subjects.
  • To compare the efficacy of different artificial neural network (ANN) algorithms for prosthetic control.

Main Methods:

  • Utilized a rat spinal withdrawal reflex system as a model for prosthetic control.
  • Validated the control system in human subjects, including one with traumatic hand amputation.

Related Experiment Videos

  • Employed various ANN algorithms, including a modified Self-Organising Feature Map (SOFM) combining Kohonen network and conscience mechanism (KNC), for classifying electromyography (EMG) signals.
  • Compared KNC performance against reference networks like multi-layer perceptrons.

    • Main Results:

    • The modified SOFM (KNC) algorithm outperformed reference networks in training time, memory consumption, and ease of parameter optimization.
    • KNC achieved high classification accuracy for five movements in the animal model.
    • KNC demonstrated high accuracy in classifying seven distinct movements in human subjects.

    Conclusions:

    • The proposed KNC-based control system offers a superior, efficient, and accurate method for advanced prosthetic control.
    • This approach shows significant promise for enhancing prosthetic functionality in both simulated and real-world applications.
    • The study validates the effectiveness of advanced ANNs in decoding complex biological movement signals for prosthetic interfaces.