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Continuous myoelectric control for powered prostheses using hidden Markov models.

Adrian D C Chan1, Kevin B Englehart

  • 1Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada. adcehan@sce.carleton.ca

IEEE Transactions on Bio-Medical Engineering
|January 18, 2005
PubMed
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This study introduces a novel hidden Markov model (HMM) for myoelectric control of upper extremity prostheses. This advanced technique enhances limb movement classification accuracy for more natural prosthesis control.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Robotics
  • Signal Processing

Background:

  • Myoelectric control of upper extremity prostheses aims for dexterous and natural limb movement.
  • Previous methods often require signal segmentation and struggle with continuous control.
  • Individualized muscle activation patterns are key for intuitive prosthesis operation.

Purpose of the Study:

  • To investigate a hidden Markov model (HMM) for processing myoelectric signals for prosthesis control.
  • To achieve higher classification accuracy compared to existing methods.
  • To enable continuous, natural control of upper extremity prostheses.

Main Methods:

  • Utilized a hidden Markov model (HMM) to process four channels of myoelectric signal data.

Related Experiment Videos

  • Trained the HMM to discriminate between six classes of upper extremity limb movements.
  • Developed a system that does not require myoelectric signal segmentation for continuous decision-making.

    • Main Results:

    • The HMM approach demonstrated superior classification accuracy over multilayer perceptron methods.
    • The system provides a continuous stream of movement class decisions, enabling uninterrupted control.
    • Learned individual muscle activation patterns for personalized and natural prosthesis actuation.

    Conclusions:

    • Hidden Markov models offer a robust and accurate method for myoelectric control of upper extremity prostheses.
    • The continuous decision stream and adaptive training capabilities facilitate real-time, natural prosthesis function.
    • This approach advances the potential for complex, multi-joint manipulation in prosthetic devices.