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Related Experiment Videos

Continuous classifier training for myoelectrically controlled prostheses.

A W Plumb1, A D C Chan, A R Goge

  • 1Dept. of Syst. & Comput. Eng., Carleton Univ., Ont., Canada.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
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Continuous training of myoelectric prostheses classifiers improves pattern recognition accuracy. This adaptive approach enhances prosthetic control by reducing errors caused by changing conditions, leading to better performance.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Engineering
  • Machine Learning in Prosthetics

Background:

  • Myoelectric prostheses rely on pattern recognition systems for motion classification.
  • Offline training of these systems leads to increased error rates when environmental conditions change.
  • Existing systems lack adaptability to real-world, dynamic signal variations.

Purpose of the Study:

  • To develop and evaluate a continuously trained classifier for myoelectrically controlled prostheses.
  • To improve the adaptability and reduce error rates of prosthetic control systems.
  • To enhance the performance of pattern recognition systems in dynamic conditions.

Main Methods:

  • Implemented a continuous retraining strategy for the pattern recognition classifier.

Related Experiment Videos

  • Utilized valid class decisions from a retraining buffer to update the classifier.
  • Incorporated new feature vectors while discarding older ones to maintain a current training set.

    • Main Results:

    • The continuously trained classifier demonstrated an average improvement of 2.57% compared to non-continuously trained systems.
    • The adaptive retraining method effectively reduced classification errors in changing conditions.
    • Validated decisions using consecutive, identical majority votes for retraining.

    Conclusions:

    • Continuous training significantly enhances the performance of myoelectric prostheses.
    • The developed system offers improved adaptability and robustness against signal variations.
    • This approach represents a step forward in creating more intuitive and reliable prosthetic control.