Prosthesis-guided training of pattern recognition-controlled myoelectric prosthesis.

Caitlin L. Chicoine*, Ann M. Simon, Levi J. Hargrove

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Pattern recognition can provide intuitive control of myoelectric prostheses. Currently, screen-guided training (SGT), in which individuals perform specific muscle contractions in sync with prompts displayed on a screen, is the common method of collecting the electromyography (EMG) data necessary to train a pattern recognition classifier. Prosthesis-guided training (PGT) is a new data collection method that requires no additional hardware and allows the individuals to keep their focus on the prosthesis itself. The movement of the prosthesis provides the cues of when to perform the muscle contractions. This study compared the training data obtained from SGT and PGT and evaluated user performance after training pattern recognition classifiers with each method. Although the inclusion of transient EMG signal in PGT data led to decreased accuracy of the classifier, subjects completed a performance task faster than when compared to using a classifier built from SGT data. This may indicate that training data collected using PGT that includes both steady state and transient EMG signals generates a classifier that more accurately reflects muscle activity during real-time use of a pattern recognition-controlled myoelectric prosthesis.

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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