Adaptive pattern recognition of myoelectric signals: Exploration of conceptual framework and practical algorithms

Jonathon W. Sensinger, Blair A. Lock, Todd A. Kuiken

Research output: Contribution to journalArticlepeer-review

145 Scopus citations

Abstract

Pattern recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without knowledge of the intended class, thus achieving adaptation that is not cumbersome to the user, but at the cost of reduced accuracy. This study reports a novel adaptive experiment on eight subjects that allowed repeated measures post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 26% compared to the nonadapting classifier. Most unsupervised adaptation paradigms provided smaller reductions in error, due to frequent uncertainty of the correct class. One method that selected high-confidence samples showed the most practical implementation, although the other methods warrant future investigation. Supervised adaptation should be considered for incorporation into any clinically viable pattern recognition controller, and unsupervised adaptation should receive renewed interest in order to provide transparent adaptation.

Original languageEnglish (US)
Pages (from-to)270-278
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume17
Issue number3
DOIs
StatePublished - Jun 2009

Keywords

  • Adaptation
  • Myoelectric
  • Pattern recognition
  • Prosthesis
  • Targeted reinnervation

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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