Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis

Levi J. Hargrove, Erik J. Scheme, Kevin B. Englehart, Bernard S. Hudgins

Research output: Contribution to journalArticlepeer-review

131 Scopus citations

Abstract

This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error metric and functionally with a clothespin test implemented in a virtual environment. For each case, the proposed system was compared to a state-of-the-art pattern recognition system based on linear discriminant analysis and a conventional myoelectric control scheme with mode switching. These assessments showed that the proposed control system had a higher classification error (p < 0.001) but yielded a more controllable myoelectric control system (p < 0.001) as measured through a clothespin usability test implemented in a virtual environment. Furthermore, the system was computationally simple and applicable for real-time embedded implementation. This work provides the basis for a clinically viable pattern recognition based myoelectric control system which is robust, easily configured, and highly usable.

Original languageEnglish (US)
Article number5378611
Pages (from-to)49-57
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume18
Issue number1
DOIs
StatePublished - Feb 2010

Keywords

  • Artificial arms
  • Electromyogram (EMG)
  • Myoelectric signal (MES)
  • Myoelectric signals
  • Pattern recognition
  • Powered prostheses

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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