Real-time and offline performance of pattern recognition myoelectric control using a generic electrode grid with targeted muscle reinnervation patients

Dennis C. Tkach, Aaron J. Young, Lauren H. Smith, Elliott J. Rouse, Levi J. Hargrove

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Targeted muscle reinnervation (TMR) is a surgical technique that creates myoelectric prosthesis control sites for high-level amputees. The electromyographic (EMG) signal patterns provided by the reinnervated muscles are well-suited for pattern recognition control. Pattern recognition allows for control of a greater number of degrees of freedom (DOF) than the conventional, EMG amplitude-based approach. Previous pattern recognition studies have shown benefit in placing electrodes directly over the reinnervated muscles. Localizing the optimal TMR locations is inconvenient and time consuming. In this contribution, we demonstrate that a clinically practical grid arrangement of electrodes yields real-time control performance that is equivalent to, or better than, the site-specific electrode placement for simultaneous control of multiple DOFs using pattern recognition. Additional findings indicate that grid-like electrode arrangement yields significantly lower classification errors for classifiers with a large number of movement classes (>9). These findings suggest that a grid electrode arrangement can be effectively used to control a multi-DOF upper limb prosthesis while reducing the time and effort associated with fitting the prosthesis due to clinical localization of control sites on amputee patients.

Original languageEnglish (US)
Article number6737321
Pages (from-to)727-734
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume22
Issue number4
DOIs
StatePublished - Jul 2014

Keywords

  • Electrode grid
  • electromyography (EMG) prosthesis control
  • pattern recognition
  • targeted muscle reinnervation
  • upper limb amputee

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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