Crosspoint switching of EMG signals to increase number of channels for pattern recognition myoelectric control

Rudhram Gajendran, Dennis C. Tkach, Levi J. Hargrove

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Myoelectric pattern recognition (PR) can provide a more intuitive control for upper limb amputees in using multifunction prosthesis than direct control. Accuracy of a pattern recognition system has been shown to improve with increasing number of EMG channels. However, increasing the number of channels comes with a drawback of increased weight, cost and complexity of the prosthesis. This paper presents the concept and design of a novel EMG acquisition system to acquire higher number of channels without increasing the number of electrodes placed or the complexity of the prosthetic device. A prototype of the device was developed and tested on able-bodied subjects to evaluate its performance in pattern recognition. Subjects were requested to perform 9 different hand movements while EMG data was collected into training and test groups. Test results indicate a 15% improvement in classification accuracy with the new system when compared to conventional systems. A system like this is valuable for patients with higher level amputations where placing higher number of electrodes is not feasible due to limited availability of appropriate muscle sites.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages259-262
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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