DocumentCode
662934
Title
Crosspoint switching of EMG signals to increase number of channels for pattern recognition myoelectric control
Author
Gajendran, Rudhram ; Tkach, Dennis C. ; Hargrove, Levi J.
Author_Institution
Univ. of Illinois at Chicago, Chicago, IL, USA
fYear
2013
fDate
6-8 Nov. 2013
Firstpage
259
Lastpage
262
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.
Keywords
biomedical electrodes; electromyography; gait analysis; handicapped aids; medical signal processing; pattern recognition; prosthetics; signal classification; EMG acquisition system design; EMG channels; EMG data; EMG signals; PR; able-bodied subjects; appropriate muscle sites; classification accuracy; crosspoint switching; direct control; electrode number; hand movements; multifunction prosthesis; pattern recognition myoelectric control; pattern recognition system; prosthesis cost; prosthetic device complexity; upper limb amputees; Electrodes; Electromyography; Muscles; Pattern recognition; Prosthetics; Switches;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on
Conference_Location
San Diego, CA
ISSN
1948-3546
Type
conf
DOI
10.1109/NER.2013.6695921
Filename
6695921
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