DocumentCode
776355
Title
A Comparison of Surface and Intramuscular Myoelectric Signal Classification
Author
Hargrove, Levi J. ; Englehart, K. ; Hudgins, B.
Author_Institution
Dept. of Electr. & Comput. Eng., New Brunswick Univ., Fredericton, NB
Volume
54
Issue
5
fYear
2007
fDate
5/1/2007 12:00:00 AM
Firstpage
847
Lastpage
853
Abstract
The surface myoelectric signal (MES) has been used as an input to controllers for powered prostheses for many years. As a result of recent technological advances it is reasonable to assume that there will soon be implantable myoelectric sensors which will enable the internal MES to be used as input to these controllers. An internal MES measurement should have less muscular crosstalk allowing for more independent control sites. However, it remains unclear if this benefit outweighs the loss of the more global information contained in the surface MES. This paper compares the classification accuracy of six pattern recognition-based myoelectric controllers which use multi-channel surface MES as inputs to the same controllers which use multi-channel intramuscular MES as inputs. An experiment was designed during which surface and intramuscular MES were collected simultaneously for 10 different classes of isometric contraction. There was no significant difference in classification accuracy as a result of using the intramuscular MES measurement technique when compared to the surface MES measurement technique. Impressive classification accuracy (97%) could be achieved by optimally selecting only three channels of surface MES
Keywords
electromyography; medical control systems; medical signal processing; pattern recognition; prosthetics; signal classification; controllers; implantable myoelectric sensors; intramuscular myoelectric signal classification; isometric contraction; pattern recognition; powered prostheses; surface myoelectric signal classification; Biomedical signal processing; Control systems; Data mining; Elbow; Feature extraction; Measurement techniques; Pattern classification; Pattern recognition; Prosthetics; Technological innovation; Classification; EMG; intramuscular; myoelectric; pattern recognition; prostheses; Adult; Electric Impedance; Electrodes; Electromyography; Forearm; Hand; Humans; Isometric Contraction; Male; Models, Biological; Muscle, Skeletal; Pattern Recognition, Physiological; Signal Processing, Computer-Assisted; Software; Wrist;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/TBME.2006.889192
Filename
4154997
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