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Computers 2015, 4(3), 251-264; doi:10.3390/computers4030251

Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction

1
Department of Computing Science and Engineering, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait
2
School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Bradley Alexander
Received: 15 May 2015 / Revised: 6 August 2015 / Accepted: 1 September 2015 / Published: 10 September 2015
View Full-Text   |   Download PDF [284 KB, uploaded 10 September 2015]   |  

Abstract

Surface electromyographic (sEMG) activity of the biceps muscle was recorded from 13 subjects. Data was recorded while subjects performed dynamic contraction until fatigue and the signals were segmented into two parts (Non-Fatigue and Fatigue). An evolutionary algorithm was used to determine the elbow angles that best separate (using Davies-Bouldin Index, DBI) both Non-Fatigue and Fatigue segments of the sEMG signal. Establishing the optimal elbow angle for feature extraction used in the evolutionary process was based on 70% of the conducted sEMG trials. After completing 26 independent evolution runs, the best run containing the optimal elbow angles for separation (Non-Fatigue and Fatigue) was selected and then tested on the remaining 30% of the data to measure the classification performance. Testing the performance of the optimal angle was undertaken on nine features extracted from each of the two classes (Non-Fatigue and Fatigue) to quantify the performance. Results showed that the optimal elbow angles can be used for fatigue classification, showing 87.90% highest correct classification for one of the features and on average of all eight features (including worst performing features) giving 78.45%. View Full-Text
Keywords: genetic algorithms; localised muscle fatigue; electromyography; wavelet analysis; pseudo-wavelets; elbow angle genetic algorithms; localised muscle fatigue; electromyography; wavelet analysis; pseudo-wavelets; elbow angle
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Al-Mulla, M.R.; Sepulveda, F.; Al-Bader, B. Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction. Computers 2015, 4, 251-264.

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