Electromyographic classification of effort in muscle strength assessment

Electromyographic classification of effort in muscle strength assessment AbstractDual-channel evaluation of surface electromyogram (SEMG) signals acquired from amputee subjects using computational techniques for classification of arm motions is presented in this study. SEMG signals were classified by the neural network (NN) and interpretation was done using statistical techniques to extract the effectiveness of the recorded signals. From the results, it was observed that there exists a calculative difference in amplitude gain across different motions and that SEMG signals have great potential to classify arm motions. The outcomes indicated that the NN algorithm performs significantly better than other algorithms, with a classification rate (CR) of 96.40%. Analysis of variance (ANOVA) presents the results to validate the effectiveness of the recorded data to discriminate SEMG signals. The results are of significant thrust in identifying the operations that can be implemented for classifying upper-limb movements suitable for prostheses’ design. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering / Biomedizinische Technik de Gruyter

Electromyographic classification of effort in muscle strength assessment

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Publisher
de Gruyter
Copyright
©2018 Walter de Gruyter GmbH, Berlin/Boston
ISSN
1862-278X
eISSN
1862-278X
D.O.I.
10.1515/bmt-2016-0038
Publisher site
See Article on Publisher Site

Abstract

AbstractDual-channel evaluation of surface electromyogram (SEMG) signals acquired from amputee subjects using computational techniques for classification of arm motions is presented in this study. SEMG signals were classified by the neural network (NN) and interpretation was done using statistical techniques to extract the effectiveness of the recorded signals. From the results, it was observed that there exists a calculative difference in amplitude gain across different motions and that SEMG signals have great potential to classify arm motions. The outcomes indicated that the NN algorithm performs significantly better than other algorithms, with a classification rate (CR) of 96.40%. Analysis of variance (ANOVA) presents the results to validate the effectiveness of the recorded data to discriminate SEMG signals. The results are of significant thrust in identifying the operations that can be implemented for classifying upper-limb movements suitable for prostheses’ design.

Journal

Biomedical Engineering / Biomedizinische Technikde Gruyter

Published: Mar 28, 2018

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