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Conference Paper: Characterization of surface EMG with cumulative residual entropy

TitleCharacterization of surface EMG with cumulative residual entropy
Authors
KeywordsClassification
Cumulative residual entropy
Surface electromyography
Approximate entropy
Classification accuracy
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800540
Citation
The 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2012), Hong Kong, 12-15 August 2012. In IEEE ICSPCC Proceedings, 2012, p. 55-58 How to Cite?
AbstractThe cumulative residual entropy (CREn) is an alternative measure of uncertainty in a random variable. In this paper, we applied CREn as a feature extraction method to characterize six hand and wrist motions from four-channel surface electromyography (SEMG) signals. For comparison, fuzzy entropy, sample entropy and approximate entropy were also used to characterize the SEMG signals. The support vector machine (SVM) and linear discriminant analysis (LDA) were used to discriminate six hand and wrist motions in order to evaluate the performance of different entropies. The experimental results indicate that the CREn-based classification outperforms other entropy based methods with the best classification accuracy of is 97.17±1.97% by SVM and 93.56±4.13 by LDA. Furthermore, the computational complexity of CREn is lower than those of other entropies. It suggests that CREn has the potential to be applied as an effective feature extraction method in the control of SEMG-based multifunctional prosthesis. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/181795
ISBN

 

DC FieldValueLanguage
dc.contributor.authorCai, Yen_US
dc.contributor.authorShi, Jen_US
dc.contributor.authorZhong, Jen_US
dc.contributor.authorWang, Fen_US
dc.contributor.authorHu, Yen_US
dc.date.accessioned2013-03-19T03:58:15Z-
dc.date.available2013-03-19T03:58:15Z-
dc.date.issued2012en_US
dc.identifier.citationThe 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2012), Hong Kong, 12-15 August 2012. In IEEE ICSPCC Proceedings, 2012, p. 55-58en_US
dc.identifier.isbn978-1-4673-2193-8-
dc.identifier.urihttp://hdl.handle.net/10722/181795-
dc.description.abstractThe cumulative residual entropy (CREn) is an alternative measure of uncertainty in a random variable. In this paper, we applied CREn as a feature extraction method to characterize six hand and wrist motions from four-channel surface electromyography (SEMG) signals. For comparison, fuzzy entropy, sample entropy and approximate entropy were also used to characterize the SEMG signals. The support vector machine (SVM) and linear discriminant analysis (LDA) were used to discriminate six hand and wrist motions in order to evaluate the performance of different entropies. The experimental results indicate that the CREn-based classification outperforms other entropy based methods with the best classification accuracy of is 97.17±1.97% by SVM and 93.56±4.13 by LDA. Furthermore, the computational complexity of CREn is lower than those of other entropies. It suggests that CREn has the potential to be applied as an effective feature extraction method in the control of SEMG-based multifunctional prosthesis. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800540-
dc.relation.ispartofIEEE International Conference on Signal Processing, Communications and Computing Proceedingsen_US
dc.rightsIEEE International Conference on Signal Processing, Communications and Computing Proceedings. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectClassification-
dc.subjectCumulative residual entropy-
dc.subjectSurface electromyography-
dc.subjectApproximate entropy-
dc.subjectClassification accuracy-
dc.titleCharacterization of surface EMG with cumulative residual entropyen_US
dc.typeConference_Paperen_US
dc.identifier.emailHu, Y: yhud@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.description.naturepublished_or_final_version-
dc.identifier.scopuseid_2-s2.0-84869439470-
dc.identifier.hkuros213620en_US
dc.identifier.spage55-
dc.identifier.epage58-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 130418-

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