A cepstrum analysis-based classification method for hand movement surface EMG signals

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dc.authorid0000-0002-3159-2497en_US
dc.contributor.authorYavuz, Erdem
dc.contributor.authorEyupoglu, Can
dc.date.accessioned2021-03-20T20:12:28Z
dc.date.available2021-03-20T20:12:28Z
dc.date.issued2019
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractIt is of great importance to effectively process and interpret surface electromyogram (sEMG) signals to actuate a robotic and prosthetic exoskeleton hand needed by hand amputees. In this paper, we have proposed a cepstrum analysis-based method for classification of basic hand movement sEMG signals. Cepstral analysis technique primarily used for analyzing acoustic and seismological signals is effectively exploited to extract features of time-domain sEMG signals by computing mel-frequency cepstral coefficients (MFCCs). The extracted feature vector consisting of MFCCs is then forwarded to feed a generalized regression neural network (GRNN) so as to classify basic hand movements. The proposed method has been tested on sEMG for Basic Hand movements Data Set and achieved an average accuracy rate of 99.34% for the five individual subjects and an overall mean accuracy rate of 99.23% for the collective (mixed) dataset. The experimental results demonstrate that the proposed method surpasses most of the previous studies in point of classification accuracy. Discrimination ability of the cepstral features exploited in this study is quantified using Kruskal-Wallis statistical test. Evidenced by the experimental results, this study explores and establishes applicability and efficacy of cepstrum-based features in classifying sEMG signals of hand movements. Owing to the non-iterative training nature of the artificial neural network type adopted in the study, the proposed method does not demand much time to build up the model in the training phase.en_US
dc.identifier.doi10.1007/s11517-019-02024-8en_US
dc.identifier.endpage2201en_US
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue10en_US
dc.identifier.pmid31388900en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2179en_US
dc.identifier.urihttp://doi.org/10.1007/s11517-019-02024-8
dc.identifier.urihttps://hdl.handle.net/20.500.12885/572
dc.identifier.volume57en_US
dc.identifier.wosWOS:000498052200007en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorYavuz, Erdem
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMedical & Biological Engineering & Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSurface electromyogramen_US
dc.subjectCepstrum analysisen_US
dc.subjectCepstral coefficientsen_US
dc.subjectProsthetic handen_US
dc.subjectRadial basis functionen_US
dc.subjectGeneralized regression neural networken_US
dc.titleA cepstrum analysis-based classification method for hand movement surface EMG signalsen_US
dc.typeArticleen_US

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