A cepstrum analysis-based classification method for hand movement surface EMG signals.
It 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....
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 10; pp. 2179 - 2202 |
|---|---|
| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139126445&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139126445 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2019 vid: 57 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139126445 139126445 NLM31388900 10.1007/s11517-019-02024-8 NLM31388900 139126445 ppf: 2179 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A cepstrum analysis-based classification method for hand movement surface EMG signals. aug: au: Yavuz, Erdem Eyupoglu, Can affil: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, Yildirim, Bursa, Turkey sug: ab: It 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. Graphical abstract. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|