Abstract：
: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.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Yavuz E,Eyupoglu Cdoi
10.1007/s11517-019-02024-8subject
Has Abstractpub_date
2019-10-01 00:00:00pages
2179-2201issue
10eissn
0140-0118issn
1741-0444pii
10.1007/s11517-019-02024-8journal_volume
57pub_type
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