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

• Published in: Medical & biological engineering & computing Vol. 57; no. 10; pp. 2179 - 2201
• Main Authors: Yavuz, Erdem, Eyupoglu, Can
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2019; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Cepstral analysis; Classification; Computer Applications; Electromyography; Exoskeleton; Exoskeletons; Feature extraction; Female; Hand; Hand - physiology; Human Physiology; Humans; Imaging; Iterative methods; Male; Movement - physiology; Neural networks; Neural Networks, Computer; Original Article; Principal Component Analysis; Prostheses; Radiology; Regression analysis; Seismology; Signal classification; Signal processing; Signal Processing, Computer-Assisted; Statistical analysis; Statistical tests; Training; Radial basis function; Surface electromyogram; Generalized regression neural network; Cepstrum analysis; Prosthetic hand; Cepstral coefficients
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-019-02024-8

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