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

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....