Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion.
In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).
Wang
Gang
G
Department of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai, 200240, People's Republic of China.
Wang
Zhizhong
Z
Chen
Weiting
W
Zhuang
Jun
J
D004576:Electromyography / Q000379:methods*; D005542:Forearm / Q000502:physiology; D006225:Hand / Q000502:physiology; D006801:Humans; D008433:Mathematics; D008954:Models, Biological; D009068:Movement / Q000502:physiology; D009119:Muscle Contraction / Q000502:physiology; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D019736:Prostheses and Implants; D012815:Signal Processing, Computer-Assisted