Classification of surface EMG signal using relative wavelet packet energy.
Features can be classified into interferential features and discriminable features according to their contribution to pattern recognition. In this paper, a novel and simple method based on wavelet packet transform is proposed to extract the features from surface EMG signal. In this method, the features are relative wavelet packet energy (RWPE), which is evaluated from several selected frequency bands of surface EMG signal. Compared with a conventional method, which is of the best performance in previous applications, the method can compress the interferential features and enhance the discriminable features more effectively. In consequence, the RWPE features calculated by the method represent different patterns of surface EMG signal more accurately and the accuracy of surface EMG signal pattern classification is improved greatly.
Hu
Xiao
X
Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China. huxiaocz@163.com
Wang
Zhizhong
Z
Ren
Xiaomei
X
D004576:Electromyography; D012815:Signal Processing, Computer-Assisted