Feature Eztraction of Surface EMG Signal Based on Wavelet Coefficient Entropy
This paper introduces a novel and simple method to extract the general feature of two surface EMG signal patterns: forearm supination (FS) surface EMG signal and forearm pronation (FP) surface EMG signal. The method decomposes surface EMG signal into 16 Frequency bands (FB) by wavelet packet transform (WPT), and then wavelet coefficient entropy (WCE) of two chosen FBs is calculated. The two WCEs were used to distinguish FS surface EMG signals from FP surface EMG signals. The result shows that WCE is an effective method for extracting the feature from surface EMG signal.
surface EMG signal wavelet transform entropy pattern recognition
Xiao Hu Qun Yu Waixi Liu Jian Qin
Information, Machinery and Electronics College Guangzhou University Guangzhou 510006, China
国际会议
上海
英文
1758-1760
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）