Abstract

Info

Title:: Empirical mode decomposition and wavelet packet transform applied to surface EMG signal for hand gesture recognition

Author(s):: FENG Kai; DONG Xiucheng; LIU Dongbo; School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 611730, China

Keywords:: Keywords: surface electromyography signal empirical mode decomposition wavelet packet transformation feature extraction pattern recognition

Abstract:: Abstract: Compared with conventional surface electromyography (EMG) signal preprocessing and feature extraction methods, a model based on empirical mode decomposition and wavelet packet transform for hand gesture recognition through surface EMG signals is proposed for improving the accuracy of surface EMG signal in hand gesture recognition. Empirical mode decomposition is firstly used to smooth the surface EMG signal for obtaining a series of intrinsic mode functions. Subsequently, the correlation between each intrinsic mode function and the original signal is obtained, and the top 4 components with higher correlation are selected as effective components. Then, Db3 wavelet function is used to perform wavelet packet transformation, and the average energy, average absolute value, maximum value, root mean square and variance of the wavelet packet coefficients are extracted. Finally, linear discriminant analysis and support vector machine are used to recognize 12 hand gestures separately. The results show that the hand gesture recognition accuracy of applying empirical mode decomposition and wavelet packet transform to surface EMG signal is higher than that of directly extracting wavelet packet coefficients.