Motion Classification of EMG Signals Based on Wavelet Packet Transform and LS-SVMs Ensemble  

作　　者：颜志国 尤晓明 陈嘉敏 叶小华 

机构地区：[1]Department of Criminal Detecting, The Third Research Institute of Ministry of Public Security [2]Department of Mechanical Engineering, Anhui University of Technology

出　　处：《Transactions of Tianjin University》2009年第4期300-307,共8页天津大学学报（英文版）

基　　金：Supported by the National Basic Research Program("973"Program, No2005CB724303 )

摘　　要：This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.This paper presents an effective method for motion classification using the surface electromyographic （sEMG） signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform （WPT） is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine （LS-SVM） is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.

关 键 词：pattern recognition wavelet packet transform least squares support vector machine surface electromyographic signal neural network SEPARABILITY 

分 类 号：Q445[生物学—生理学]