基于小波包变换的表面肌电信号模式识别

采用小波包变换的方法对表面肌电信号sEMG进行了多尺度分解，并提取小波包分解系数的能量值构建特征矢量，采用四种方法设计多类最小二乘支持向量机（LS-SVM）分类器，对8种表面肌电信号进行了模式分类。实验结果表明，采用四种多类分类方法的LS-SVM分类器对8种表面肌电信号的平均识别率在90%以上，LS-SVM分类准确率明显优于传统的RBF神经网络分类器。

Surface EMG Signal Mode Recognition Based on Wavelet Package Transform

The surface electromyographic signal is analyzed by wavelet package transform.The feature vectors are built by extracting the energy value of the wavelet package coefficients.The multi-class least squares support vector machine classifier is designed by using four kinds of multi-class classification approach.The LS-SVM classifier is applied to the classification of eight movements with recording of the surface EMG.Experimental results show that the average recognition rate is over 90%,and the classification...