小波变换在表面肌电信号分类中的应用

Surface EMG Signal Classification Using Wavelet Transform

针对肌电信号的非平稳特性 ,采用小波变换方法对表面肌电信号进行分析。通过奇异值分解有效地提取信号特征进行模式识别 ,能够成功地从掌长肌和肱桡肌采集的两道表面肌电信号中识别展拳、握拳、前臂内旋、前臂外旋四种运动模式。实验表明 ,基于小波变换的奇异值分解方法是一种稳定、有效的特征提取方法 ,为非平稳生理信号的分析提供了新的手段。

A Surface EMG signal classification method based on wavelet transform is presented in this paper. To utilize the nonstationary character of the EMG signals, dyadic wavelet transform is employed to obtain the signals' time frequency representation. Singular value decomposition(SVD) is then used to extract feature vector for pattern classification. This motion classifier can successfully identify four types of forearm movement:hand grasp, hand extension, forearm pronation and forearm supination. Experimental result shows that this method has a great potential in the practical application of prothesis control.