摘要
针对表面肌电信号(sEMG)非线性非平稳等特点,提出一种基于小波包熵和支持向量机的表面肌电信号多运动模式识别方法。该方法利用小波包系数能量分布分析表面肌电信号特性,结合信息熵分析其不确定性和复杂性,先对表面肌电信号进行小波包分解,提取各节点的小波包系数矩阵,由各子带能量计算出小波包熵。以三路肌电信号的小波包熵为特征构建三维特征向量输入支持向量机分类器,对手部的多个动作进行分类。实验结果表明,基于小波包熵的特征向量结合支持向量机的方法能够以较高识别率区分伸腕、屈腕、展拳、握拳、腕外旋、腕内旋6种动作,能够得到比传统的神经网络分类器更为准确的分类结果。
Considering the nonstationary and nonlinear of surface electromyography(sEMG),a new method for sEMG signal recognition based on wavelet packet entropy and support vector machines was proposed.The uncertainty and complexity of sEMG were analyzed using wavelet packet entropy.According to the sEMG wavelet packet transform,extracted the wavelet packet coefficient matrix and calculate wavelet packet entropy.A 3-dimension eigenvector which was constructed with three-sEMG signal wavelet packet entropy was inputted Support vector machines(SVM) to classify the hand actions.Experimental results show that six movements(wrist spreads,wrist bends,hand extension,hand grasps,wrist external rotation,wrist internal rotation) are successfully identified by the method of SVM combined with the wavelet packet entropy.Compared with neural network sorter,the more precise classified results can be get.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第S2期150-154,共5页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(60903084
61172134
61201300)
浙江省自然科学基金资助项目(LY13F030017
Y1111189
LY12F03006)
关键词
表面肌电信号
小波包熵
支持向量机
模式识别
surface electromyography
wavelet packet entropy
support vector machines
pattern recognition
