基于表面肌电信相号的手腕动作模式识别.docVIP

基于表面肌电信号的手腕动作模式识别 张启忠* 席旭刚 马玉良 罗志增 佘青山 （杭州电子科技大学智能控制与机器人研究所， 杭州 310018） 摘 要：基于表面肌电信号的肢体动作模式识别是假手仿生控制的基础。为提高动作模式识别率，论文从肌电信号的产生机理出发，选取分别表征肌电信号形态特征-细节复杂度和整体自似性的近似熵指标和分维数指标，作为模式识别的特征向量；同时提出了一种具有增量学习能力的K最近邻(KNN)模型增量学习算法作为模式识别的分类器。在对10位受试者手腕的四个精细动作腕伸、腕屈、腕内旋、腕外旋的识别实验中取得了92.5 %以上的正确识别率。论文同时对增量学习能力对分类器动作模式识别率的影响做了对比实验，当假肢使用者生理变化时，以?KNN模型增量学习算法作为分类器比采用不具增量学习能力的KNN模型算法的识别率高4.5%。上述实验表明，论文所提肌电信号动作模式识别方法方案合理，具有应用价值。 关键词：肌电信号；肌电假肢；KNN模型增量学习算法；近似熵；分维数 中图分类号：TP 241.3 文献标识码：A Study on wrist’s multi-movement pattern recognition based on sEMGdoi:10.3969/j.issn.0258-8021.201 doi:10.3969/j.issn.0258-8021.2013.03.000 收稿日期：2012-00-00，录用日期：2013-00-00 ?基金项目：国家自然科学基金；浙江省自然科学基金（Y1111189， LY12F03007）; 浙江省科技计划项目(2012C33075) *通信作者。 E-mail:zqz@hdu.edu.cn ZHANG Qi-Zhong XI Xu-Gang MA Yu-Liang LUO Zhi-Zeng SHE Qing-Shan (Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou 310018,China Abstract: Action?pattern?recognition of limbs using sEMG is the basis?for?bionic control of a prosthetic hand.?In consideration of the generation?mechanism of sEMG, the approximate?entropy and the fractal dimension, which?feature the sEMG’s morphological characteristics including complexity and overall self-similarity,???was chosen as the?feature vector?of pattern recognition to improve action mode recognition rate.??In the meantime, a?K?Nearest?Neighbor?(KNN) model incremental learning method?with?incremental learning ability, was?presented?as?a?classifier of pattern recognition. In pattern recognition experiment of classifying four fine?movements?of the wrist (namely wrist extension, wrist flexion, wrist pronation, wrist supination) with 10 participants, the correct mode recognition rate is above 92.5%.??In?a?contrast experiment that?was designed to evaluate the?effects of?the increment learning ability to the?action?mode?recognition rate, the correct recognition rate is 4.5 percent higher than KNN mode arithmetic without incremental learning ability?when the prosthet