基于粒子群优化-电子与信息学报.pdf

第 37 卷第 5 期 电 子 与 信 息 学 报 Vol.37 No.5 2015 年 5 月 Journal of Electronics Information Technology May 2015 基于粒子群优化-支持向量机方法的下肢肌电信号步态识别 高发荣* 王佳佳 席旭刚 佘青山 罗志增 (杭州电子科技大学智能控制与机器人研究所 杭州 310018) 摘 要：为提高下肢表面肌电信号步态识别的准确性和实时性，该文提出一种基于粒子群优化(PSO)算法优化支持 向量机(SVM)的模式识别方法。首先对消噪后的肌电信号提取积分肌电值和方差作为特征样本，然后利用PSO 算 法优化 SVM 的惩罚参数和核函数参数，最后利用步态动作的肌电信号样本数据对构造的 SVM 分类器进行训练、 测试。实验结果表明 PSO-SVM 分类器对下肢正常行走 5 个步态的识别率，明显高于未经参数优化的 SVM 分类器， 优化后平均识别率达到 97.8%，并兼顾了分类的准确性和自适应性。 关键词：模式识别；步态分析；肌电信号；粒子群优化；支持向量机 中图分类号：TP391.4; TP24 文献标识码： A 文章编号：1009-5896(2015)05-1154-06 DOI: 10.11999/JEIT141083 Gait Recognition for Lower Extremity Electromyographic Signals Based on PSO-SVM Method Gao Fa-rong Wang Jia-jia Xi Xu-gang She Qing-shan Luo Zhi-zeng (Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou 310018, China) Abstract: To improve the lower limb surface ElectroMyoGraphic (EMG) gait recognition accuracy and real time performance, this paper deals with a pattern recognition method for optimizing the Support Vector Machine (SVM) by using the Particle Swarm Optimization (PSO) algorithm. Firstly, the values of Integrated EMG and variance are extracted as the feature samples from the de-noised EMG signals. Then, the SVM parameters of the punishment and the kernel function are optimized by PSO. Finally, the constructed SVM classifiers are trained and tested by using the EMG sample data of the gait movements. The experimental results show that for five normal walking gaits of the lower extremity, the recognition rate of the PSO-SVM classifier is significantly higher than that of the non-parameter-optimized