传感技术学报Issue(11)：1586-1590,5.DOI:10.3969/j.issn.1004-1699.2015.11.002
基于熵和PSO优化SVM的肌电信号跌倒识别
Fall Recognition Based on EMG Signal Entropy and PSO-SVM
摘要
Abstract
A new fall detection method was designed for fall alarm based on sEMG. Firstly,the sEMG signals are de⁃composed into subspaces with wavelet packet. Then,depending on the signal characteristics,signals of low-frequen⁃cy component were recombined to calculate the permutation entropy. Finally,the SVM method was used to recog⁃nize eight actions according to the permutation entropy of four sEMG signals,and the particle swarm optimization was used to optimize punishment parameter c and nuclear parameter g . The result shows fall sensitivity,fall spec⁃ificity,the average recognition rate were 88%,98.3%,97.0%,better than the gird method and genetic algorithm pa⁃rameters optimization. The method has strong robustness and noise immunity.关键词
表面肌电信号/小波包分解/排列组合熵/支持向量机/粒子群算法Key words
surface electromyography/wavelet packet decomposition/permutation entropy/support vector machine/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
武昊,席旭刚,罗志增..基于熵和PSO优化SVM的肌电信号跌倒识别[J].传感技术学报,2015,(11):1586-1590,5.基金项目
国家自然科学基金项目（60903084，61172134）；浙江省自然科学基金项目（LY13F030017）；浙江省科技计划项目 （）
