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 SVM classifier, and the average recognition rate is up to 97.8%, as well as the classification accuracy and self-adaptability are also improved.
高发荣, 王佳佳, 席旭刚, 佘青山, 罗志增. 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[J]. 电子与信息学报, 2015, 37(5): 1154-1159.
Gao Fa-Rong, Wang Jia-Jia, Xi Xu-Gang, She Qing-Shan, Luo Zhi-Zeng. Gait Recognition for Lower Extremity Electromyographic Signals Based on PSO-SVM Method. , 2015, 37(5): 1154-1159.
2015, Vol. 37 
