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

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。

Gait Recognition for Lower Extremity Electromyographic Signals Based on PSO-SVM Method

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.