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JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
Lower limb locomotion-mode identification based on multi-source information and particle swarm optimization algorithm
LIU Lei1, YANG Peng1,2 , LIU Zuo-jun1,2
1. School of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China; 2. Engineering Research Center of Intelligent Rehabilitation and Detecting Technology, Tianjin 300130, China
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Abstract  

An approach based on multi-source information and particle swarm optimization algorithm-back propagation(PSO-BP)neural network was proposed in order to improve the accuracy of human lower limb locomotion-mode identification. A multi-source information acquisition system was established, which was composed of lower limb surface electromyography signal (sEMG),hip joint angle and hip joint acceleration. Specifically, skewness, kurtosis and power spectrum ratio were extracted from surface electromyography (sEMG); the average ratio of hip angle of segmentation mode was extracted from gyroscope; standard deviation, peak of energy and correlation coefficient were extracted from accelerometer. Principal component analysis (PCA) was used to fuse these features. PSO-BP neural network was trained using an experimental database for locomotion-mode identification. The test results indicated that the locomotion-mode identification rate was 95.75%, and the average identification time was 1.2348 s.



Published: 28 August 2015
CLC:  TP 391  
Cite this article:

LIU Lei, YANG Peng,LIU Zuo-jun1. Lower limb locomotion-mode identification based on multi-source information and particle swarm optimization algorithm. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2015, 49(3): 439-447.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2015.03.007     OR     http://www.zjujournals.com/eng/Y2015/V49/I3/439


基于多源信息和粒子群优化算法的下肢运动模式识别

为了提高人体下肢多运动模式识别的准确性,提出一种基于多源信息和粒子群优化算法-误差反向传播(PSO-BP)神经网络的识别方法.建立下肢多源信息采集系统,该系统由下肢表面肌电信号、髋关节角度、髋关节加速度组成.选择肌电信号偏度、峭度和功率谱比值为肌电信号特征,髋关节角度细分模式均值比为腿部角度信号特征,加速度标准差、能量峰值、两轴相关性系数为髋关节加速度特征.按照主成分分析(PCA)方法融合上述特征值,利用PSO-BP进行识别.实验结果表明:该方法识别率为95.75%,平均识别时间为1.234 8 s.

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