Optimizing Support Vector Machine with Genetic Algorithm for Capacitive   Sensing-Based Locomotion Mode Recognition

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	Optimizing Support Vector Machine with Genetic Algorithm for Capacitive Sensing-Based Locomotion Mode Recognition
	Song, Yi ; Zhu, Yating ; Zheng, Enhao ; Tao, Fei ; Wang, Qining
	2016
关键词	Capacitive sensing Support vector machine Genetic algorithm Locomotion mode recognition Lower-limb prostheses TRANSTIBIAL PROSTHESIS ANKLE DESIGN
DOI	10.1007/978-3-319-08338-4_75
英文摘要	Capacitive sensing has been proven valid for locomotion mode recognition as an alternative of popular electromyography-based methods in the control of powered prostheses. In order to obtain higher recognition accuracy, in this paper, we try to improve the support vector machine (SVM)-based classifier by selecting suitable kernel function and optimizing the parameters with genetic algorithm (GA). According to different phases of the gait, the phase-dependant GA-SVM models are built and the recognition accuracy increase from 94.0 to 99.1%, which is satisfactory for practical applications.; EI; CPCI-S(ISTP); qiningwang@pku.edu.cn; 1035-1047; 302
会议录	13th International Conference on Intelligent Autonomous Systems (IAS)
语种	英语
内容类型	会议论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/423393]
专题	工学院
推荐引用方式 GB/T 7714	Song, Yi,Zhu, Yating,Zheng, Enhao,et al. Optimizing Support Vector Machine with Genetic Algorithm for Capacitive Sensing-Based Locomotion Mode Recognition[C]. 见:.