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)
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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:. |
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