@inproceedings{3a2eca47eb60452cb2458411c839af74,
title = "Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition",
abstract = "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.",
keywords = "Capacitive sensing, Genetic algorithm, Locomotion mode recognition, Lower-limb prostheses, Support vector machine",
author = "Yi Song and Yating Zhu and Enhao Zheng and Fei Tao and Qining Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 13th International Conference on Intelligent Autonomous Systems, IAS 2014 ; Conference date: 15-07-2014 Through 18-07-2014",
year = "2016",
doi = "10.1007/978-3-319-08338-4\_75",
language = "英语",
isbn = "9783319083377",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "1035--1047",
editor = "Hiroaki Yamaguchi and Nathan Michael and Karsten Berns and Emanuele Menegatti",
booktitle = "Intelligent Autonomous Systems - Proceedings of the 13th International Conference IAS, 2014",
}