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2016 | OriginalPaper | Chapter

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

Authors : Yi Song, Yating Zhu, Enhao Zheng, Fei Tao, Qining Wang

Published in: Intelligent Autonomous Systems 13

Publisher: Springer International Publishing

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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.

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Metadata
Title
Optimizing Support Vector Machine with Genetic Algorithm for Capacitive Sensing-Based Locomotion Mode Recognition
Authors
Yi Song
Yating Zhu
Enhao Zheng
Fei Tao
Qining Wang
Copyright Year
2016
Book
Intelligent Autonomous Systems 13

Print ISBN: 978-3-319-08337-7

Electronic ISBN: 978-3-319-08338-4

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-08338-4_75

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