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2017 | OriginalPaper | Buchkapitel

Gait Recognition Using GA-SVM Method Based on Electromyography Signal

verfasst von : Ying Li, Farong Gao, Xiao Zheng, Haitao Gan

Erschienen in: Intelligent Robotics and Applications

Verlag: Springer International Publishing

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Abstract

To improve the recognition accuracy of the lower limb gait, a classification method based on genetic algorithm (GA) optimizing the support vector machine (SVM) was proposed. Firstly, electromyography (EMG) signals were collected from four thigh muscles related to lower limb movements. Then the values of variance and integral of absolute were extracted as the useful features from de-noised EMG signals. Finally, the penalty parameter and the kernel parameter were optimized by GA. The results show that the GA-SVM classifier can effectively identify five gait phases of the extremity motion, and the average accuracy is increased by 6.56%, higher than the non-parameter-optimized SVM method.

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Titel: Gait Recognition Using GA-SVM Method Based on Electromyography Signal
verfasst von: Ying Li
Farong Gao
Xiao Zheng
Haitao Gan
Verlag: Springer International Publishing
Buch: Intelligent Robotics and Applications
Print ISBN: 978-3-319-65288-7

Electronic ISBN: 978-3-319-65289-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-65289-4_30

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