Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition

Yi Song; Yating Zhu; Enhao Zheng; Fei Tao; Qining Wang

doi:10.1007/978-3-319-08338-4_75

Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Intelligent Autonomous Systems - Proceedings of the 13th International Conference IAS, 2014
Editors	Hiroaki Yamaguchi, Nathan Michael, Karsten Berns, Emanuele Menegatti
Publisher	Springer Verlag
Pages	1035-1047
Number of pages	13
ISBN (Print)	9783319083377
DOIs	https://doi.org/10.1007/978-3-319-08338-4_75
State	Published - 2016
Externally published	Yes
Event	13th International Conference on Intelligent Autonomous Systems, IAS 2014 - Padova, Italy Duration: 15 Jul 2014 → 18 Jul 2014

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	302
ISSN (Print)	2194-5357

Conference

Conference	13th International Conference on Intelligent Autonomous Systems, IAS 2014
Country/Territory	Italy
City	Padova
Period	15/07/14 → 18/07/14

Keywords

Capacitive sensing
Genetic algorithm
Locomotion mode recognition
Lower-limb prostheses
Support vector machine

Access to Document

10.1007/978-3-319-08338-4_75

Cite this

Song, Y., Zhu, Y., Zheng, E., Tao, F., & Wang, Q. (2016). Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition. In H. Yamaguchi, N. Michael, K. Berns, & E. Menegatti (Eds.), Intelligent Autonomous Systems - Proceedings of the 13th International Conference IAS, 2014 (pp. 1035-1047). (Advances in Intelligent Systems and Computing; Vol. 302). Springer Verlag. https://doi.org/10.1007/978-3-319-08338-4_75

Song, Yi ; Zhu, Yating ; Zheng, Enhao et al. / Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition. Intelligent Autonomous Systems - Proceedings of the 13th International Conference IAS, 2014. editor / Hiroaki Yamaguchi ; Nathan Michael ; Karsten Berns ; Emanuele Menegatti. Springer Verlag, 2016. pp. 1035-1047 (Advances in Intelligent Systems and Computing).

@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",

}

Song, Y, Zhu, Y, Zheng, E, Tao, F & Wang, Q 2016, Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition. in H Yamaguchi, N Michael, K Berns & E Menegatti (eds), Intelligent Autonomous Systems - Proceedings of the 13th International Conference IAS, 2014. Advances in Intelligent Systems and Computing, vol. 302, Springer Verlag, pp. 1035-1047, 13th International Conference on Intelligent Autonomous Systems, IAS 2014, Padova, Italy, 15/07/14. https://doi.org/10.1007/978-3-319-08338-4_75

Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition. / Song, Yi; Zhu, Yating; Zheng, Enhao et al.
Intelligent Autonomous Systems - Proceedings of the 13th International Conference IAS, 2014. ed. / Hiroaki Yamaguchi; Nathan Michael; Karsten Berns; Emanuele Menegatti. Springer Verlag, 2016. p. 1035-1047 (Advances in Intelligent Systems and Computing; Vol. 302).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition

AU - Song, Yi

AU - Zhu, Yating

AU - Zheng, Enhao

AU - Tao, Fei

AU - Wang, Qining

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Capacitive sensing

KW - Genetic algorithm

KW - Locomotion mode recognition

KW - Lower-limb prostheses

KW - Support vector machine

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U2 - 10.1007/978-3-319-08338-4_75

DO - 10.1007/978-3-319-08338-4_75

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SN - 9783319083377

T3 - Advances in Intelligent Systems and Computing

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BT - Intelligent Autonomous Systems - Proceedings of the 13th International Conference IAS, 2014

A2 - Yamaguchi, Hiroaki

A2 - Michael, Nathan

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PB - Springer Verlag

T2 - 13th International Conference on Intelligent Autonomous Systems, IAS 2014

Y2 - 15 July 2014 through 18 July 2014

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Song Y, Zhu Y, Zheng E, Tao F, Wang Q. Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition. In Yamaguchi H, Michael N, Berns K, Menegatti E, editors, Intelligent Autonomous Systems - Proceedings of the 13th International Conference IAS, 2014. Springer Verlag. 2016. p. 1035-1047. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-08338-4_75

Optimizing support vector machine with genetic algorithm for capacitive sensing-based locomotion mode recognition

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