Feature analysis for the EMG signals based on the class distance

Yuuki Yazama; Yasue Mitsukura; Minora Fukumi; Norio Akamatsu

doi:10.1109/CIRA.2003.1222292

Feature analysis for the EMG signals based on the class distance

Yuuki Yazama, Yasue Mitsukura, Minora Fukumi, Norio Akamatsu

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

14 Citations (Scopus)

Abstract

In this paper, a feature vector is extracted from an elec-tromyography (EMG) signal at a wrist, and the EMG signals based on 7 motions are recognized. In order to perform good pattern recognition, it is desirable that the distance in feature vector between classes is far, and that the variance in a class is small. In consideration of these, important frequency bands of EMG signals are selected by using a genetic algorithm. We use the selected frequency band to perform the recognition experiment of EMG signal by a neural network. Finally, the effectiveness of this method is demonstrated by means of computer simulations.

Original language	English
Title of host publication	Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Subtitle of host publication	Computational Intelligence in Robotics and Automation for the New Millennium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	860-863
Number of pages	4
ISBN (Electronic)	0780378660
DOIs	https://doi.org/10.1109/CIRA.2003.1222292
Publication status	Published - 2003 Jan 1
Externally published	Yes
Event	2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003 - Kobe, Japan Duration: 2003 Jul 16 → 2003 Jul 20

Publication series

Name	Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
Volume	2

Other

Other	2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003
Country/Territory	Japan
City	Kobe
Period	03/7/16 → 03/7/20

ASJC Scopus subject areas

Computational Mathematics

Access to Document

10.1109/CIRA.2003.1222292

Cite this

Yazama, Y., Mitsukura, Y., Fukumi, M., & Akamatsu, N. (2003). Feature analysis for the EMG signals based on the class distance. In Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation: Computational Intelligence in Robotics and Automation for the New Millennium (pp. 860-863). Article 1222292 (Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA; Vol. 2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIRA.2003.1222292

Feature analysis for the EMG signals based on the class distance. / Yazama, Yuuki; Mitsukura, Yasue; Fukumi, Minora et al.
Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation: Computational Intelligence in Robotics and Automation for the New Millennium. Institute of Electrical and Electronics Engineers Inc., 2003. p. 860-863 1222292 (Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA; Vol. 2).

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

Yazama, Y, Mitsukura, Y, Fukumi, M & Akamatsu, N 2003, Feature analysis for the EMG signals based on the class distance. in Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation: Computational Intelligence in Robotics and Automation for the New Millennium., 1222292, Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, vol. 2, Institute of Electrical and Electronics Engineers Inc., pp. 860-863, 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003, Kobe, Japan, 03/7/16. https://doi.org/10.1109/CIRA.2003.1222292

Yazama Y, Mitsukura Y, Fukumi M, Akamatsu N. Feature analysis for the EMG signals based on the class distance. In Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation: Computational Intelligence in Robotics and Automation for the New Millennium. Institute of Electrical and Electronics Engineers Inc. 2003. p. 860-863. 1222292. (Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA). doi: 10.1109/CIRA.2003.1222292

Yazama, Yuuki ; Mitsukura, Yasue ; Fukumi, Minora et al. / Feature analysis for the EMG signals based on the class distance. Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation: Computational Intelligence in Robotics and Automation for the New Millennium. Institute of Electrical and Electronics Engineers Inc., 2003. pp. 860-863 (Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA).

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AB - In this paper, a feature vector is extracted from an elec-tromyography (EMG) signal at a wrist, and the EMG signals based on 7 motions are recognized. In order to perform good pattern recognition, it is desirable that the distance in feature vector between classes is far, and that the variance in a class is small. In consideration of these, important frequency bands of EMG signals are selected by using a genetic algorithm. We use the selected frequency band to perform the recognition experiment of EMG signal by a neural network. Finally, the effectiveness of this method is demonstrated by means of computer simulations.

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Feature analysis for the EMG signals based on the class distance

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