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 proceedingConference 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 languageEnglish
Title of host publicationProceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Subtitle of host publicationComputational Intelligence in Robotics and Automation for the New Millennium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages860-863
Number of pages4
ISBN (Electronic)0780378660
DOIs
Publication statusPublished - 2003 Jan 1
Externally publishedYes
Event2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003 - Kobe, Japan
Duration: 2003 Jul 162003 Jul 20

Publication series

NameProceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
Volume2

Other

Other2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003
CountryJapan
CityKobe
Period03/7/1603/7/20

ASJC Scopus subject areas

  • Computational Mathematics

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  • 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). [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