An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models

Akira Furui, Hideaki Hayashi, Toshio Tsuji

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

This paper proposes an electromyogram (EMG) pattern classification method based on a mixture of variance distribution models. A variance distribution model is a stochastic model of raw surface EMG signals in which the EMG variance is taken as a random variable, allowing the representation of uncertainty in the variance. In this paper, we extend the variance distribution model to the multidimensional case and enhance its flexibility for multichannel and processed EMG signals. The enhanced model enables the accurate classification of EMG patterns while considering the uncertainty in the EMG variance. The robustness and applicability of the proposed method are demonstrated through a simulation experiment using artificially generated data and EMG classification experiments using two real datasets.

本文言語英語
ホスト出版物のタイトル40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5216-5219
ページ数4
ISBN(電子版)9781538636466
DOI
出版ステータス出版済み - 10 26 2018
イベント40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, 米国
継続期間: 7 18 20187 21 2018

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2018-July
ISSN(印刷版)1557-170X

その他

その他40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Country米国
CityHonolulu
Period7/18/187/21/18

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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