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

Akira Furui, Hideaki Hayashi, Toshio Tsuji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5216-5219
Number of pages4
ISBN (Electronic)9781538636466
DOIs
Publication statusPublished - Oct 26 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: Jul 18 2018Jul 21 2018

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2018-July
ISSN (Print)1557-170X

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period7/18/187/21/18

Fingerprint

Electromyography
Pattern recognition
Stochastic models
Uncertainty
Random variables
Experiments

All Science Journal Classification (ASJC) codes

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

Cite this

Furui, A., Hayashi, H., & Tsuji, T. (2018). An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (pp. 5216-5219). [8513446] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8513446

An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models. / Furui, Akira; Hayashi, Hideaki; Tsuji, Toshio.

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 5216-5219 8513446 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Furui, A, Hayashi, H & Tsuji, T 2018, An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models. in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018., 8513446, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 5216-5219, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, United States, 7/18/18. https://doi.org/10.1109/EMBC.2018.8513446
Furui A, Hayashi H, Tsuji T. An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 5216-5219. 8513446. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2018.8513446
Furui, Akira ; Hayashi, Hideaki ; Tsuji, Toshio. / An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 5216-5219 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).
@inproceedings{1f9d4cf7d4f14bd38e0c69a97d266c40,
title = "An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models",
abstract = "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.",
author = "Akira Furui and Hideaki Hayashi and Toshio Tsuji",
year = "2018",
month = "10",
day = "26",
doi = "10.1109/EMBC.2018.8513446",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5216--5219",
booktitle = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018",
address = "United States",

}

TY - GEN

T1 - An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models

AU - Furui, Akira

AU - Hayashi, Hideaki

AU - Tsuji, Toshio

PY - 2018/10/26

Y1 - 2018/10/26

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85056604556&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056604556&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2018.8513446

DO - 10.1109/EMBC.2018.8513446

M3 - Conference contribution

C2 - 30441514

AN - SCOPUS:85056604556

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 5216

EP - 5219

BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -