SEMG based intention identification of complex hand motion using nonlinear time series analysis

Yaxu Xue; Zhaojie Ju

doi:10.1109/ICIST.2019.8836817

SEMG based intention identification of complex hand motion using nonlinear time series analysis

Yaxu Xue, Zhaojie Ju

School of Computing

Pingdingshan University

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

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Abstract

This paper proposes a hand motion recognition system for classifying different complex hand motions based on Surface Electromyography (SEMG). By defining ten common hand motions, the SEMG signals are recorded based on a SEMG capture device. A series of signal processing methods, including signal denoising, and feature extraction are analyzed to acquire the SEMG features. A trained Random Forest (RF) algorithm is used for the classification of ten different hand motions. The experimental results show that the proposed hand motion recognition system has a higher classification accuracy for identifying different hand motions.

Original language	English
Title of host publication	2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	357-361
Number of pages	5
ISBN (Electronic)	978-1-5386-1728-1
ISBN (Print)	978-1-5386-1729-8
DOIs	https://doi.org/10.1109/ICIST.2019.8836817
Publication status	Published - 16 Sept 2019

Publication series

Name	2019 9th International Conference on Information Science and Technology (ICIST)
Publisher	IEEE
ISSN (Print)	2573-3311

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10.1109/ICIST.2019.8836817

ICIST_2019_paper_104_SEMG_based
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Accepted author manuscript (Post-print), 1.56 MB

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Xue, Y., & Ju, Z. (2019). SEMG based intention identification of complex hand motion using nonlinear time series analysis. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 357-361). (2019 9th International Conference on Information Science and Technology (ICIST)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIST.2019.8836817

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title = "SEMG based intention identification of complex hand motion using nonlinear time series analysis",

abstract = "This paper proposes a hand motion recognition system for classifying different complex hand motions based on Surface Electromyography (SEMG). By defining ten common hand motions, the SEMG signals are recorded based on a SEMG capture device. A series of signal processing methods, including signal denoising, and feature extraction are analyzed to acquire the SEMG features. A trained Random Forest (RF) algorithm is used for the classification of ten different hand motions. The experimental results show that the proposed hand motion recognition system has a higher classification accuracy for identifying different hand motions.",

author = "Yaxu Xue and Zhaojie Ju",

year = "2019",

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doi = "10.1109/ICIST.2019.8836817",

language = "English",

isbn = "978-1-5386-1729-8",

series = "2019 9th International Conference on Information Science and Technology (ICIST)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)",

address = "United States",

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Xue, Y & Ju, Z 2019, SEMG based intention identification of complex hand motion using nonlinear time series analysis. in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2019 9th International Conference on Information Science and Technology (ICIST), Institute of Electrical and Electronics Engineers Inc., pp. 357-361. https://doi.org/10.1109/ICIST.2019.8836817

SEMG based intention identification of complex hand motion using nonlinear time series analysis. / Xue, Yaxu; Ju, Zhaojie.
2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Institute of Electrical and Electronics Engineers Inc., 2019. p. 357-361 (2019 9th International Conference on Information Science and Technology (ICIST)).

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

TY - GEN

T1 - SEMG based intention identification of complex hand motion using nonlinear time series analysis

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AU - Ju, Zhaojie

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N2 - This paper proposes a hand motion recognition system for classifying different complex hand motions based on Surface Electromyography (SEMG). By defining ten common hand motions, the SEMG signals are recorded based on a SEMG capture device. A series of signal processing methods, including signal denoising, and feature extraction are analyzed to acquire the SEMG features. A trained Random Forest (RF) algorithm is used for the classification of ten different hand motions. The experimental results show that the proposed hand motion recognition system has a higher classification accuracy for identifying different hand motions.

AB - This paper proposes a hand motion recognition system for classifying different complex hand motions based on Surface Electromyography (SEMG). By defining ten common hand motions, the SEMG signals are recorded based on a SEMG capture device. A series of signal processing methods, including signal denoising, and feature extraction are analyzed to acquire the SEMG features. A trained Random Forest (RF) algorithm is used for the classification of ten different hand motions. The experimental results show that the proposed hand motion recognition system has a higher classification accuracy for identifying different hand motions.

U2 - 10.1109/ICIST.2019.8836817

DO - 10.1109/ICIST.2019.8836817

M3 - Conference contribution

SN - 978-1-5386-1729-8

T3 - 2019 9th International Conference on Information Science and Technology (ICIST)

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EP - 361

BT - 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

PB - Institute of Electrical and Electronics Engineers Inc.

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SEMG based intention identification of complex hand motion using nonlinear time series analysis

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