Towards active muscle pattern analysis for dynamic hand motions via sEMG

Jiahan Li; Yinfeng Fang; Yongan Huang; Gongfa Li; Zhaojie Ju; Honghai Liu

doi:10.1007/978-3-319-97982-3_31

Towards active muscle pattern analysis for dynamic hand motions via sEMG

Jiahan Li, Yinfeng Fang, Yongan Huang, Gongfa Li, Zhaojie Ju, Honghai Liu

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

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Abstract

Surface Electromyographys (sEMG) as a widespread human-computer interaction method can reflect the activity of human muscles. When the human forearm finishes different hand motions, there will be strong sEMG signals in different regions of the skin surface. This paper investigates the mapping relationship between sEMG signal patterns and the dynamic hand motions. Four different hand motions are studied based on the extracted signal with mean absolute value (MAV) features and the shape-preserving piecewise cubic interpolation method. In the experiments, a 16-channel electrode sleeve is used to collect 9-subject EMG signals. According to the distribution of electrodes in the forearm, the forearm surface is divided into 8 different muscle regions. The preliminary experimental results show that different hand motions can cause different distribution of sEMG signals in different regions. It confirms that different subjects show similar patterns for the same motions. The experimental results can be applied as new sEMG features with a higher computational speed.

Original language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK
Editors	Ahmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity
Publisher	Springer
Pages	372-382
ISBN (Electronic)	978-3-319-97982-3
ISBN (Print)	978-3-319-97981-6
DOIs	https://doi.org/10.1007/978-3-319-97982-3_31
Publication status	Published - Sept 2018
Event	18th UK Workshop on Computational Intelligence - Nottingham, United Kingdom Duration: 5 Sept 2018 → 7 Sept 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	840
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Workshop

Workshop	18th UK Workshop on Computational Intelligence
Country/Territory	United Kingdom
Period	5/09/18 → 7/09/18

Access to Document

10.1007/978-3-319-97982-3_31

Towards Active Muscle Pattern Analysis for Dynamic Hand Motions via sEMG
The final authenticated version is available online at: http://dx.doi.org/10.1007%2F978-3-319-97982-3_31.
Accepted author manuscript (Post-print), 1.72 MB

Cite this

Li, J., Fang, Y., Huang, Y., Li, G., Ju, Z., & Liu, H. (2018). Towards active muscle pattern analysis for dynamic hand motions via sEMG. In A. Lotfi, H. Bouchachia, A. Gegov, C. Langensiepen, & M. McGinnity (Eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK (pp. 372-382). (Advances in Intelligent Systems and Computing ; Vol. 840). Springer. https://doi.org/10.1007/978-3-319-97982-3_31

Li, Jiahan ; Fang, Yinfeng ; Huang, Yongan et al. / Towards active muscle pattern analysis for dynamic hand motions via sEMG. Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. editor / Ahmad Lotfi ; Hamid Bouchachia ; Alexander Gegov ; Caroline Langensiepen ; Martin McGinnity. Springer, 2018. pp. 372-382 (Advances in Intelligent Systems and Computing ).

@inproceedings{02d4ddaecc73412eb81a3659f4cb2586,

title = "Towards active muscle pattern analysis for dynamic hand motions via sEMG",

abstract = "Surface Electromyographys (sEMG) as a widespread human-computer interaction method can reflect the activity of human muscles. When the human forearm finishes different hand motions, there will be strong sEMG signals in different regions of the skin surface. This paper investigates the mapping relationship between sEMG signal patterns and the dynamic hand motions. Four different hand motions are studied based on the extracted signal with mean absolute value (MAV) features and the shape-preserving piecewise cubic interpolation method. In the experiments, a 16-channel electrode sleeve is used to collect 9-subject EMG signals. According to the distribution of electrodes in the forearm, the forearm surface is divided into 8 different muscle regions. The preliminary experimental results show that different hand motions can cause different distribution of sEMG signals in different regions. It confirms that different subjects show similar patterns for the same motions. The experimental results can be applied as new sEMG features with a higher computational speed.",

author = "Jiahan Li and Yinfeng Fang and Yongan Huang and Gongfa Li and Zhaojie Ju and Honghai Liu",

year = "2018",

month = sep,

doi = "10.1007/978-3-319-97982-3_31",

language = "English",

isbn = "978-3-319-97981-6",

series = "Advances in Intelligent Systems and Computing ",

publisher = "Springer",

pages = "372--382",

editor = "Ahmad Lotfi and Hamid Bouchachia and Alexander Gegov and Caroline Langensiepen and Martin McGinnity",

booktitle = "Advances in Computational Intelligence Systems",

note = "18th UK Workshop on Computational Intelligence ; Conference date: 05-09-2018 Through 07-09-2018",

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Li, J, Fang, Y, Huang, Y, Li, G, Ju, Z & Liu, H 2018, Towards active muscle pattern analysis for dynamic hand motions via sEMG. in A Lotfi, H Bouchachia, A Gegov, C Langensiepen & M McGinnity (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. Advances in Intelligent Systems and Computing , vol. 840, Springer, pp. 372-382, 18th UK Workshop on Computational Intelligence, United Kingdom, 5/09/18. https://doi.org/10.1007/978-3-319-97982-3_31

Towards active muscle pattern analysis for dynamic hand motions via sEMG. / Li, Jiahan; Fang, Yinfeng; Huang, Yongan et al.
Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. ed. / Ahmad Lotfi; Hamid Bouchachia; Alexander Gegov; Caroline Langensiepen; Martin McGinnity. Springer, 2018. p. 372-382 (Advances in Intelligent Systems and Computing ; Vol. 840).

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

TY - GEN

T1 - Towards active muscle pattern analysis for dynamic hand motions via sEMG

AU - Li, Jiahan

AU - Fang, Yinfeng

AU - Huang, Yongan

AU - Li, Gongfa

AU - Ju, Zhaojie

AU - Liu, Honghai

PY - 2018/9

Y1 - 2018/9

N2 - Surface Electromyographys (sEMG) as a widespread human-computer interaction method can reflect the activity of human muscles. When the human forearm finishes different hand motions, there will be strong sEMG signals in different regions of the skin surface. This paper investigates the mapping relationship between sEMG signal patterns and the dynamic hand motions. Four different hand motions are studied based on the extracted signal with mean absolute value (MAV) features and the shape-preserving piecewise cubic interpolation method. In the experiments, a 16-channel electrode sleeve is used to collect 9-subject EMG signals. According to the distribution of electrodes in the forearm, the forearm surface is divided into 8 different muscle regions. The preliminary experimental results show that different hand motions can cause different distribution of sEMG signals in different regions. It confirms that different subjects show similar patterns for the same motions. The experimental results can be applied as new sEMG features with a higher computational speed.

AB - Surface Electromyographys (sEMG) as a widespread human-computer interaction method can reflect the activity of human muscles. When the human forearm finishes different hand motions, there will be strong sEMG signals in different regions of the skin surface. This paper investigates the mapping relationship between sEMG signal patterns and the dynamic hand motions. Four different hand motions are studied based on the extracted signal with mean absolute value (MAV) features and the shape-preserving piecewise cubic interpolation method. In the experiments, a 16-channel electrode sleeve is used to collect 9-subject EMG signals. According to the distribution of electrodes in the forearm, the forearm surface is divided into 8 different muscle regions. The preliminary experimental results show that different hand motions can cause different distribution of sEMG signals in different regions. It confirms that different subjects show similar patterns for the same motions. The experimental results can be applied as new sEMG features with a higher computational speed.

U2 - 10.1007/978-3-319-97982-3_31

DO - 10.1007/978-3-319-97982-3_31

M3 - Conference contribution

SN - 978-3-319-97981-6

T3 - Advances in Intelligent Systems and Computing

SP - 372

EP - 382

BT - Advances in Computational Intelligence Systems

A2 - Lotfi, Ahmad

A2 - Bouchachia, Hamid

A2 - Gegov, Alexander

A2 - Langensiepen, Caroline

A2 - McGinnity, Martin

PB - Springer

T2 - 18th UK Workshop on Computational Intelligence

Y2 - 5 September 2018 through 7 September 2018

ER -

Li J, Fang Y, Huang Y, Li G, Ju Z , Liu H. Towards active muscle pattern analysis for dynamic hand motions via sEMG. In Lotfi A, Bouchachia H, Gegov A, Langensiepen C, McGinnity M, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. Springer. 2018. p. 372-382. (Advances in Intelligent Systems and Computing ). Epub 2018 Aug 11. doi: 10.1007/978-3-319-97982-3_31

Towards active muscle pattern analysis for dynamic hand motions via sEMG

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