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

^*Corresponding author for this work

Wuhan University of Science and Technology
University of Portsmouth
Huazhong University of Science and Technology

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

1 Scopus citations

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 - Contributions Presented at the 18th UK Workshop on Computational Intelligence, 2018
Editors	Ahmad Lotfi, Caroline Langensiepen, Hamid Bouchachia, Alexander Gegov, Martin McGinnity
Publisher	Springer Verlag
Pages	372-382
Number of pages	11
ISBN (Print)	9783319979816
DOIs	https://doi.org/10.1007/978-3-319-97982-3_31
State	Published - 2019
Externally published	Yes
Event	18th UK Workshop on Computational Intelligence, UKCI 2018 - Nottingham, United Kingdom Duration: 5 Sep 2018 → 7 Sep 2018

Publication series

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

Conference

Conference	18th UK Workshop on Computational Intelligence, UKCI 2018
Country/Territory	United Kingdom
City	Nottingham
Period	5/09/18 → 7/09/18

Keywords

Local maximum
MAV
Muscle regions
Shape-preserving piecewise cubic interpolation
sEMG

Access to Document

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

Cite this

Li, J., Fang, Y., Huang, Y., Li, G., Ju, Z., & Liu, H. (2019). Towards active muscle pattern analysis for dynamic hand motions via sEMG. In A. Lotfi, C. Langensiepen, H. Bouchachia, A. Gegov, & M. McGinnity (Eds.), Advances in Computational Intelligence Systems - Contributions Presented at the 18th UK Workshop on Computational Intelligence, 2018 (pp. 372-382). (Advances in Intelligent Systems and Computing; Vol. 840). Springer Verlag. 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, 2018. editor / Ahmad Lotfi ; Caroline Langensiepen ; Hamid Bouchachia ; Alexander Gegov ; Martin McGinnity. Springer Verlag, 2019. pp. 372-382 (Advances in Intelligent Systems and Computing).

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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.",

keywords = "Local maximum, MAV, Muscle regions, Shape-preserving piecewise cubic interpolation, sEMG",

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

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 18th UK Workshop on Computational Intelligence, UKCI 2018 ; Conference date: 05-09-2018 Through 07-09-2018",

year = "2019",

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

language = "英语",

isbn = "9783319979816",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "372--382",

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

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Li, J, Fang, Y, Huang, Y, Li, G, Ju, Z & Liu, H 2019, Towards active muscle pattern analysis for dynamic hand motions via sEMG. in A Lotfi, C Langensiepen, H Bouchachia, A Gegov & M McGinnity (eds), Advances in Computational Intelligence Systems - Contributions Presented at the 18th UK Workshop on Computational Intelligence, 2018. Advances in Intelligent Systems and Computing, vol. 840, Springer Verlag, pp. 372-382, 18th UK Workshop on Computational Intelligence, UKCI 2018, Nottingham, 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, 2018. ed. / Ahmad Lotfi; Caroline Langensiepen; Hamid Bouchachia; Alexander Gegov; Martin McGinnity. Springer Verlag, 2019. p. 372-382 (Advances in Intelligent Systems and Computing; Vol. 840).

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

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

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

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.

KW - Local maximum

KW - MAV

KW - Muscle regions

KW - Shape-preserving piecewise cubic interpolation

KW - sEMG

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

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SN - 9783319979816

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BT - Advances in Computational Intelligence Systems - Contributions Presented at the 18th UK Workshop on Computational Intelligence, 2018

A2 - Lotfi, Ahmad

A2 - Langensiepen, Caroline

A2 - Bouchachia, Hamid

A2 - Gegov, Alexander

A2 - McGinnity, Martin

PB - Springer Verlag

T2 - 18th UK Workshop on Computational Intelligence, UKCI 2018

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, Langensiepen C, Bouchachia H, Gegov A, McGinnity M, editors, Advances in Computational Intelligence Systems - Contributions Presented at the 18th UK Workshop on Computational Intelligence, 2018. Springer Verlag. 2019. p. 372-382. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-97982-3_31

Towards active muscle pattern analysis for dynamic hand motions via sEMG

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