Examining the impact of muscle-electrode distance in sEMG based hand motion recognition

Jinwei Shi; mingchun  Liu; Yinfeng Fang; Jiahui Yu; Hongwei Gao; Zhaojie Ju

doi:10.1007/978-981-99-6489-5_5

Examining the impact of muscle-electrode distance in sEMG based hand motion recognition

Jinwei Shi, mingchun Liu, Yinfeng Fang, Jiahui Yu, Hongwei Gao, Zhaojie Ju

School of Computing

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

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Abstract

There are several factors that affect the sEMG signal during the process from its generation to its acquisition by sEMG devices. In this study, we tried to explain the physiological functional relationship between sEMG signals and muscles and between muscles and gestures in the human right forearm to increase confidence in the application of artificial intelligence in the medical field. For this purpose, we simulated the muscle and electrode positions with a 3D model to calculate their distance relationship, designed a cuff based on this model, and considered the effect of different distance solving methods on gesture recognition. The results showed that the highest accuracy of 93.95% was achieved for gesture recognition with the center of gravity method to find the electrode-to-muscle distance when the ratio of muscle electrode distance to the number of nerve muscle branches was 1:0.1. It is explained that the distance factor is the main factor affecting the recognition of sEMG signals, and an appropriate increase in the muscle length or neuromuscular branch number factor will play a positive role in the accuracy. The visualization of muscle activation further verifies and explains the relationship between sEMG signals and muscles, which makes the rehabilitation training more scientific and effective.

Original language	English
Title of host publication	Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III
Editors	Huayong Yang, Honghai Liu, Jun Zou, Zhouping Yin, Lianquing Liu, Geng Yang, Xiaoping Ouyang, Zhiyong Wang
Publisher	Springer
Pages	55-67
Number of pages	11
ISBN (Electronic)	9789819964895
ISBN (Print)	9789819964888
DOIs	https://doi.org/10.1007/978-981-99-6489-5_5
Publication status	Published - 11 Oct 2023
Event	International Conference on Intelligent Robotics and Applications - Hangzhou, China Duration: 5 Jul 2023 → 7 Jul 2023

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	14269
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Intelligent Robotics and Applications
Country/Territory	China
City	Hangzhou
Period	5/07/23 → 7/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

sEMG
biological relationship
interpretability

Access to Document

10.1007/978-981-99-6489-5_5

Examining_the_Impact_of_Muscle-Electrode_Distance_in_sEMG_Based_Hand_Motion_Recognition
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-981-99-6489-5_5.
Accepted author manuscript (Post-print), 4.04 MB

Cite this

Shi, J., Liu, M., Fang, Y., Yu, J., Gao, H., & Ju, Z. (2023). Examining the impact of muscle-electrode distance in sEMG based hand motion recognition. In H. Yang, H. Liu, J. Zou, Z. Yin, L. Liu, G. Yang, X. Ouyang, & Z. Wang (Eds.), Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III (pp. 55-67). (Lecture Notes in Computer Science; Vol. 14269). Springer. https://doi.org/10.1007/978-981-99-6489-5_5

Shi, Jinwei ; Liu, mingchun ; Fang, Yinfeng et al. / Examining the impact of muscle-electrode distance in sEMG based hand motion recognition. Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III. editor / Huayong Yang ; Honghai Liu ; Jun Zou ; Zhouping Yin ; Lianquing Liu ; Geng Yang ; Xiaoping Ouyang ; Zhiyong Wang. Springer, 2023. pp. 55-67 (Lecture Notes in Computer Science).

@inproceedings{403248f5e42749fc9e306f2fc5b1fb56,

title = "Examining the impact of muscle-electrode distance in sEMG based hand motion recognition",

abstract = "There are several factors that affect the sEMG signal during the process from its generation to its acquisition by sEMG devices. In this study, we tried to explain the physiological functional relationship between sEMG signals and muscles and between muscles and gestures in the human right forearm to increase confidence in the application of artificial intelligence in the medical field. For this purpose, we simulated the muscle and electrode positions with a 3D model to calculate their distance relationship, designed a cuff based on this model, and considered the effect of different distance solving methods on gesture recognition. The results showed that the highest accuracy of 93.95\% was achieved for gesture recognition with the center of gravity method to find the electrode-to-muscle distance when the ratio of muscle electrode distance to the number of nerve muscle branches was 1:0.1. It is explained that the distance factor is the main factor affecting the recognition of sEMG signals, and an appropriate increase in the muscle length or neuromuscular branch number factor will play a positive role in the accuracy. The visualization of muscle activation further verifies and explains the relationship between sEMG signals and muscles, which makes the rehabilitation training more scientific and effective.",

keywords = "sEMG, biological relationship, interpretability",

author = "Jinwei Shi and mingchun Liu and Yinfeng Fang and Jiahui Yu and Hongwei Gao and Zhaojie Ju",

year = "2023",

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doi = "10.1007/978-981-99-6489-5\_5",

language = "English",

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series = "Lecture Notes in Computer Science",

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editor = "Huayong Yang and Honghai Liu and Jun Zou and Zhouping Yin and Lianquing Liu and Geng Yang and Xiaoping Ouyang and Zhiyong Wang",

booktitle = "Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III",

address = "Netherlands",

note = "International Conference on Intelligent Robotics and Applications ; Conference date: 05-07-2023 Through 07-07-2023",

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Shi, J, Liu, M, Fang, Y, Yu, J, Gao, H & Ju, Z 2023, Examining the impact of muscle-electrode distance in sEMG based hand motion recognition. in H Yang, H Liu, J Zou, Z Yin, L Liu, G Yang, X Ouyang & Z Wang (eds), Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III. Lecture Notes in Computer Science, vol. 14269, Springer, pp. 55-67, International Conference on Intelligent Robotics and Applications, Hangzhou, China, 5/07/23. https://doi.org/10.1007/978-981-99-6489-5_5

Examining the impact of muscle-electrode distance in sEMG based hand motion recognition. / Shi, Jinwei; Liu, mingchun ; Fang, Yinfeng et al.
Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III. ed. / Huayong Yang; Honghai Liu; Jun Zou; Zhouping Yin; Lianquing Liu; Geng Yang; Xiaoping Ouyang; Zhiyong Wang. Springer, 2023. p. 55-67 (Lecture Notes in Computer Science; Vol. 14269).

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

TY - GEN

T1 - Examining the impact of muscle-electrode distance in sEMG based hand motion recognition

AU - Shi, Jinwei

AU - Liu, mingchun

AU - Fang, Yinfeng

AU - Yu, Jiahui

AU - Gao, Hongwei

AU - Ju, Zhaojie

PY - 2023/10/11

Y1 - 2023/10/11

N2 - There are several factors that affect the sEMG signal during the process from its generation to its acquisition by sEMG devices. In this study, we tried to explain the physiological functional relationship between sEMG signals and muscles and between muscles and gestures in the human right forearm to increase confidence in the application of artificial intelligence in the medical field. For this purpose, we simulated the muscle and electrode positions with a 3D model to calculate their distance relationship, designed a cuff based on this model, and considered the effect of different distance solving methods on gesture recognition. The results showed that the highest accuracy of 93.95% was achieved for gesture recognition with the center of gravity method to find the electrode-to-muscle distance when the ratio of muscle electrode distance to the number of nerve muscle branches was 1:0.1. It is explained that the distance factor is the main factor affecting the recognition of sEMG signals, and an appropriate increase in the muscle length or neuromuscular branch number factor will play a positive role in the accuracy. The visualization of muscle activation further verifies and explains the relationship between sEMG signals and muscles, which makes the rehabilitation training more scientific and effective.

AB - There are several factors that affect the sEMG signal during the process from its generation to its acquisition by sEMG devices. In this study, we tried to explain the physiological functional relationship between sEMG signals and muscles and between muscles and gestures in the human right forearm to increase confidence in the application of artificial intelligence in the medical field. For this purpose, we simulated the muscle and electrode positions with a 3D model to calculate their distance relationship, designed a cuff based on this model, and considered the effect of different distance solving methods on gesture recognition. The results showed that the highest accuracy of 93.95% was achieved for gesture recognition with the center of gravity method to find the electrode-to-muscle distance when the ratio of muscle electrode distance to the number of nerve muscle branches was 1:0.1. It is explained that the distance factor is the main factor affecting the recognition of sEMG signals, and an appropriate increase in the muscle length or neuromuscular branch number factor will play a positive role in the accuracy. The visualization of muscle activation further verifies and explains the relationship between sEMG signals and muscles, which makes the rehabilitation training more scientific and effective.

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KW - biological relationship

KW - interpretability

U2 - 10.1007/978-981-99-6489-5_5

DO - 10.1007/978-981-99-6489-5_5

M3 - Conference contribution

SN - 9789819964888

T3 - Lecture Notes in Computer Science

SP - 55

EP - 67

BT - Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III

A2 - Yang, Huayong

A2 - Liu, Honghai

A2 - Zou, Jun

A2 - Yin, Zhouping

A2 - Liu, Lianquing

A2 - Yang, Geng

A2 - Ouyang, Xiaoping

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PB - Springer

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ER -

Shi J, Liu M, Fang Y, Yu J, Gao H, Ju Z. Examining the impact of muscle-electrode distance in sEMG based hand motion recognition. In Yang H, Liu H, Zou J, Yin Z, Liu L, Yang G, Ouyang X, Wang Z, editors, Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III. Springer. 2023. p. 55-67. (Lecture Notes in Computer Science). doi: 10.1007/978-981-99-6489-5_5

Examining the impact of muscle-electrode distance in sEMG based hand motion recognition

Abstract

Publication series

Conference

UN SDGs

Keywords

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