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

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


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 languageEnglish
Title of host publicationIntelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III
EditorsHuayong Yang, Honghai Liu, Jun Zou, Zhouping Yin, Lianquing Liu, Geng Yang, Xiaoping Ouyang, Zhiyong Wang
Number of pages11
ISBN (Electronic)9789819964895
ISBN (Print)9789819964888
Publication statusPublished - 11 Oct 2023
EventInternational Conference on Intelligent Robotics and Applications - Hangzhou, China
Duration: 5 Jul 20237 Jul 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Intelligent Robotics and Applications


  • sEMG
  • biological relationship
  • interpretability

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