ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Translational Neuroscience
This article is part of the Research TopicAdvanced Computational Framework and AI Model: Powering Breakthroughs in Neurological Disorders HealthcareView all articles
Influence of Electrode Placement on the Recognition of Different Gesture Categories Using High-Density sEMG
Provisionally accepted
- 1Shanghai University of International Business and Economics, Shanghai, China
- 2Guangxi University of Science and Technology, Liuzhou, China
- 3East China University of Science and Technology, Shanghai, China
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High-density surface electromyography (HD-sEMG)-based gesture recognition serves as a critical interface for human-computer interaction (HCI). However, recognition accuracy exhibits a significant dependency on gesture complexity and electrode positioning. To address this, we systematically investigated the relationship between gesture types and sEMG electrode placement locations through intra-subject, inter-day, and inter-subject validation protocols. Two distinct gesture categories were analyzed, i.e., single-degree-of-freedom (single-DoF) gestures and daily-used multi-finger synergistic gestures. Using an open-access gesture dataset, HD-sEMG signals were acquired from three forearm regions: the distal wrist, mid-forearm, and proximal elbow, separately. Classification results using support vector machine (SVM) revealed that single-DoF gestures achieved peak accuracy with distal wrist signals (98.63% for intra-subject, 79.73% for inter-day, and 75.47% for inter-subject validation protocols), whereas daily-used gestures performed optimally with signals from the mid-forearm and proximal elbow regions. These findings demonstrate the specific relationship between electrode placement and gesture type, providing valuable insights for EMG-HCI design and sensor placement strategies based on the nature of the target gesture.
Keywords: gesture category, Hand gesture recognition, HD-sEMG, human-machine interaction, Sampling position, SVM
Received: 20 Nov 2025; Accepted: 17 Dec 2025.
Copyright: © 2025 Qiu, Liu and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiaodong Liu
Xinming Ye
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