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ORIGINAL RESEARCH article
Front. Neurorobot.
Volume 19 - 2025 | doi: 10.3389/fnbot.2025.1531815
Medium Density EMG Armband for Gesture Recognition
Provisionally accepted- 1Newcastle University, Newcastle upon Tyne, North East England, United Kingdom
- 2University of Edinburgh, Edinburgh, Scotland, United Kingdom
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Electromyography (EMG) systems are essential for the advancement of neuroprosthetics and human-machine interfaces. However, the gap between low-density and high-density systems poses challenges to researchers in experiment design and knowledge transfer. Medium-density surface EMG systems offer a balanced alternative, providing greater spatial resolution than low-density systems while avoiding the complexity and cost of high-density arrays. In this study, we developed a research-friendly medium-density EMG system and evaluated its performance with eleven volunteers performing grasping tasks. To enhance decoding accuracy, we introduced a novel spatio-temporal convolutional neural network that integrates spatial information from additional EMG sensors with temporal dynamics. The results show that medium-density EMG sensors significantly improve classification accuracy compared to low-density systems while maintaining the same footprint. Furthermore, the proposed neural network outperforms traditional gesture decoding approaches. This work highlights the potential of medium-density EMG systems as a practical and effective solution, bridging the gap between low-and high-density systems.These findings pave the way for broader adoption in research and potential clinical applications.
Keywords: Medium-density, myoelectric control, gesture recognition, Temporal neural network, machine learning
Received: 21 Nov 2024; Accepted: 26 Mar 2025.
Copyright: © 2025 Aghchehli, Jabbari, Ma, Dyson and Nazarpour. 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: Kianoush Nazarpour, University of Edinburgh, Edinburgh, EH8 9YL, Scotland, United Kingdom
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.