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Medium density EMG armband for gesture recognition

Lookup NU author(s): Eisa Aghchehli, Dr Matthew DysonORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

Copyright © 2025 Aghchehli, Jabbari, Ma, Dyson and Nazarpour.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.

Publication metadata

Author(s): Aghchehli E, Jabbari M, Ma C, Dyson M, Nazarpour K

Publication type: Article

Publication status: Published

Journal: Frontiers in Neurorobotics

Year: 2025

Volume: 19

Online publication date: 30/04/2025

Acceptance date: 26/03/2025

Date deposited: 27/05/2025

ISSN (electronic): 1662-5218

Publisher: Frontiers Media SA

URL: https://doi.org/10.3389/fnbot.2025.1531815

DOI: 10.3389/fnbot.2025.1531815

Data Access Statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/ MoveR-Digital-Health-and-Care-Hub/Mid-density-electrodesDataset/.

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