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Lookup NU author(s): Eisa Aghchehli, Dr Matthew DysonORCiD, Professor Kianoush Nazarpour
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Surface electromyogram (EMG) signals find diverse applications in movement rehabilitation and human-computer interfacing. For instance, future advanced prostheses, which use artificial intelligence, will require EMG signals recorded from several sites on the forearm. This requirement will entail complex wiring and data handling. We present the design and evaluation of a bespoke EMG sensing system that addresses the above challenges, enables distributed signal processing, and balances local versus global power consumption. Additionally, the proposed EMG system enables the recording and simultaneous analysis of skin-sensor impedance, needed to ensure signal fidelity. We evaluated the proposed sensing system in three experiments, namely, monitoring muscle fatigue, real-time skin-sensor impedance measurement, and control of a myoelectric computer interface. The proposed system offers comparable signal acquisition characteristics to that achieved by a clinically-approved product. It will serve and integrate future myoelectric technology better via enabling distributed machine learning and improving the signal transmission efficiency.
Author(s): Aghchehli E, Kyranou I, Dyson M, Nazarpour K
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Year: 2024
Volume: 32
Pages: 2826-2834
Online publication date: 30/07/2024
Acceptance date: 08/07/2024
Date deposited: 13/08/2024
ISSN (print): 1534-4320
ISSN (electronic): 1558-0210
Publisher: IEEE
URL: https://doi.org/10.1109/TNSRE.2024.3435740
DOI: 10.1109/TNSRE.2024.3435740
PubMed id: 39078765
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