Continuous estimation of FES-induced neuromuscular fatigue using mechanomyography signals
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Inducing muscle activity with electric current - Functional Electrical Stimulation (FES) - is a key therapy for improving extremity function after neurological trauma (e.g. post-stroke). Positive outcomes, however, are heavily dependent on modulating stimulation to maximise response while limiting rapid FES-induced muscle fatigue. Feedback control is not possible today with standard electromyography (EMG); electrical stimulation obscures EMG measurement, severely limiting range of use and benefits. Mechanomyography (MMG), though underexplored, is immune to electrical artifacts, enabling closed-loop modulation. We introduce and validate a novel MMG-FES system to optimise therapeutic stimulation post-stroke. It combines a new wearable with two myography modalities (pressure P_MMG, microphone M_MMG), an MMG- Tibialis Anterior (TA) musculotendon model and an MMG-derived fatigue index. An isometric FES fatigue protocol is executed on control (N=15) and post-stroke (N=3) participants, recording force and MMG signals. The P_MMG Mean Value (MV) signals consistently decreased with fatigue, showing strong Pearson correlations (r¯) with force decline in control (r¯=0.740) and stroke (r¯=0.928) groups (p≤0.005). The P_MMG MV-driven model also accurately predicted force decline, achieving mean coefficients of determination (R2) of 0.741 (control) and 0.774 (stroke), with strong prediction correlations (r¯>0.87,p<0.01). Conversely, M_MMG prediction had weaker correlation to force and fatigue. We conclude: 1) the pressure-based P_MMG sensor is a robust, non-invasive FES-induced fatigue indicator; 2) the P_MMG-driven model allows continuous estimation of force capacity; and 3) the combined system enables closed-loop FES modulation to optimize rehabilitation.
Date Issued
2025-11-01
Date Acceptance
2025-06-01
Citation
IEEE Journal of Biomedical and Health Informatics, 2025, 29 (11), pp.7969-7982
ISSN
2168-2208
Publisher
Institute of Electrical and Electronics Engineers
Start Page
7969
End Page
7982
Journal / Book Title
IEEE Journal of Biomedical and Health Informatics
Volume
29
Issue
11
Copyright Statement
Copyright © 2025 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Date Publish Online
2025-06-17