Continuous Estimation of FES-induced Neuromuscular Fatigue Using Mechanomyography Signals

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 ($\bar{r}$) with force decline in control ($\bar{r}=0.740$) and stroke ($\bar{r}=0.928$) groups ($p \le 0.005$). The P_MMG MV-driven model also accurately predicted force decline, achieving mean coefficients of determination ($R^{2}$) of 0.741 (control) and 0.774 (stroke), with strong prediction correlations ($\bar{r} > 0.87, p < 0.01$). 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.