JBHI Journal 2025 Journal Article
Continuous Estimation of FES-Induced Neuromuscular Fatigue Using Mechanomyography Signals
- Zehao Liu
- Weiguang Huo
- Zhenhua Yu
- Paul Bentley
- Anthony M. J. Bull
- Ravi Vaidyanathan
Functional Electrical Stimulation (FES), a key therapy for improving extremity function (e. g. , in post-stroke patients), is limited by rapid FES-induced muscle fatigue. Additionally, Electromyography (EMG) monitoring is significantly compromised by FES artifacts. Mechanomyography (MMG), directly immune to such electrical FES artifacts, offers a promising alternative for fatigue estimation; however, its quantitative use for closed-loop FES remains underdeveloped. This study validated an MMG-based FES fatigue assessment system, introducing a novel wearable sensor (pressure P_MMG, microphone M_MMG) and an MMG-driven Tibialis Anterior (TA) musculotendon model with an MMG-derived fatigue index. An isometric FES fatigue protocol was conducted on control ( $N=15$ ) and post-stroke ( $N=3$ ) participants, recording force and MMG signals. P_MMG Mean Value (MV) signals consistently decreased with fatigue, showing strong average Pearson correlations ( $\bar{r}$ ) with force decline in both control ( $\bar{r}=0. 740$ ) and stroke ( $\bar{r}=0. 928$ ) groups ( $p \leq 0. 005$ ). Conversely, M_MMG signals exhibited inconsistent trends and weaker force correlations, largely due to non-monotonic behavior in many participants. The P_MMG MV-driven model accurately predicted force decline, achieving mean coefficients of determination ( $R^{2}$ ) of 0. 741 (control) and 0. 774 (stroke), with strong prediction correlations ( $\bar{r} > 0. 87, p < 0. 01$ ). Model predictions utilizing M_MMG signals were successful only for participant subsets with consistent signal trends. The pressure-based P_MMG sensor provided a robust, non-invasive FES-induced fatigue indicator. The P_MMG-driven model allows continuous estimation of force capacity decline, promising for closed-loop FES to optimize rehabilitation.