Muscle fatigue limits the effectiveness of functional electrical stimulation (FES), yet fatigue monitoring during active stimulation remains challenging because electromyography (EMG) is severely contaminated by stimulation artifacts. To address this issue, this study proposes an integrated mechanomyography-based sensing framework (MMG-FES) for artifact-resistant fatigue monitoring during stimulation. Fatigue states were first characterized during voluntary movement using established EMG spectral indicators, including median frequency (MF) and mean power frequency (MPF), and then linked to MMG-derived features, including high-frequency energy ratio (HFER) and spectral bandwidth (BW), for fatigue recognition under stimulation conditions. Experimental results showed consistent fatigue-related trends, with EMG spectral features decreasing and MMG features exhibiting coordinated spectral redistribution across repeated contractions. Using these features, the proposed framework achieved fatigue-state recognition accuracies of 0.93 and 0.96 with logistic regression and random forest, respectively. These findings support MMG as a physiologically meaningful and stimulation-compatible modality for fatigue-aware closed-loop neuromuscular stimulation.

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