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Related Experiment Video

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Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test
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Continuous Estimation of FES-Induced Neuromuscular Fatigue Using Mechanomyography Signals.

Zehao Liu, Weiguang Huo, Zhenhua Yu

    IEEE Journal of Biomedical and Health Informatics
    |June 17, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A new wearable system using pressure-based Mechanomyography (MMG) effectively monitors muscle fatigue during Functional Electrical Stimulation (FES) therapy. This allows for optimized, closed-loop adjustments to improve rehabilitation outcomes for individuals with neurological impairments.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Neuroscience

    Background:

    • Functional Electrical Stimulation (FES) enhances extremity function post-neurological trauma but requires precise modulation to prevent muscle fatigue.
    • Standard electromyography (EMG) is incompatible with FES due to electrical interference, hindering closed-loop control.
    • Mechanomyography (MMG) offers an artifact-free alternative for monitoring muscle activity during FES.

    Purpose of the Study:

    • To introduce and validate a novel MMG-FES system for optimizing therapeutic stimulation in post-stroke individuals.
    • To assess the efficacy of pressure-based (P_MMG) and microphone-based (M_MMG) MMG in detecting FES-induced muscle fatigue.
    • To develop and evaluate a P_MMG-driven model for predicting muscle force capacity during FES.

    Main Methods:

    • Development of a wearable system integrating P_MMG and M_MMG sensors.
    • Implementation of an MMG-derived fatigue index and a Tibialis Anterior (TA) musculotendon model.
    • Execution of an isometric FES fatigue protocol on control and post-stroke participants, recording force and MMG signals.

    Main Results:

    • Pressure-based MMG (P_MMG) Mean Value (MV) signals strongly correlated with force decline during FES-induced fatigue in both control (r=0.740) and stroke (r=0.928) groups.
    • The P_MMG MV-driven model accurately predicted force decline (R²=0.741 for control, R²=0.774 for stroke).
    • Microphone-based MMG (M_MMG) showed weaker correlations with force and fatigue compared to P_MMG.

    Conclusions:

    • Pressure-based MMG serves as a robust, non-invasive indicator of FES-induced muscle fatigue.
    • A P_MMG-driven model enables continuous estimation of muscle force capacity during FES.
    • The integrated MMG-FES system facilitates closed-loop modulation for optimized rehabilitation therapy.