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Koopman-Driven Grip Force Prediction Through EMG Sensing.

Tomislav Bazina, Ervin Kamenar, Maria Fonoberova

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |June 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study predicts hand grip force using surface electromyography (sEMG) signals for better robotic rehabilitation. The novel method accurately estimates and predicts grip force, improving assistive device control.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Neuroscience

    Background:

    • Loss of hand function significantly impacts daily life for individuals with conditions like stroke and multiple sclerosis.
    • Robotic rehabilitation offers promising tools for restoring hand function.
    • Surface electromyography (sEMG) can personalize robotic device force output for enhanced rehabilitation.

    Purpose of the Study:

    • To accurately predict grip force during medium wrap grasps using a single sEMG sensor pair.
    • To develop a data-driven method for grip force estimation and short-term prediction from sEMG signals.
    • To address the challenge of increasing sensor requirements in hand function rehabilitation.

    Main Methods:

    • Collected sEMG data from 13 subjects at two forearm positions, validated with a hand dynamometer.
    • Applied flexible signal processing to achieve high cross-correlations between sEMG and grip force.
    • Utilized a novel data-driven Koopman-based approach with data lifting for grip force prediction.

    Main Results:

    • Achieved a weighted mean absolute percentage error (wMAPE) of ~5.5% for grip force estimation.
    • Demonstrated ~17.9% wMAPE for 0.5-second grip force predictions.
    • Found electrode positioning had a non-significant effect on error metrics, indicating robustness.

    Conclusions:

    • Developed a fast and accurate method for estimating and predicting grip force from sEMG signals.
    • The algorithm's speed (~30 ms per 0.5-second batch) facilitates real-time implementation in robotic rehabilitation.
    • The approach enhances personalized rehabilitation by adapting device control to user's neuromuscular signals.