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Dynamics Combined With Hill Model for Functional Electrical Stimulation Ankle Angle Prediction.

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    A new Functional Electrical Stimulation (FES) model accurately simulates ankle dorsiflexion, overcoming limitations of EMG-based methods for improved rehabilitation research and patient training.

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    Area of Science:

    • Biomechanics
    • Rehabilitation Engineering
    • Neuroprosthetics

    Background:

    • Musculoskeletal models are crucial for ankle rehabilitation research.
    • Existing models often rely on EMG, which has limitations like sensitivity to artifacts and electrode placement.
    • There's a need for models that address the complex, dynamic nature of joint movement.

    Purpose of the Study:

    • To propose and validate a novel Functional Electrical Stimulation (FES) model for simulating ankle dorsiflexion.
    • To overcome the limitations of EMG-based models in clinical settings.
    • To provide a tool for developing closed-loop feedback control systems for FES.

    Main Methods:

    • Developed an FES model integrating Hill-based muscle contraction dynamics and ankle inverse dynamics.
    • Utilized the extended Kalman filter (EKF) algorithm for parameter identification.
    • Validated the model using experimental data from healthy volunteers.

    Main Results:

    • The model effectively simulated ankle dorsiflexion, connecting FES parameters to joint torques and angles.
    • Achieved a root mean square error (RMSE) of 11.93%±0.53% and a normalized RMSE (NRMSE) of 1.39°±0.26°.
    • Demonstrated the model's ability to predict ankle joint angle variations based on electrical stimulation parameters.

    Conclusions:

    • The proposed FES model accurately predicts ankle joint movement during dorsiflexion.
    • This model is vital for advancing closed-loop FES control strategies.
    • It holds significant potential for enhancing patient rehabilitation training.