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Multi-Modal Muscle Activation Modeling Using Koopman Operator Linearization for an Ankle Exoskeleton.

Noor Hakam, Mayank Singh, Nitin Sharma

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    Summary
    This summary is machine-generated.

    This study introduces a novel data-driven model for gait rehabilitation. It enhances human-machine interaction by accurately estimating ankle torque using fused muscle signals for better exoskeleton assistance.

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    Area of Science:

    • Biomechanics
    • Robotics
    • Rehabilitation Engineering

    Background:

    • Gait impairments significantly reduce mobility and quality of life.
    • Intelligent rehabilitation devices require dynamic models for effective human-machine interaction and assistance.
    • Accurate estimation of volitional torque is crucial for personalized gait support.

    Purpose of the Study:

    • To develop a data-driven model for estimating volitional ankle torque during the gait cycle.
    • To integrate Koopman operator linear estimations with fused surface electromyography (sEMG) and ultrasound (US) imaging.
    • To improve the accuracy of state and volitional torque estimation for enhanced gait rehabilitation.

    Main Methods:

    • Utilized Koopman operator linear estimations to model nonlinear, phase-dependent ankle motion dynamics.
    • Fused surface electromyography (sEMG) and ultrasound (US) imaging data to enhance muscle activation predictions.
    • Developed a data-driven framework for real-time volitional torque estimation throughout the gait cycle.

    Main Results:

    • The integrated model demonstrated improved accuracy in estimating volitional ankle torque.
    • Fusion of sEMG and US signals provided combined electrical and morphological insights into muscle activation.
    • The framework successfully estimated nonlinear, phase-dependent dynamics of ankle motion.

    Conclusions:

    • The proposed data-driven model shows significant potential for improving gait rehabilitation strategies.
    • Accurate volitional torque estimation is achievable through the fusion of sEMG, US imaging, and Koopman operator methods.
    • This framework lays the foundation for individualized exoskeleton assistance and more effective gait rehabilitation.