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    This study introduces a hybrid exoskeleton combining functional electrical stimulation (FES) and robotics to combat inactivity from neurological conditions. The novel control system uses neural networks for improved adaptive therapy and patient outcomes.

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    Area of Science:

    • Biomedical Engineering
    • Neurorehabilitation
    • Robotics

    Background:

    • Neurological conditions (NCs) lead to sedentary lifestyles, increasing risks of diabetes, obesity, and cardiovascular disease.
    • Current solutions like functional electrical stimulation (FES) have limitations, including rapid patient fatigue.
    • There is a critical need for advanced therapeutic systems to improve physical activity and quality of life for individuals with NCs.

    Purpose of the Study:

    • To develop a novel, robust, and adaptive control structure for a hybrid exoskeleton system.
    • To enhance functional electrical stimulation (FES)-based therapies by integrating robotic assistance.
    • To address limitations in current FES systems and improve rehabilitation outcomes for neurological conditions.

    Main Methods:

    • A hybrid exoskeleton system combining functional electrical stimulation (FES) and robotics was developed.
    • A recently developed ARISE control approach was modified and implemented for the hybrid exoskeleton.
    • Neural networks were integrated into the control law to learn and adapt to dynamic model uncertainties.
    • Lyapunov-based stability analysis was performed to rigorously evaluate the control system's performance.

    Main Results:

    • The study successfully developed and implemented a novel robust and adaptive control structure for a hybrid exoskeleton.
    • The integration of neural networks allowed the system to learn and compensate for dynamic model uncertainties.
    • The modified ARISE control approach demonstrated effective performance in the hybrid exoskeleton system.
    • Lyapunov-based stability analysis confirmed the robustness and reliability of the developed control system.

    Conclusions:

    • The developed hybrid exoskeleton with an adaptive control system offers a promising solution for improving physical activity in individuals with neurological conditions.
    • This novel approach enhances FES-based therapies by mitigating patient fatigue and adapting to individual needs.
    • The integration of neural networks and advanced control strategies represents a significant advancement in neurorehabilitation robotics.