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Upper Limb Movement Assistance Through Model-Based Path Planning and Control of Hybrid FES-Exoskeleton Systems.

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Related Experiment Video

Updated: Jul 31, 2025

The Impact of Motor Task Conditions on Goal-Directed Arm Reaching Kinematics and Trunk Compensation in Chronic Stroke Survivors
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Data-Driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury.

Derek N Wolf, Antonie J van den Bogert, Eric M Schearer

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 4, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Functional electrical stimulation (FES) helps restore reaching for spinal cord injury (SCI) patients. A new trajectory optimization method improves FES-driven reaching by using muscle data, enhancing accuracy and feasibility.

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    Area of Science:

    • Biomedical Engineering
    • Neurorehabilitation
    • Robotics

    Background:

    • Functional electrical stimulation (FES) shows potential for restoring upper-limb function after spinal cord injury (SCI).
    • Achieving effective FES-controlled reaching is challenging due to limited individual muscle capabilities in SCI.
    • Existing control strategies struggle with the complexities of FES-driven movements.

    Purpose of the Study:

    • To develop and evaluate a novel trajectory optimization method for FES-driven reaching.
    • To incorporate experimentally measured muscle capability data into trajectory planning.
    • To assess the method's performance with different FES control structures.

    Main Methods:

    • Developed a trajectory optimization algorithm using subject-specific muscle capability data.
    • Simulated FES-driven reaching tasks for an individual with SCI.
    • Compared the optimization method against direct-to-target paths using feedback, feedforward-feedback, and model predictive control.

    Main Results:

    • Trajectory optimization significantly improved the ability to reach targets compared to naive paths.
    • Enhanced accuracy was observed for feedforward-feedback and model predictive control strategies when combined with trajectory optimization.
    • The method demonstrated feasibility in a simulated SCI scenario.

    Conclusions:

    • Trajectory optimization using muscle capability data is a viable approach to improve FES-driven reaching.
    • This method enhances the performance of common FES control structures.
    • Practical implementation of this trajectory optimization technique is recommended to advance FES applications for SCI.