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Tolerance of neural decoding errors for powered artificial legs: A pilot study.

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    This summary is machine-generated.

    This study introduces a fault-tolerant control (FTC) strategy to improve the safety of powered artificial legs controlled by neural-machine interfaces (NMI). The FTC system effectively counteracts NMI decoding errors, enhancing user confidence and safety during walking.

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    Area of Science:

    • Biomedical Engineering
    • Neuroprosthetics
    • Rehabilitation Robotics

    Background:

    • Neural-machine interfaces (NMI) enable intuitive control of powered prostheses.
    • NMI decoding errors can compromise user safety and prosthesis functionality.
    • Existing methods struggle to completely eliminate NMI decoding errors.

    Purpose of the Study:

    • To develop and evaluate a fault-tolerant control (FTC) strategy for powered artificial legs.
    • To enhance the robustness of NMI-controlled prostheses against decoding errors.
    • To improve the safety and reliability of neural control in lower-limb prostheses.

    Main Methods:

    • Implemented a novel reconfiguration mechanism with an NMI decoding error detector.
    • Integrated the FTC strategy into a prototypical powered knee prosthesis.
    • Conducted preliminary testing with transfemoral amputees simulating NMI errors.

    Main Results:

    • The FTC strategy effectively counteracted the disruptive effects of simulated NMI decoding errors.
    • Reduced mechanical work changes around the prosthetic knee joint caused by NMI errors.
    • Demonstrated improved stability and safety during walking with the FTC system.

    Conclusions:

    • The developed FTC strategy offers a potential engineering solution for safer NMI-controlled powered artificial legs.
    • FTC enhances the resilience of neuroprosthetic control systems to decoding inaccuracies.
    • This approach can increase user confidence and safety in advanced prosthetic devices.