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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Human-Computer Interaction

    Background:

    • Individuals with upper-limb disabilities often face challenges using conventional assistive technologies.
    • Robotic arms offer potential for enhanced independence, but require intuitive control interfaces.

    Purpose of the Study:

    • To develop and evaluate a multimodal body-machine interface (BoMI) for controlling assistive robotic arms.
    • To assess the BoMI's performance and accessibility for individuals with upper-limb impairments.

    Main Methods:

    • A wearable and wireless body sensor network (WBSN) using inertial measurement units (IMUs) and surface electromyography (sEMG) sensors.
    • Custom-designed sensor nodes integrated into a headset and patches on upper-body parts.
    • Control command generation based on natural head, upper-body gestures, and muscular activity.
    • Evaluation using the JACO 6-DoF assistive robotic arm and a standardized upper-limb functional test.

    Main Results:

    • The BoMI system demonstrated effective control of the robotic arm for Activities of Daily Living (ADLs).
    • Users performed tasks with a 30% time overhead compared to joystick control, indicating high efficiency.
    • Control performance improved by up to 17% on average after three trials.
    • The BoMI offers a potentially more accessible alternative for individuals unable to use joystick controllers.

    Conclusions:

    • The multimodal BoMI provides an effective and adaptable control solution for assistive robotic arms.
    • This technology shows promise for improving the quality of life and independence for individuals with upper-limb disabilities.
    • Further research can optimize the BoMI for diverse user needs and advanced robotic functionalities.