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    This study shows ultrasound imaging can estimate lower limb movements for intuitive control of assistive devices. This technology may improve mobility for individuals with impairments.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Medical Imaging

    Background:

    • Powered assistive devices require intuitive control for better clinical use.
    • Identifying user intent is crucial for seamless device operation.
    • Skeletal muscle synergistic contractions offer potential for device joint control.

    Purpose of the Study:

    • To assess the feasibility of using ultrasound (US) imaging to estimate human lower limb movements.
    • To develop an algorithm for real-time estimation of joint kinematics from muscle US data.
    • To explore US as a non-invasive neural interface for assistive device control.

    Main Methods:

    • Developed a novel algorithm to compute US features of the rectus femoris muscle.
    • Utilized a multiscale ridge filter and RANSAC for segmentation of muscle structures.
    • Employed Gaussian process regression models to estimate knee joint angle and angular velocity.
    • Tested on nine able-bodied subjects during non-weight-bearing knee flexion/extension.

    Main Results:

    • The algorithm estimated knee joint angle with a 7.45° RMSE and angular velocity with a 0.262 rad/s RMSE.
    • Achieved an average processing rate of 3-6 frames/s, indicating real-time potential.
    • Demonstrated the feasibility of using US features for motion estimation.

    Conclusions:

    • Ultrasound imaging is a feasible method for estimating human lower extremity motion.
    • Real-time US-based algorithms can serve as neural interfaces for lower limb assistive devices.
    • Wearable US imaging may enable volitional control, enhancing mobility for impaired individuals.