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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Improving Automatic Control of Upper-Limb Prosthesis Wrists Using Gaze-Centered Eye Tracking and Deep Learning.

Maxim Karrenbach, David Boe, Astrini Sie

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |January 31, 2022
    PubMed
    Summary

    This study introduces a gaze-centered vision system to predict prosthetic wrist rotations, reducing compensatory movements and task time for upper-limb prosthesis users. This enhances prosthetic hand performance and user experience.

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    Area of Science:

    • Robotics
    • Human-Computer Interaction
    • Biomedical Engineering

    Background:

    • Upper-limb prostheses often lack natural wrist rotation.
    • This deficiency leads to compensatory movements, overuse, and prosthesis abandonment.

    Purpose of the Study:

    • To investigate a data-driven predictive control strategy for prosthetic wrist rotation.
    • To assess the impact of gaze-centered vision for predicting user intent in object grasping tasks.

    Main Methods:

    • Developed a predictive control strategy using gaze-centered vision.
    • Implemented a user study in a virtual reality environment.
    • Collected data on compensatory movements and task completion times.

    Main Results:

    • Gaze-centered vision effectively predicts user intent for wrist rotation.
    • The predictive control system reduced shoulder compensatory movements.
    • Task completion time was significantly decreased with the implemented system.

    Conclusions:

    • Vision-based predictive control enhances prosthetic hand functionality.
    • Gaze-centered vision offers valuable insights into user intention.
    • This approach improves prosthetic performance and user outcomes.