Improving Automatic Control of Upper-Limb Prosthesis Wrists Using Gaze-Centered Eye Tracking and Deep Learning

Improving Automatic Control of Upper-Limb Prosthesis Wrists Using Gaze-Centered Eye Tracking and Deep Learning

Many upper-limb prostheses lack proper wrist rotation functionality, leading to users performing poor compensatory strategies, leading to overuse or abandonment. In this study, we investigate the validity of creating and implementing a data-driven predictive control strategy in object grasping tasks...

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Bibliographic Details
Main Authors: Maxim Karrenbach, David Boe, Astrini Sie, Rob Bennett, Eric Rombokas
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Gaze-centered vision
predictive control
deep learning
compensatory strategies
Online Access:https://ieeexplore.ieee.org/document/9698069/

Internet

https://ieeexplore.ieee.org/document/9698069/

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