Learning Hand State Estimation for a Light Exoskeleton

• URL: http://arxiv.org/abs/2411.09294v1
• Date: Thu, 14 Nov 2024 09:12:38 GMT
• Title: Learning Hand State Estimation for a Light Exoskeleton
• Authors: Gabriele Abbate, Alessandro Giusti, Luca Randazzo, Antonio Paolillo,
• Abstract summary: We propose a machine learning-based estimator of the hand state for rehabilitation purposes, using light exoskeletons. We build a supervised approach using information from the muscular activity of the forearm and the motion of the exoskeleton to reconstruct the hand's opening degree and compliance level. Our approach is validated with a real light exoskeleton.
• Score: 50.05509088121445
• License:
• Abstract: We propose a machine learning-based estimator of the hand state for rehabilitation purposes, using light exoskeletons. These devices are easy to use and useful for delivering domestic and frequent therapies. We build a supervised approach using information from the muscular activity of the forearm and the motion of the exoskeleton to reconstruct the hand's opening degree and compliance level. Such information can be used to evaluate the therapy progress and develop adaptive control behaviors. Our approach is validated with a real light exoskeleton. The experiments demonstrate good predictive performance of our approach when trained on data coming from a single user and tested on the same user, even across different sessions. This generalization capability makes our system promising for practical use in real rehabilitation.

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