Learning Hand State Estimation for a Light Exoskeleton

TL;DR

This paper introduces a machine learning method to estimate hand state using a light exoskeleton, aiding rehabilitation by tracking hand opening and compliance for personalized therapy.

Contribution

It presents a supervised learning approach combining muscular activity and exoskeleton motion data to estimate hand state, validated on real devices and demonstrating good generalization.

Findings

High predictive accuracy within the same user across sessions

Effective estimation of hand opening and compliance levels

Potential for practical rehabilitation applications

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.