Proposal of Assistant System for Self-rehabilitation at Home based on Decision Tree: Study of Sanding Task

Abstract

Rehabilitation is important for hemiplegic patients after stroke to improve their motor deficit. For recovering and keeping their motor function following stroke, it may be effective to do rehabilitation at not only rehabilitation facility but also their home continuously. In this paper, we propose a rehabilitation assistant system at home for patients who go to rehabilitation facility regularly to perform rehabilitation in the right form which is taught by therapists at rehabilitation facility. This system implements a rehabilitation form classification engine based on decision tree algorithm applied to accelerometer data obtained during rehabilitation action and form classes defined by therapists. We evaluated the accuracy of the form classification engine with accelerometer data of simulated actions by 5 subjects without any disability. The accuracy of the proposed engine was more than 70% all over the subjects.