A co-design approach for a rehabilitation robot coach for physical rehabilitation based on the error classification of motion errors

TL;DR

This paper presents a co-designed rehabilitation robot coach that uses probabilistic motion modeling and real-time error classification to provide personalized feedback and improve patient exercise performance.

Contribution

It introduces a novel human-robot interaction system that learns ideal movements from experts and classifies motion errors in real-time for effective patient coaching.

Findings

Effective real-time error classification for rehabilitation exercises

Successful learning of ideal movements using Gaussian Mixture Models

Potential for personalized feedback to improve patient outcomes

Abstract

The rising number of the elderly incurs growing concern about healthcare, and in particular rehabilitation healthcare. Assistive technology and assistive robotics in particular may help to improve this process. We develop a robot coach capable of demonstrating rehabilitation exercises to patients, watch a patient carry out the exercises and give him feedback so as to improve his performance and encourage him. The HRI of the system is based on our study with a team of rehabilitation therapists and with the target population.The system relies on human motion analysis. We develop a method for learning a probabilistic representation of ideal movements from expert demonstrations. A Gaussian Mixture Model is employed from position and orientation features captured using a Microsoft Kinect v2. For assessing patients' movements, we propose a real-time multi-level analysis to both temporally and spatially identify and explain body part errors. This analysis combined with a classification algorithm allows the robot to provide coaching advice to make the patient improve his movements. The evaluation on three rehabilitation exercises shows the potential of the proposed approach for learning and assessing kinaesthetic movements.