A co-design approach for a rehabilitation robot coach for physical rehabilitation based on the error classification of motion errors
This work addresses rehabilitation healthcare for the elderly by providing assistive robotic coaching, though it appears incremental as it builds on existing motion analysis and classification methods.
The authors tackled the problem of automated physical rehabilitation coaching by developing a robot that learns ideal movements from expert demonstrations and assesses patient performance using real-time multi-level error analysis, achieving evaluation on three rehabilitation exercises.
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.