  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.
