  The research of a socially assistive robot has a potential to augment and
assist physical therapy sessions for patients with neurological and
musculoskeletal problems (e.g. stroke). During a physical therapy session,
generating personalized feedback is critical to improve patient's engagement.
However, prior work on socially assistive robotics for physical therapy has
mainly utilized pre-defined corrective feedback even if patients have various
physical and functional abilities. This paper presents an interactive approach
of a socially assistive robot that can dynamically select kinematic features of
assessment on individual patient's exercises to predict the quality of motion
and provide patient-specific corrective feedback for personalized interaction
of a robot exercise coach.
