Designing Personalized Interaction of a Socially Assistive Robot for Stroke Rehabilitation Therapy
This addresses the need for personalized interaction in physical therapy for stroke patients, representing an incremental improvement over prior work that used pre-defined feedback.
The paper tackles the problem of generating personalized feedback in socially assistive robotics for stroke rehabilitation by dynamically selecting kinematic features to predict motion quality and provide patient-specific corrective feedback, resulting in a robot exercise coach that adapts to individual patient's physical and functional abilities.
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.