REPAIR Platform: Robot-AidEd PersonAlIzed Rehabilitation

Physical rehabilitation is essential for restoring functionality and improving the quality of life for individuals affected by neurological or musculoskeletal conditions. Rehabilitation robots emerged as key-enabling technology to deliver intensive treatments and objectively quantify patients’ motor performance. In the context of Healthcare 5.0, personalization of the treatment is paramount to improve the effectiveness of the interventions. Personalization can be implemented reactively, by providing real-time physical assistance and feedback, and deliberatively, by planning sessions based on therapeutic goals. Inspired by Kahneman’s dual-system theory, this paper proposes a cognitive architecture for a robot-aided rehabilitation platform capable of delivering personalized treatment through deliberative and reactive techniques. The proposed cognitive architecture is described and validated through experimental sessions. Six healthy participants were enrolled in the experiments, simulating a robot-aided rehabilitation session with a TIAGo service robot serving as the physical interface to deliver the planned session. The results highlight that the plans generated according to different clinical objectives elicited distinct physiological responses from the participants, demonstrating the effectiveness of the personalized approach.