An Automated Planning Approach for Personalized Robot-Aided Rehabilitation Sessions

Robot-aided rehabilitation is increasingly recognized for its potential to restore functionality and improve the quality of life for individuals with neurological or musculoskeletal conditions. Personalized treatment is crucial in Healthcare 5.0, employing reactive methods for immediate assistance and feedback and deliberative approaches that strategically plan sessions based on therapeutic goals. This paper proposes a cognitive architecture for a robot-aided rehabilitation platform based on Kahneman's dual-system theory. It integrates automated planning techniques to tailor session exercises to individual user conditions and clinical objectives, optimizing therapeutic outcomes. By combining automated planning with real-time monitoring and feedback mechanisms, the proposed framework enhances the personalization of robot-aided rehabilitation interventions.