Enhancing Personalized Rehabilitation: Integrating Assist-as-Needed System With Real-Time Feedback

Abstract

Abstract Conventional rehabilitation programs often lack precise real-time feedback, leading to varied training results at different centers. To address this, personalized rehabilitation strategies have been developed using robotic technologies and exoskeletons with assist-as-needed (AAN) controllers to improve recovery rates for individuals with stroke and neuromuscular disorders. This research introduces an automated therapy approach with a patient-specific AAN support strategy that provides immediate feedback, enhancing the rehabilitation process. The system uses minimal data to create effective personalized models and integrates sensors and algorithms for continuous monitoring, improving the efficacy of therapy. Initial trials with an exoskeleton that focused on elbow movements showed promising results in adjusting support in real-time and providing personalized interventions through torque-controlled motors. This system assesses impairment levels, visualizes motor energy needs, and helps design customized rehabilitation programs, significantly enhancing therapeutic results.