Purpose The purpose of this study is to propose and experimentally validate an adaptive robotic rehabilitation system for elbow joint recovery that delivers personalized assistance while accounting for patient-specific biomechanics and real-time muscle fatigue. Design/methodology/approach The proposed system is based on a structured three-phase rehabilitation protocol consisting of passive parameter extraction, active torque identification and fatigue-aware continuous exercise. A personalized dynamic biomechanical model of the elbow joint is embedded within the control architecture to distinguish passive joint torque from voluntary active torque, enabling accurate patient-specific assistance. Muscle fatigue is estimated online using a K-nearest neighbors (K-NN) classifier trained on time-domain features extracted from surface electromyography (sEMG) signals. The estimated fatigue index is continuously used to adapt the level of robotic assistance, preventing overexertion while encouraging voluntary participation. Findings Experimental validation was conducted with four orthopedic patients undergoing post-fracture elbow rehabilitation. The system demonstrated accurate joint torque estimation, achieving a root mean square error between 0.17 and 0.29 Nm. The proposed fatigue estimation method achieved a classification accuracy exceeding 93%. Compared to baseline conditions, passive joint torque was reduced by 39%, active torque contribution increased by 85% and elbow range of motion improved by 35%. These preliminary results suggest the feasibility of the proposed approach to provide safe, effective and adaptive rehabilitation assistance. Originality/value This work introduces a novel fatigue-aware robotic rehabilitation framework that combines personalized biomechanical modeling with real-time sEMG-based fatigue estimation. The proposed approach enables adaptive, patient-specific control, contributing to the development of intelligent robotic systems for musculoskeletal rehabilitation.
Abdallah et al. (Thu,) studied this question.