Reinforcement Learning in Wearable Robotic Systems for Orthopedic Rehabilitation: An Elbow-Focused Narrative Review

Abstract

Orthopedic rehabilitation after upper-limb trauma increasingly emphasizes protected early motion, quantitative monitoring, and patient-specific assistance. This narrative review examines how reinforcement learning (RL) may contribute to wearable robotic systems for orthopedic rehabilitation, with emphasis on elbow-centered applications and transferable evidence from upper-limb exoskeletons, prosthetic-control studies, and musculoskeletal simulation. Literature from clinical and engineering sources was synthesized across four themes: clinical rationale, device platforms, control architecture, and translational readiness. The reviewed evidence suggests that the most plausible near-term platform is an externally worn powered orthosis rather than an implanted robotic joint. Across studies, RL is most defensible as a supervisory or personalization layer that adapts assistance within hard constraints on torque, speed, and range of motion, rather than as an unconstrained end-to-end controller. Multimodal sensing, especially combinations of electromyography, kinematics, and interaction sensing, appears more robust than any single intent channel. Digital twins and musculoskeletal simulators provide a practical substrate for offline training and conservative policy transfer, but fracture-specific clinical validation remains limited. Key barriers include alignment, comfort, safety governance, and the persistent gap between simulation and bedside deployment. Overall, the literature supports a staged translational strategy centered on hierarchical control, conservative safety design, and clinically bounded personalization.