Research progress on elbow joint rehabilitation robots

Elbow orthoses offer a straightforward mechanical approach for rehabili-tation following elbow joint injuries. The convergence of artificial intelligence with these orthoses has led to the development of elbow rehabilitation robots, designed to address the personalized rehabilitation requirements of patients with diverse injury profiles. A typical elbow rehabilitation robot system comprises four core components: rigid mechanical structure, an actuation system, bionic sensors, and integrated software. The rigid structure, analogous to the human skeletal system, includes linkage mechanisms and gear transmission assemblies. The actuation system, mimicking the function of muscles and ligaments, generates and modulates the necessary forces and torques for movement, employing actuators such as pneumatic, elastic and cable-driven types. Bionic sensors, serving as the robot’s perceptual interface, encompass photoelectric encoders, force/torque sensors, electromyo-graphic (EMG) signal sensors, and temperature sensors. The software system, encompassing control algorithms and machine learning models, functions as the “neural center,” enables intelligent decision-making and motion control. The core technological achievement lies in the seamless integration of hardware and software to enable precise tracking of elbow joint kinematics and adaptive modulation of assistive forces based on real-time human-robot interaction. This integration supports multiple training modalities, including passive, assistive, active, and resistive modes and enables safe, personalized, and intelligent rehabilitation support across different recovery phases. By harnessing technologies like bio-inspired design, precise impedance control, EMG-based assistance, and virtual reality-integrated task training, these robotic systems improve training comfort, assistance accuracy, and patient adherence. This review outlines the current state of elbow rehabilitation robotics, details the key system components and primary training modalities, discusses clinical needs and future development trends, and aims to offer insights for the further refinement of rehabilitation robotic systems.