A Robot-Assisted Remote Rehabilitation System for Ankle Fractures Based on Predictive Force and Full-Cycle Training Strategy

Abstract

The postoperative rehabilitation of ankle fractures, particularly in the home setting, has a crucial influence on the recovery of lower limb function. To enhance the portability, real-time performance, and safety of postoperative remote rehabilitation training, this study proposes a novel robot-assisted remote rehabilitation system tailored for postoperative ankle fracture patients. Based on a distributed system architecture, the hardware system enables modular decomposition and facilitates wireless control of the lower controller. The total weight of the robotic system is 2.634 kg. By combining a deep learning algorithm with an interpolation fitting method, the time delay in interaction force signals during remote communication is predicted and compensated. The control frequency is elevated to 100 Hz with a maximum normalized root mean square error of 10.89%, ensuring the precision and continuity of the robot control system. Additionally, a full-cycle rehabilitation training strategy based on adaptive admittance control with system stiffness identification is proposed, encompassing passive, active–passive, isotonic, and active activities of daily living trainings. Experimental results indicate that the robotic system can execute the training strategies at each phase with high accuracy and safety, and the proposed adaptive control strategy has better compliance than fixed parameter admittance control and fuzzy admittance control methods.