Reinforcement Learning-Driven Path Generation for Ankle Rehabilitation Robot Using Musculoskeletal-Informed Energy Optimization

Abstract

In rehabilitation robotics, optimizing energy consumption and high interaction forces is essential to prevent unnecessary muscle fatigue and excessive joint loading as they often cause an inefficient trajectory planning and disrupt natural movement patterns. Stroke patients frequently exhibit asymmetrical muscle activation and impaired neuromuscular coordination, making it necessary to design a system that adapts to their specific motor limitations with energy-efficient and excessive torque control. This study presents a reinforcement learning-based trajectory optimization framework for a 3-DOF ankle rehabilitation robot, integrating musculoskeletal modeling, transactive energy and real-time physiological feedback to generate adaptive rehabilitation trajectories. The methodology utilizes electromyography (EMG) signals from key ankle muscles and joint reaction forces to refine movement patterns to ensure biomechanical efficiency. The methodology is validated using data from ten stroke patients, demonstrating its potential to enhance rehabilitation effectiveness by promoting more natural, efficient, and physiologically accurate movement trajectories.