Reinforcement Learning-Driven Path Generation for Ankle Rehabilitation Robot Using Musculoskeletal-Informed Energy Optimization
Khan N.A. Jamwal P.K. Hussain F. Ghayesh M.H. Hussain S.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Neural Systems and Rehabilitation Engineering
2025#331774 - 1784 pp.
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.
adaptive learning , energy transfer , human-robot interaction , Musculoskeletal modeling , neurorehabilitation , reinforcement learning , transactive energy
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University of Canberra, School of Information Technology and Systems, ACT, Canberra, 2617, Australia
Nazarbayev University, Department of Electrical and Computer Engineering, Astana, 010000, Kazakhstan
The University of Adelaide, School of Electrical and Mechanical Engineering, Adelaide, 5005, Australia
University of Canberra
Nazarbayev University
The University of Adelaide
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026