Quantum Driven Dynamic Passivity-Based Neuromechanical Control for Wrist Rehabilitation Robot
Khan N.A. Hussain F. Goyal T. Jamwal P.K. Hussain S.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Medical Robotics and Bionics
2025#7Issue 31237 - 1247 pp.
Robotic-assisted rehabilitation for wrist movements demands adaptive systems capable of balancing patient autonomy with robotic support. The integration of artificial intelligence (AI) into robotic-assisted rehabilitation offers transformative potential in delivering personalized, dynamic, and effective therapeutic interventions. This study introduces a novel neuromechanical control framework integrating a passivity observer with Quantum-Enhanced Deep Reinforcement Learning (QDRL) for adaptive impedance scaling in wrist rehabilitation robotics. The passivity observer continuously monitors energy exchanges to classify patient states into passive (patient requiring robotic assistance) and non-passive (patient actively participating) categories, dynamically guiding the robot’s impedance adjustments. Experiments were conducted with ten unimpaired human subjects (eight male and two female), who were instructed to simulate rehabilitation scenarios, focusing on three key wrist movements, flexion/extension (FL/EX), abduction/adduction (AB/AD), and pronation/supination (PR/SU). Experimental results showed high correlations (> 0.83) between energy-based and electromyography (EMG)-based passivity classifications, confirming the reliability of the proposed approach. Furthermore, the designed QDRL model significantly outperformed traditional reinforcement learning methods, achieving superior adaptability, stability, and higher average rewards during robotic impedance control. The framework offers advancement in optimizing robotic assistance during motor recovery, promoting personalized rehabilitation by tailoring interventions to the specific needs of each patient.
adaptive learning , energy transfer , Neuromechanical control , passivity observer , physical human-robot interaction , quantum computing
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University of Canberra, School of Information Technology and Systems, Canberra, 2617, ACT, Australia
Nazarbayev University, Department of Electrical and Computer Engineering, Astana, 010000, Kazakhstan
University of Canberra
Nazarbayev University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026