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About

Naveed Ahmad Khan is at the forefront of integrating artificial intelligence with rehabilitation robotics, pioneering intelligent systems that restore human movement. His research focuses on three key areas: deep learning for robotic control, energy-optimized path planning, and human-robot interaction safety. Khan’s major contributions include developing a deep learning-driven analysis of a six-bar mechanism for personalized gait rehabilitation (17 citations), which demonstrates how computational intelligence can enhance robotic exoskeleton adaptability. He also introduced a transformer-based approach for predicting transactive energy in neurorehabilitation (8 citations), addressing the critical challenge of safe energy transfer during physical human-robot interaction. His reinforcement learning framework for ankle rehabilitation robots (8 citations) uses musculoskeletal-informed energy optimization to prevent muscle fatigue and joint loading in stroke patients. Khan has further advanced inverse kinematics solutions for upper limb rehabilitation robots using deep learning models (6 citations), solving a traditionally intractable problem. His recent work on quantum-driven neuromechanical control for wrist rehabilitation (2 citations) represents a novel frontier in adaptive robotic assistance. With a rapidly growing citation impact and publications spanning 2024-2025, Khan is establishing himself as a transformative researcher in AI-powered rehabilitation engineering.