Papers
15
Total Citations
223
H-Index
9
About
Muhammad Nasir Khan is a prolific researcher specializing in telepresence robotics, human-robot interaction, and intelligent control systems, with a particular focus on healthcare applications. His work has gained significant momentum in the post-COVID-19 era, where the demand for remote interaction technologies surged dramatically. Khan's most influential contributions center on solving one of telepresence robotics' most persistent challenges: communication time delay. By pioneering the application of deep reinforcement learning (DRL) algorithms to predict teleoperator behavior and compensate for latency, he has substantially advanced the reliability and responsiveness of remotely operated robots in clinical settings. His 2023 paper on DRL-assisted delay compensation in IoT-enabled healthcare environments alone has garnered 42 citations, reflecting strong community recognition. Beyond delay mitigation, Khan has extended his research to obstacle avoidance, orientation control, and telehealth-enabled rehabilitation for brachial plexus injury patients, demonstrating impressive breadth. His more recent work exploring telepresence robots in higher education governance and gamification signals an expanding interdisciplinary vision. With a cumulative citation count exceeding 190 across his top publications, Khan's research is shaping how intelligent robotic systems bridge physical distances in healthcare, education, and industry.
Research Focus
Key Achievements
Top Papers
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- 7Telepresence Robots and Controlling Techniques in Healthcare System14 citations · 2022
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