Yongxuan Tan
Papers
3
Total Citations
62
H-Index
3
About
Yongxuan Tan is a pioneering researcher at the intersection of robotics, haptics, and medical simulation, whose work is transforming how medical students learn to interpret patient pain. His primary research areas include medical training simulators, facial expression rendering, and soft tissue characterization. Tan’s major contribution lies in developing a robotic patient that can dynamically simulate facial expressions of pain in response to visuo-haptic interactions—a breakthrough that bridges the gap between physical palpation and realistic patient feedback. His highly cited 2022 paper on this topic (32 citations) demonstrates the impact of his work, while his 2020 review (26 citations) established the current state and future directions for facial expression rendering in medical simulators. Notably, Tan has also innovated in medical percussion, using a novel robotic device combined with acoustic analysis and neural networks to objectively characterize soft tissues—a centuries-old manual technique now made quantitative. His research is essential reading for anyone interested in human-robot interaction, affective computing, or the future of medical education.
Research Focus
Key Achievements
Top Papers
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