Papers
17
Total Citations
321
H-Index
7
About
Mun Sang Kim is a multidisciplinary robotics and AI researcher whose work spans human-robot interaction, cognitive neuroscience, and assistive technology. Best known for his pioneering investigations into robot-assisted cognitive training, Kim's landmark 2015 study — garnering 98 citations — demonstrated measurable structural brain changes in elderly participants following multi-domain cognitive training, bridging robotics with neuroscience in a clinically significant way. His early contributions to hazardous environment robotics, particularly his double-track mobile robot system (79 citations), established him as an innovator in autonomous vehicle design for real-world applications including firefighting and mine detection. In recent years, Kim has directed his expertise toward deep learning-based clinical screening tools, developing novel AI systems for fall detection in elderly populations and ADHD classification in children using skeletal motion data, RGB-D sensors, and robot-led screening games. These contributions are particularly noteworthy for their potential to enable early, objective diagnosis of a disorder increasingly prevalent worldwide. Across his career, Kim has consistently sought to deploy robotics and intelligent systems at the intersection of human health and safety, accumulating over 290 citations and demonstrating sustained impact across engineering, geriatric care, and pediatric neurodevelopmental research.
Research Focus
Key Achievements
Top Papers
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- 2Double-track mobile robot for hazardous environment applications79 citations · 2003
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- 6A Kalman filter based visual tracking algorithm for an object moving in 3D13 citations · 2002
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