Papers
8
Total Citations
112
H-Index
6
About
Deok-Won Lee is a researcher whose work sits at the dynamic intersection of artificial intelligence, human health monitoring, and assistive robotics. His research focuses primarily on deep learning-based classification systems for health conditions, fall detection, and human-robot interaction, with particular emphasis on improving the quality of life for vulnerable populations including the elderly and children. Lee's most impactful contribution is a novel double-check fall detection method combining IMU sensors and RGB cameras, achieving 100% detection accuracy and earning 42 citations — a milestone in elderly care technology. He has also made significant strides in the automated screening of ADHD in children, developing deep learning frameworks that analyze skeletal and behavioral data captured during robot-led screening games, collectively accumulating over 40 citations across multiple studies. Notably, his work introduced a three-class ADHD-RISK classification category, advancing diagnostic nuance beyond simple binary screening. Beyond health monitoring, Lee has contributed to humanoid robot motion imitation using ZMP stability criteria, soft robotic glove design for high-force grip assistance, and AI-driven medication management via mobile robots. His body of work reflects a consistent commitment to bridging robotics and clinical healthcare, making him a notable figure in socially assistive and intelligent robotic systems research.
Research Focus
Key Achievements
Top Papers
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