Kooksung Jun
Papers
6
Total Citations
97
H-Index
4
About
Kooksung Jun is a researcher specializing in artificial intelligence-driven healthcare technologies, with particular focus on human activity recognition, pediatric neurological screening, and assistive robotics for elderly care. His work sits at the intersection of deep learning, computer vision, and clinical application, tackling real-world medical challenges with innovative sensor fusion approaches. Jun's most influential contribution is his double-check fall detection system (2021, 42 citations), which combines IMU sensor data with RGB camera input through a deep neural network architecture to achieve near-perfect detection accuracy — directly addressing the persistent problem of false alarms and missed detections in elderly monitoring systems. Equally notable is his pioneering work on ADHD screening in children, where he developed deep learning classifiers using skeleton data and behavioral recognition during robot-led screening games, achieving nuanced classification across ADHD, ADHD-risk, and normal categories — a clinically meaningful distinction largely overlooked in prior research (26 and 16 citations respectively). Beyond these contributions, Jun has advanced mobile robotics for medication management and pathological gait recognition using ultrawideband localization, demonstrating a consistent commitment to deployable, AI-powered solutions for vulnerable populations. His growing citation record reflects the practical significance and originality of his research across both academic and clinical communities.
Research Focus
Key Achievements
Top Papers
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