Papers
2
Total Citations
11
H-Index
2
About
Hong-In Won is a rising researcher at the intersection of computer vision, deep learning, and intelligent health systems. His work primarily focuses on developing novel sensing and motion-tracking technologies for fitness, rehabilitation, and industrial applications. Won’s most cited paper, "Three-Dimensional Foot Position Estimation Based on Footprint Shadow Image Processing and Deep Learning for Smart Trampoline Fitness System" (2022, 6 citations), addresses the surging demand for home exercise equipment during COVID-19. By combining image processing with deep learning, he created a system that accurately estimates 3D foot positions, enabling safer and more effective muscle strengthening and rehabilitation exercises on smart trampolines. More recently, Won has pushed the boundaries of meta-learning for predictive maintenance. His 2025 paper, "Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems" (5 citations), introduces a novel framework that enhances fault classification in robotic arms on assembly lines. By integrating digital twins and operational grouping, his approach significantly improves the speed and accuracy of predictive maintenance—a critical need in modern manufacturing. Though early in his career, Won’s work demonstrates a clear trajectory toward impactful, application-driven research that bridges human motion analysis and industrial AI.
Research Focus
Key Achievements
Top Papers
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