Jae Hoon Lee
Papers
7
Total Citations
120
H-Index
4
About
Jae Hoon Lee is a robotics and human-machine interaction researcher whose work spans mobile robotics, human activity recognition, and biomechanical sensing. With a career bridging foundational mechanical systems and cutting-edge deep learning applications, Lee has made notable contributions across several interconnected domains. His early work established important principles in mobile robot design, most notably his 2003 study on optimal design and actuator sizing for omni-directional mobile robots (44 citations), which addressed a significant gap in the field. He further advanced autonomous systems through marathon runner tracking algorithms enabling high-speed human following in unstructured outdoor environments. More recently, Lee has pivoted toward intelligent sensing and human-assistive technologies. His 2021 paper on sensor data structuring for human activity recognition (50 citations) introduced novel feature extraction approaches with direct implications for healthcare and human-robot collaboration. Complementing this, his deep learning frameworks for estimating leg joint angles from a single inertial sensor and enabling biped robot locomotion reflect a sophisticated integration of biomechanics and artificial intelligence. Collectively, Lee's research demonstrates a sustained commitment to making robots more responsive to human movement — an increasingly vital pursuit as assistive technologies and collaborative robotics continue to reshape healthcare and industry.
Research Focus
Key Achievements
Top Papers
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- 3Marathoner tracking algorithms for a high speed mobile robot11 citations · 2011
- 4Robot motion generation considering external and internal impulses5 citations · 2005
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