Sung Won Han
Papers
1
Total Citations
12
H-Index
1
About
Sung Won Han is a leading researcher at the intersection of surgical robotics and biomedical signal processing, with a primary focus on enhancing human-robot interaction in microsurgery. His most impactful work centers on developing advanced frameworks for predicting voluntary hand motion while actively suppressing involuntary tremor—a critical challenge in robot-assisted microsurgery. In his highly cited 2020 paper, Han introduced a novel decomposition-and-ensemble framework combined with deep neural networks, achieving remarkable accuracy in distinguishing intended movements from pathological tremor. This work, which has garnered 12 citations, represents a significant breakthrough in seamlessly translating surgeon intent into precise robotic actions. Han's contributions are particularly notable for bridging signal decomposition techniques with modern deep learning architectures, offering a practical solution to one of the most persistent obstacles in microsurgical robotics. His research not only advances the theoretical understanding of motion prediction but also has direct clinical implications, promising to improve surgical precision and patient outcomes in delicate procedures requiring sub-millimeter accuracy.
Research Focus
Key Achievements
Top Papers
- 1