Papers
1
Total Citations
3
H-Index
1
About
Se-Jin Kim is a rising researcher in human-robot interaction (HRI), with a focus on deep learning and biomechanical sensing. His work centers on enabling safer, more intuitive collaboration between humans and robots, particularly through the estimation of human intent. His most cited paper, "sEMG-based Static Force Estimation for Human-Robot Interaction using Deep Learning" (2020), addresses a critical challenge in HRI: accurately inferring a user's intended force from surface electromyography (sEMG) signals. By applying deep learning to this problem, Kim provides a method to bypass the difficulty of calculating exact motion trajectories, instead relying on muscle activity to guide interaction control. This contribution is foundational for applications in human-robot collaboration, power augmentation, and rehabilitation robotics. Though early in his career, with his top paper garnering 3 citations, Kim's work signals a promising trajectory in merging neural networks with physical human-robot systems, offering a pathway toward more responsive and adaptive robotic assistants.
Research Focus
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Top Papers
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