Eun Yi Kim

Papers

Total Citations

H-Index

About

Eun Yi Kim is a researcher whose work centers on human-robot interaction, computer vision, and gesture-based control systems. Her major contributions lie in developing intuitive interfaces that allow humans to command robots through natural hand movements and gestures, bridging the gap between human intent and machine action. Her most cited paper, "HMM-Based Gesture Recognition for Robot Control" (2005), has garnered 31 citations and demonstrates her pioneering use of Hidden Markov Models to interpret dynamic gestures for robot guidance. In related work, she explored "Robot Competition Using Gesture Based Interface" (2005, 9 citations) and "Mobile robot control using hand-shape recognition" (2008, 7 citations), where she advanced vision-based control by employing active contour models with mean shift tracking to accurately segment hand shapes from moving camera feeds. This technical achievement enabled robust, real-time control of walking robots. Kim’s research is notable for its practical focus on making robot control more accessible and natural, with applications in assistive technology and autonomous systems. Her work continues to influence the development of non-invasive, vision-driven interfaces for robotics.