Sang Su Jang

Papers

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About

Sang Su Jang is a researcher in human-robot interaction and gesture-based control systems, with a focus on developing intuitive interfaces for robotic systems. His most-cited work, "HMM-Based Gesture Recognition for Robot Control" (2005), has garnered 31 citations and represents a foundational contribution to the field of gesture recognition using Hidden Markov Models (HMMs). This research demonstrated how dynamic hand gestures could be reliably interpreted to control robots in real-time, bridging the gap between human intent and machine action. Jang's work is notable for its practical application in assistive robotics and industrial automation, where natural user interfaces are critical. By integrating machine learning techniques with robotics, he has advanced the understanding of how non-verbal commands can enhance human-robot collaboration. His contributions have influenced subsequent studies in gesture-based control, particularly in the development of more robust and adaptive recognition algorithms. For students and researchers exploring the intersection of pattern recognition and robotics, Jang's work offers a clear example of how HMMs can be leveraged for real-world control tasks, making him a key figure in the evolution of intuitive robotic interfaces.