Suwon Shon

Papers

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About

Suwon Shon’s research lies at the intersection of human-robot interaction (HRI) and gesture recognition, with a focus on designing intuitive, flexible interfaces that bridge the gap between human motion and robotic control. His most notable contribution is the introduction of **motion primitives**—statistically derived building blocks of hand movements—to create adaptable gesture sets for HRI systems. By analyzing angular tendencies in sign languages and practical gestures, Shon developed a framework that enables robots to interpret a wider range of user commands without exhaustive training data. This work, published in 2011, has garnered 4 citations and laid groundwork for more natural interaction paradigms. In parallel, his research on a **Hidden Markov Model (HMM)-based hand gesture recognizer** demonstrated a real-time robot control system, where a humanoid robot processes webcam images to extract hand motion descriptors and execute commands. Though early in citation impact, Shon’s contributions are foundational for researchers exploring non-verbal, vision-based interfaces in robotics. His work emphasizes statistical rigor in gesture design, offering a scalable path toward more responsive and user-friendly robotic systems.