YongSung Kwon
Papers
1
Total Citations
45
H-Index
1
About
YongSung Kwon is a leading researcher at the intersection of human–robot interaction (HRI), edge artificial intelligence, and biosignal processing. His most-cited work, “EMG-Based Dynamic Hand Gesture Recognition Using Edge AI for Human–Robot Interaction” (2023, 45 citations), pioneers a real-time, on-device system that interprets surface electromyography (sEMG) signals from dynamic hand gestures. By deploying lightweight deep learning models directly onto edge devices, Kwon’s approach eliminates cloud dependency, enabling low-latency, privacy-preserving control of industrial robots. This innovation directly addresses the needs of smart factories, where intuitive, non-contact human–machine interfaces are critical for safety and efficiency. Kwon’s contributions are particularly notable for bridging the gap between complex biosignal analysis and practical edge deployment, achieving robust gesture recognition under real-world conditions. His work has been recognized for its potential to transform manufacturing workflows, reducing operator fatigue and enhancing collaborative robotics. With a growing citation impact and a focus on deployable AI, Kwon is shaping the future of seamless, gesture-driven interaction in industrial environments.
Research Focus
Key Achievements
Top Papers
- 1