Jungpil Shin
Papers
7
Total Citations
117
H-Index
6
About
Jungpil Shin is a prominent researcher specializing in human-computer interaction, gesture recognition, and biosignal processing, with a particular focus on developing intuitive, non-touch interfaces for next-generation computing systems. His work spans surface electromyography (sEMG)-based hand gesture recognition, virtual keyboard input systems, human activity recognition, and human-robot interaction — areas where he has made consistently impactful contributions. Shin's most celebrated work includes a multi-stream time-varying feature enhancement approach for sEMG-based hand gesture recognition (2024, 25 citations), which addresses longstanding challenges in deploying muscle-computer interfaces. His 2020 research on non-touch character writing systems using hand gesture recognition (24 citations) and virtual keyboard input systems (19 citations) demonstrated practical, user-centered applications of gesture technology in everyday communication. His 2024 sensor-based human activity recognition framework employing ECA-Net dimensionality reduction (18 citations) further highlights his versatility in tackling complex classification problems across medical and HCI domains. His 2022 work on 3D gesture-driven human-robot interaction (15 citations) underscores his commitment to advancing safe, adaptive robotic systems. Collectively accumulating over 117 citations, Shin's body of work reflects a sustained dedication to bridging advanced deep learning methodologies with real-world accessibility, making technology more intuitive and inclusive for diverse users.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Hand Movement Activity-Based Character Input System on a Virtual Keyboard19 citations · 2020
- 4
- 53D Gesture Recognition and Adaptation for Human–Robot Interaction15 citations · 2022
- 6Gestural flick input-based non-touch interface for character input11 citations · 2019
- 7