Papers
7
Total Citations
132
H-Index
4
About
Seulki Kyeong is a leading researcher in human-robot interaction, specializing in the use of surface electromyography (sEMG) for intuitive control of lower-limb exoskeletons. Her work focuses on solving the fundamental challenge of recognizing and predicting a wearer’s motion intentions before movement occurs. Kyeong’s major contributions include developing methods to fuse sEMG with mechanical sensors to accurately predict walking intentions across diverse environments, as detailed in her highly cited 2019 paper (70 citations). She has also systematically addressed the practical implementation issues of EMG-based intention detection, such as electrode location variation and signal noise, proposing enhanced torque estimation techniques. Her research demonstrates that sEMG can provide a crucial early cue for exoskeleton control, enabling more natural and responsive human-robot collaboration. With over 130 total citations, Kyeong’s work is foundational for advancing assistive and rehabilitative robotics. Notably, she has also explored vision-based aided grasping for teleoperation, broadening her impact on robotic assistance. Her systematic approach to bridging biological signals and mechanical systems makes her a key figure in the future of wearable robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7