Papers
3
Total Citations
9
H-Index
2
About
Sang Hun Chung is a researcher dedicated to advancing lower-limb rehabilitation robotics through intuitive, patient-driven control. His work centers on the recognition of human movement intention using surface electromyography (EMG), with a particular focus on restoring natural gait and functional mobility in post-stroke hemiparetic patients. Chung’s major contributions lie in developing practical methods to predict a user’s intended walking speed and transitional movements—such as sit-to-stand and stand-to-sit—solely from EMG signals. His 2020 study on predicting gait speed via soleus EMG signals (4 citations) and his 2017 work on walking speed intention models (3 citations) demonstrate a clear trajectory toward enabling real-time, patient-driven control of robotic exoskeletons. Notably, his 2015 paper introduced a novel framework using linear discriminant analysis and a multi-window feature extraction technique to classify sit-to-stand transitions from just two EMG channels. Though early in his citation impact, Chung’s research addresses a critical bottleneck in rehabilitation robotics: the lack of patient effort during therapy. By decoding motor intent before movement occurs, his work promises to make robotic gait training more engaging, adaptive, and effective for stroke survivors.
Research Focus
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Top Papers
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