Papers
5
Total Citations
35
H-Index
3
About
Hyungmin Kim is a leading researcher at the intersection of brain-computer interfaces (BCIs) and rehabilitation robotics, with a focus on decoding human movement intention from neural and physiological signals. His core contributions lie in developing advanced machine learning frameworks—such as spatio-spectral convolutional neural networks—to classify gait states and movement intentions from electroencephalography (EEG), enabling intuitive control of lower limb exoskeletons. Notably, his work on hybrid BCI paradigms combining Motor Imagery and Steady-State Somatosensory Evoked Potentials (SSSEP) has advanced the decoding of left/right movement and sit-to-stand transitions, addressing the critical trade-off between accuracy and responsiveness. Beyond EEG, Kim has explored electromyogram-based models to predict walking speed in both nondisabled and post-stroke hemiparetic patients, directly informing gait rehabilitation robot control. His most-cited paper, with 17 citations, demonstrates the practical impact of his research. Additionally, his work extends to robotic bin picking, analyzing factors affecting 3D point cloud registration for industrial automation. Kim’s interdisciplinary approach—bridging neural decoding, robotics, and clinical application—positions him as a key innovator in assistive and rehabilitative technologies.
Research Focus
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