Papers
14
Total Citations
822
H-Index
11
About
Inseung Kang is a leading researcher in wearable robotics and human-robot interaction, with a particular focus on robotic exoskeletons for lower-limb assistance and rehabilitation. His work sits at the intersection of biomechanics, machine learning, and control systems engineering, advancing how exoskeletons sense, adapt to, and augment human movement in real-world conditions. Kang's most influential contributions center on developing intelligent control frameworks for hip and knee exoskeletons. His pioneering neural network-based gait phase estimators — each accumulating over 150 citations — enable exoskeletons to continuously and accurately track a user's movement cycle across diverse locomotion modes, a critical challenge for practical deployment. His investigations into optimal hip assistance levels (131 citations) have deepened our fundamental understanding of human-exoskeleton energetic interaction. More recently, his unified control framework leveraging estimated joint moments (104 citations) represents a significant leap toward generalizable, autonomous exoskeleton assistance that reduces the burden of context-specific tuning. Across his body of work, Kang has consistently employed electromyography, deep learning, and series elastic actuation to push exoskeleton technology closer to seamless real-world use. With hundreds of citations accumulated in just a few years, his research is rapidly shaping the future of assistive and augmentative robotics.
Research Focus
Key Achievements
Top Papers
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- 4Estimating human joint moments unifies exoskeleton control, reducing user effort104 citations · 2024
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- 10Biological Hip Torque Estimation using a Robotic Hip Exoskeleton19 citations · 2020