Papers
9
Total Citations
494
H-Index
8
About
Daekyum Kim is a leading researcher at the intersection of soft robotics, wearable exoskeletons, and human-robot interaction, whose work is redefining how assistive devices perceive and respond to human intent. His most impactful contribution, the highly cited 2021 review of machine learning methods in soft robotics (249 citations), established a foundational framework for addressing the modeling and control challenges inherent in deformable systems. Kim’s core innovation lies in integrating computer vision with wearable robots, as demonstrated in his seminal 2019 work showing that “eyes are faster than hands”—a learning-based system that uses egocentric cameras to detect user intention, bypassing the limitations of traditional biosignals. He has since advanced this vision-driven paradigm across multiple domains: from soft robotic gloves for post-stroke hand rehabilitation (2024) and fingertip force estimation for grasping deformable objects (2020, 28 citations), to exosuit-assisted community walking programs (2023, 27 citations). His 2024 perspective on wearable robots needing vision (24 citations) crystallizes his vision for context-aware, real-world assistive systems. With over 490 total citations and a portfolio spanning EMG-sensor control, artificial mechanoreceptors, and intention detection, Kim is pioneering a future where soft wearable robots seamlessly interpret both user and environment.
Research Focus
Key Achievements
Top Papers
- 1Review of machine learning methods in soft robotics249 citations · 2021
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- 7Wearable robots for the real world need vision24 citations · 2024
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