Papers
2
Total Citations
33
H-Index
2
About
Jungwook Mun is a rising researcher at the intersection of soft robotics, rehabilitation engineering, and human-machine interaction. His work centers on developing intelligent assistive systems that restore hand function for stroke survivors and enhance the dexterity of soft robotic grippers. Mun’s most-cited paper, “Multiple Hand Posture Rehabilitation System Using Vision-Based Intention Detection and Soft-Robotic Glove” (2024, 29 citations), introduces a novel approach that uses computer vision to detect a user’s intended hand posture, then actuates a soft-robotic glove to facilitate active rehabilitation—eliminating the need for cumbersome biosignal sensors. This work directly addresses the critical challenge of enabling stroke survivors to perform activities of daily living. In parallel, his research on “Impact of Physical Parameters and Vision Data on Deep Learning-Based Grip Force Estimation for Fluidic Origami Soft Grippers” (2024, 4 citations) tackles the problem of sensing grip force without compromising the inherent adaptability of soft grippers. By leveraging vision data and physical parameters, Mun’s deep learning model enables precise, sensor-free force estimation, a key advance for handling fragile objects. His contributions are paving the way for more intuitive, sensorless, and adaptive robotic systems in both rehabilitation and industrial manipulation.
Research Focus
Key Achievements
Top Papers
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