Papers
3
Total Citations
14
H-Index
3
About
YoungWoo Kim is a researcher dedicated to advancing the field of robot-assisted rehabilitation and human-robot interaction. His work centers on developing intelligent control systems and training methodologies to improve motor recovery for individuals with upper and lower limb impairments. A key contribution is his adaptive impedance control framework, which enables robots to dynamically adjust their stiffness and viscosity for precise motion and force tracking during physical human-robot interaction—a foundational approach for safe and effective rehabilitation. Kim has also investigated the neural principles of bilateral movement training, exploring the "human mirror-image" effect to design robotic force fields that enhance coordination between limbs during therapy. Most recently, his 2024 work introduces a dynamic simulation framework for a Robot-Assisted Training Platform (RATP), integrating genetic algorithms with inverse dynamics to personalize gait rehabilitation. With over 14 citations across his most prominent papers, Kim’s research bridges control theory, biomechanics, and clinical application, offering practical tools for tailoring robotic therapy to individual patient needs.
Research Focus
Key Achievements
Top Papers
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