Changmook Chun
Papers
8
Total Citations
128
H-Index
5
About
Changmook Chun is a leading researcher in robotic gait rehabilitation, sensor fusion, and nonlinear filtering on Lie groups. His most influential work, "Particle filtering on the Euclidean group: framework and applications" (64 citations), pioneered a coordinate-invariant particle filter for simultaneous state and covariance estimation on SE(3), providing a rigorous mathematical foundation for filtering in robotic systems. Chun has made transformative contributions to personalized rehabilitation robotics, developing Gaussian process-based methods that learn and synthesize individualized gait trajectories at arbitrary walking speeds. His 2019 paper on Gaussian process trajectory learning (37 citations) introduced a data-driven approach to generate collision-free, patient-specific motions for assist-as-needed therapy. He further advanced the field by creating statistical models to predict personalized pelvic motion from body meta-features, critical for balance training in rehabilitation robots. Chun's work on sensor fusion for line detection and autonomous urban navigation demonstrates his versatility, applying extended Kalman filters to enable reliable robot navigation in GPS-denied environments. His research uniquely bridges theoretical filtering on Lie groups with practical rehabilitation applications, achieving over 120 total citations and establishing foundational methods for next-generation robotic gait training systems.
Research Focus
Key Achievements
Top Papers
- 1Particle filtering on the Euclidean group: framework and applications64 citations · 2007
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- 4Particle Filtering on the Euclidean Group5 citations · 2007
- 5Sensor fusion-based line detection for unmanned navigation5 citations · 2010
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- 8Autonomous urban navigation and its application to patrol3 citations · 2010