Dongjune Chang
Papers
5
Total Citations
56
H-Index
5
About
Dongjune Chang is a leading researcher at the intersection of soft robotics, haptics, and human-robot interaction, with a focus on creating safer, more intuitive robotic systems. His work spans two critical domains: developing robotic "skin" for force sensing and designing intelligent exoskeletons for human augmentation. In his highly cited 2024 study on fiber-optic force sensing for modular robotic skin, Chang pioneered a method that mimics biological mechanoreception, enabling robots to perform dexterous manipulation in hazardous environments (20 citations). His 2022 work on user-adaptive variable damping control, also garnering 20 citations, introduced a Bayesian optimization framework that dynamically adjusts robot behavior to individual users, significantly improving stability and reducing effort during physical human-robot interaction. Chang has also made foundational contributions to shoulder biomechanics, validating a novel parallel-actuated exoskeleton for characterizing shoulder impedance and uncovering sex-based differences in joint stiffness. His 2024 design of a wearable shoulder exoskeleton with dual-purpose gravity compensation and misalignment compensation represents a major step toward preventing musculoskeletal disorders in industrial workers. With over 56 total citations and a growing portfolio of high-impact publications, Chang is shaping the future of assistive robotics and human-centered automation.
Research Focus
Key Achievements
Top Papers
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