Chang‐Sik Son

Daegu Gyeongbuk Institute of Science and Technology

Papers

Total Citations

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About

Chang-Sik Son is a researcher whose work sits at the intersection of robotics, artificial intelligence, and sensor systems. His primary research areas include human locomotion recognition, wearable exoskeleton control, and mobile object tracking. Son’s most notable contribution is a 2023 study introducing a novel multivariate convolutional neural network (CNN) architecture—encompassing both single and multi-head designs—to identify user locomotion activity during operation of a wearable lower limb exoskeleton. This work, involving 500 healthy adult participants, represents a significant step toward intuitive, adaptive human-robot interaction. Beyond this, Son has contributed to robust navigation through his work on stable path planning algorithms that effectively handle dynamic obstacles, addressing a critical limitation in real-world and in-body environments. He has also explored passive UHF RFID systems for tag-interference-based mobile object tracking. With his highest-cited paper accumulating 7 citations and additional works receiving 4 citations each, Son’s research continues to influence the development of safer, more responsive robotic systems and intelligent sensing technologies.