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About

Yongje Kwon is a robotics and human-machine interface researcher whose work sits at the intersection of electromyography (EMG), teleoperation, and dexterous robotic manipulation. His research focuses on developing intuitive, wearable control systems that translate human muscular signals into meaningful robotic actions — a critical challenge in prosthetics, rehabilitation, and remote robot control. Kwon's most influential contribution, "A Learning Scheme for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions" (2019, 53 citations), established a robust framework for decoding fine-grained hand and finger movements from surface EMG signals, pushing the boundaries of what myoelectric interfaces can achieve beyond simple grasp commands. Building on this foundation, he extended his work into telemanipulation, combining EMG with fiducial marker tracking and compliance control to create shared-control frameworks that make robot teleoperation more natural and accessible. His 2021 comparative study between real-world and virtual reality environments further demonstrated the versatility and transferability of EMG-based decoding across platforms. Collectively accumulating over 117 citations, Kwon's research makes meaningful strides toward seamless human-robot collaboration, with direct applications in assistive technology, surgical robotics, and immersive human-computer interaction. His work represents a compelling bridge between neuromuscular sensing and intelligent robotic systems.