Min-Seong Gwon

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About

Min-Seong Gwon is a rising researcher in human-robot interaction and assistive robotics, with a focus on enhancing the safety and responsiveness of industrial exoskeletons. His most-cited work, "Continuous Intention Prediction of Lifting Motions Using EMG-Based CNN-LSTM" (2024, 14 citations), addresses a critical barrier to exoskeleton adoption: control delays caused by data transmission and processing. By integrating electromyographic (EMG) signals with a convolutional neural network-long short-term memory (CNN-LSTM) architecture, Gwon pioneered a method to predict a user’s lifting intentions in real time, enabling smoother, more intuitive human-machine collaboration. This contribution not only reduces latency but also improves the ergonomic and practical utility of exoskeletons in industrial settings. Though early in his career, Gwon’s work has already garnered attention for its potential to bridge the gap between robotic assistance and seamless human motion. His research stands at the intersection of biomechanics, machine learning, and wearable robotics, offering a promising path toward safer, more efficient workplaces. As the field of intelligent exoskeletons evolves, Gwon’s predictive framework represents a foundational step toward truly responsive assistive technologies.