Byeong-Hyeon Lee

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About

Byeong-Hyeon Lee is a researcher specializing in human motion analysis, wearable sensing technologies, and intelligent control systems for rehabilitation and assistive robotics. His work centers on developing sophisticated methods for gait analysis, with a particular focus on leveraging biosignals to enhance the performance of lower-limb power-assist robots. Lee's most recognized contribution, his 2019 paper on sEMG signal and IMU sensor-based gait sub-phase detection and prediction, has garnered 35 citations and represents a significant advancement in user-adaptive machine learning classifiers capable of accurately identifying and anticipating discrete phases of human gait. By integrating surface electromyogram signals, inertial measurement units, and pressure sensors, Lee addressed longstanding challenges in pattern recognition reliability that have historically limited the practical deployment of assistive exoskeletons. His earlier 2016 work introduced a novel gait phase recognition framework combining a GPES library with an integrated spectral matching filter, laying important groundwork for his subsequent research. Collectively, Lee's contributions advance the intersection of biomedical signal processing and robotic assistance, offering meaningful progress toward more intuitive and responsive devices for individuals with mobility impairments.