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About

Hyun-Dae Yang is a leading researcher in rehabilitation robotics and human–robot interaction, with a primary focus on lower limb exoskeleton systems and anthropomorphic manipulation. His most impactful work, "A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots" (2015, 112 citations), introduced a novel neural network approach for precise gait phase classification, enabling more intuitive and responsive exoskeleton control for users with mobility impairments. This contribution is critical for advancing intent detection and real-time adaptation in assistive devices. Yang also contributed foundational biomechanical insights in "Brief biomechanical analysis on the walking of spinal cord injury patients with a lower limb exoskeleton robot" (2013, 14 citations), analyzing gait patterns to improve rehabilitation outcomes. Earlier in his career, he developed a 16-degree-of-freedom anthropomorphic robot hand with back-drivability for stable grasping (2011), demonstrating his versatility across both upper and lower limb robotic systems. Yang’s work bridges neural networks, biomechanics, and mechanical design, directly impacting the development of intelligent, user-aware exoskeletons and prosthetic devices. His research continues to shape how robotic systems interpret human movement and provide meaningful assistance.