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About

Qiuzhi Song is a researcher whose work spans rehabilitation robotics, wearable exoskeleton systems, and human biomechanics. His research has made meaningful contributions to the development of intelligent assistive technologies, with a particular focus on locomotion mode recognition, gait analysis, and motor function assessment for clinical and real-world applications. Among his most impactful contributions, Song has advanced exoskeleton control systems through novel machine learning approaches, including his FSM-HSVM locomotion recognition algorithm and an improved neighborhood rough set model for sensor screening — work that streamlines multi-sensor fusion for more efficient, reliable human-robot interaction. His 2020 study on quantitative motor function assessment using rehabilitation robots (14 citations) bridges clinical need with technological innovation, offering practical tools for stroke patient evaluation. His investigation into backpack load effects during slope walking (23 citations) highlights his grounding in applied biomechanics, with direct relevance to injury prevention for soldiers and hikers. Song has also explored continuous joint torque prediction using sEMG signals and LSTM-based deep learning, advancing human-machine cooperative control. Earlier work on negative pressure wall-climbing robots demonstrates a broader engineering curiosity. Collectively, his publications reflect a productive and interdisciplinary research trajectory at the intersection of robotics, biomechanics, and rehabilitation engineering.