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About

Ziming Guo is a robotics and biomedical engineering researcher whose work sits at the intersection of human-machine interaction, rehabilitation technology, and intelligent control systems. His research focuses primarily on lower limb exoskeleton robots, with a particular emphasis on leveraging surface electromyography (sEMG) signals to create more intuitive, responsive, and stable assistive devices for individuals with lower limb dysfunction and paraplegia. Guo's most significant contributions center on developing real-time control frameworks that translate a wearer's muscular intent into precise exoskeleton movements. His 2021 paper on stability control through sEMG interfaces (17 citations) and his 2019 work on active sEMG-based control (16 citations) demonstrate his consistent drive to bridge the gap between commercial rehabilitation robotics and genuine user-responsive functionality. Notably, his application of Long Short-Term Memory neural networks for gait phase classification reflects his commitment to integrating modern deep learning techniques into rehabilitation engineering. Across his body of work, accumulating over 60 citations, Guo has advanced the field's understanding of gait switching strategies, stability planning, and intelligent motion intention recognition — contributions that hold meaningful promise for improving the quality of life of patients undergoing motor rehabilitation.