Xiao Li Yang

University of Electronic Science and Technology of China, Tianjin Medical University, Universidad de León

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Total Citations

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About

Xiao Li Yang is a pioneering researcher at the intersection of neural engineering and rehabilitation robotics, whose work focuses on developing intuitive human-exoskeleton interfaces for movement assistance and recovery. His primary research areas include multimodal biological signal processing—particularly electroencephalography (EEG) and surface electromyography (sEMG)—and deep learning architectures for real-time limb movement prediction. Yang’s major contributions center on overcoming the unreliability of single-signal interfaces by fusing EEG and sEMG data, as demonstrated in his most-cited 2022 work (14 citations), which proposed a novel multimodal interface for rehabilitation training. He also introduced MCSNet (2021, 11 citations), a channel synergy-based framework that leverages sEMG to enhance exoskeleton responsiveness, and developed a convolutional neural network that integrates hand-crafted features with learned representations (2022, 9 citations) for improved lower-limb prediction in hemiplegic patients. Beyond rehabilitation, Yang has expanded into precision livestock farming, contributing a dataset for herding and predator detection using robots (2024, 7 citations). His work has been instrumental in advancing natural human-robot interaction, with cumulative citations reflecting growing impact in assistive technology and autonomous systems.