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About

Xiaoyun Wang is an emerging researcher specializing in neural signal processing, human motion recognition, and intelligent rehabilitation engineering. Her work sits at the intersection of machine learning and biomedical engineering, with a particular focus on decoding lower limb movement intentions from surface electromyography (sEMG) signals to advance exoskeleton and assistive technology design. Wang's most significant contributions center on developing sophisticated deep learning architectures for motion intention recognition. Her interpretable dual-branch EMGNet framework, which garnered 19 citations shortly after publication, introduced transfer learning approaches to address the persistent challenge of inter-subject variability in EMG-based systems. Complementing this, her exploration of multimodal information fusion frameworks (18 citations) has broadened the field's understanding of how combining sensory data streams can enhance recognition accuracy. Her work extends beyond healthy populations — notably examining stroke patient rehabilitation — and increasingly incorporates graph neural networks to handle real-world complexities such as class imbalance and temporal gait dynamics. With over 56 cumulative citations across five recent publications, Wang is rapidly establishing herself as a notable contributor to the development of intelligent, adaptive rehabilitation devices that could meaningfully improve mobility outcomes for individuals with motor impairments.