Papers
6
Total Citations
78
H-Index
5
About
Dr. Zidong Yu is a leading researcher in the field of intelligent prosthetics and human motion analysis, with a primary focus on decoding lower limb movement intentions from surface electromyography (sEMG) signals. His work bridges the gap between biosignal processing and adaptive exoskeleton control, aiming to restore mobility for individuals with motor impairments. Dr. Yu’s major contributions include the development of an end-to-end lower limb activity recognition framework that leverages sEMG data augmentation and an enhanced CapsNet architecture, which has garnered 22 citations. He has also pioneered interpretable deep learning models, such as the Dual-branch EMGNet, enabling inter-subject motion intention recognition with 19 citations. His exploration of multimodal information fusion frameworks (18 citations) and continuous movement decoding for adaptive exoskeleton controllers (12 citations) further underscores his impact. Notably, his recent work on temporal-constrained parallel graph neural networks addresses class-imbalanced scenarios in gait phase recognition (6 citations), while his pilot study on stroke patients (1 citation) demonstrates clinical relevance. With over 78 total citations, Dr. Yu’s research is shaping the future of human-robot interaction and rehabilitation engineering.
Research Focus
Key Achievements
Top Papers
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