Baicun Wang
Papers
9
Total Citations
593
H-Index
6
About
Dr. Baicun Wang is a leading researcher at the intersection of intelligent manufacturing and human-robot collaboration, with a core focus on developing deep learning and digital twin technologies for human-centric automation. His most influential work, the state-of-the-art review on intelligent welding system technologies (402 citations), has established a foundational roadmap for the field. Dr. Wang’s major contributions lie in advancing human motion perception and prediction for safe, efficient collaboration. He pioneered an attention-based deep learning approach for inertial motion recognition (53 citations) and developed a deep learning-enabled visual-inertial fusion method to overcome occlusion challenges in collaborative assembly (15 citations). His innovative work on the GuLiM hybrid motion mapping technique (32 citations) directly addressed critical needs during the COVID-19 pandemic by enabling teleoperation of medical assistive robots. Dr. Wang is also advancing the concept of Human Motion Digital Twins (27 citations) for human-centric applications. His recent work includes the ATD-GCN framework for activity recognition and a spatio-temporal transformer network for motion prediction, pushing the boundaries of bidirectional human-robot perception. With over 590 total citations, Dr. Wang’s research is shaping the future of intelligent, safe, and collaborative industrial systems.
Research Focus
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