Papers
1
Total Citations
2
H-Index
1
About
Duo He is a researcher focused on advancing human action recognition, particularly through the analysis of skeletal data. His key research areas include intelligent monitoring, human–computer interaction, and robotics, where he develops innovative methods to improve machine understanding of complex human movements. He is best known for his work on the NST-GCN (Non-local Spatio-Temporal Graph Convolutional Network), a framework that enhances hand action recognition by capturing global correlations in skeletal data. This approach effectively mitigates common challenges such as background noise and variations in movement speed, which often degrade performance in traditional vision-based systems. His most-cited paper, "Global Correlation Enhanced Hand Action Recognition Based on NST-GCN" (2022), has garnered 2 citations, reflecting its emerging influence in the field. By focusing on skeleton-based recognition, He's contributions offer a robust alternative to conventional methods, paving the way for more reliable and efficient human–computer interaction systems. His work is particularly valuable for applications requiring precise gesture interpretation in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1Global Correlation Enhanced Hand Action Recognition Based on NST-GCN2 citations · 2022