Xiaoyue Tan
Papers
1
Total Citations
4
H-Index
1
About
Xiaoyue Tan is a researcher at the forefront of human pose estimation (HPE), with a focus on developing lightweight, real-time solutions for critical medical applications. Her most cited work, "HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method" (2025, 4 citations), addresses pressing challenges in nursing robotics, where accurate patient monitoring is essential. Tan’s major contribution lies in tackling the persistent issues of high false positive and false negative rates, while meeting stringent real-time demands under constrained computational resources. By designing an efficient architecture that balances speed and precision, she has advanced the feasibility of deploying HPE in practical healthcare settings. Her work is particularly notable for its potential to enhance robotic-assisted care, improving patient safety and autonomy. With a growing citation impact, Tan is establishing herself as an innovator in applied computer vision, bridging the gap between algorithmic efficiency and real-world medical needs. Her research not only pushes the boundaries of pose estimation but also underscores the transformative role of AI in healthcare technology.
Research Focus
Key Achievements
Top Papers
- 1HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method4 citations · 2025