Qingxia Yu
Papers
1
Total Citations
4
H-Index
1
About
Qingxia Yu is a researcher advancing deep learning for intelligent healthcare systems, with a focus on gesture recognition and fall detection for companion robots. Her most-cited work, "Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network" (2025), tackles the critical challenge of balancing accuracy and real-time performance in resource-constrained environments. This paper, which has already garnered 4 citations shortly after publication, proposes a novel attentional up-downsampling pyramid network that significantly reduces model complexity while maintaining high detection precision—a breakthrough for deploying AI in assistive robotics. Yu’s contributions address the urgent need for optimized care solutions amid rising healthcare costs driven by aging populations and chronic illnesses. Her research is particularly notable for bridging the gap between state-of-the-art object detection architectures and practical, lightweight implementations suitable for edge devices. By enabling more responsive and reliable companion robots, Yu’s work has direct implications for improving quality of life for elderly and chronically ill patients. Her innovative approach to model efficiency positions her as a key contributor to the next generation of intelligent, real-time healthcare technologies.
Research Focus
Key Achievements
Top Papers
- 1Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network4 citations · 2025