Zhiquan Qiu
Papers
1
Total Citations
4
H-Index
1
About
Zhiquan Qiu is a researcher advancing the intersection of computer vision and healthcare robotics, with a primary focus on human pose estimation (HPE) for medical applications. His most notable contribution is the development of HP-YOLO, a lightweight, real-time human pose estimation method introduced in 2025. This work directly addresses critical challenges in nursing robotics, including reducing high false positive and negative rates while meeting stringent real-time processing demands on computationally limited devices. By optimizing the YOLO architecture for pose estimation, Qiu’s method enables more reliable patient monitoring without sacrificing speed or accuracy—a vital improvement for assistive healthcare environments. Although early in its impact, HP-YOLO has already garnered 4 citations, signaling growing interest from researchers working on efficient, deployment-ready vision systems. Qiu’s research bridges the gap between state-of-the-art computer vision and practical medical robotics, offering solutions that are both robust and resource-conscious. His work is particularly relevant for students and engineers seeking to build real-time, on-device pose estimation systems for healthcare, rehabilitation, or human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method4 citations · 2025