Hao Quan

Papers

Total Citations

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About

Hao Quan is a robotics and computer vision researcher whose work focuses on human-robot interaction and human activity understanding. His most notable contribution is the development of a novel human-like control framework for mobile medical service robots, designed to address the urgent need for contactless delivery of meals and medication to isolated patients during the COVID-19 pandemic. This work, which has garnered 5 citations, demonstrates his commitment to applying AI and robotics to real-world healthcare challenges. Beyond robotics, Quan has made significant contributions to human pose tracking and annotation. He developed HAVPTAT, a semi-automatic annotation tool that detects and tracks multiple people and their poses in video, improving annotation efficiency and providing dynamic visualization of human bounding boxes and keypoints. Complementing this, he created PyHAPT, a Python-based framework for processing annotated human pose tracking data from unconstrained environments, offering interpolation and other essential data-processing functionalities. Together, these tools (with 4 and 3 citations, respectively) advance the field of human activity analysis, enabling more robust and efficient data preparation for training computer vision models. Quan’s work bridges the gap between automated perception and practical deployment in healthcare and human behavior analysis.