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About

Xiaofei Ji is a leading researcher in human–robot interaction, computer vision, and intelligent robotics, with a particular focus on gesture recognition and autonomous systems. Her most influential work, "An Integrative Framework of Human Hand Gesture Segmentation for Human–Robot Interaction" (2015, 51 citations), introduces a novel, humanlike approach to segmenting hand gestures from RGB-D data captured by Kinect sensors. This framework significantly reduces sensing errors and improves segmentation precision, directly enhancing the responsiveness of robotic systems to human commands. Ji has also pioneered the use of fuzzy qualitative methods for human motion analysis, as seen in her 2017 works on motion sensing, recognition, and kinematics, which offer robust alternatives to traditional quantitative approaches. More recently, she has advanced autonomous robotics with the design of a fully autonomous indoor spray robot (2023) and the development of RS-RCNN, a hybrid ResNet-Swin Transformer object detection algorithm for indoor window detection. Her contributions bridge the gap between intuitive human gesture communication and precise robotic control, earning her recognition for both foundational theory and practical deployment. With over 80 combined citations, Ji’s work continues to shape safer, more efficient human–robot collaboration in real-world environments.