Yuanning Liu

University of Science and Technology of China

Papers

Total Citations

H-Index

About

Yuanning Liu is a researcher whose work sits at the intersection of computer vision, human-computer interaction, and machine learning. Their most-cited paper, "A Kinect based gesture recognition algorithm using GMM and HMM" (2013, 25 citations), pioneered a robust method for full-body gesture recognition by combining Gaussian Mixture Models (GMM) with Hidden Markov Models (HMM) using Microsoft Kinect’s 3D joint data. This work addressed key challenges in modeling continuous, natural gestures for robotics and interactive systems, laying a foundation for more intuitive human-machine interfaces. Liu’s contributions are particularly notable for advancing real-time, markerless motion capture and recognition, a critical step toward seamless HCI. With 25 citations, this paper has influenced subsequent research in gesture-based control and assistive technologies. Liu’s work demonstrates a clear focus on bridging the gap between raw sensor data and meaningful interaction, making them a valuable voice in the ongoing effort to make computers more responsive to human movement.