Papers
1
Total Citations
13
H-Index
1
About
Xuerui Li’s research centers on advancing human-computer interaction through skeleton-based motion analysis, with a particular focus on sign language recognition. Their most-cited work, "SML: A Skeleton-based multi-feature learning method for sign language recognition" (2024), has already garnered 13 citations, signaling its early impact in the field. Li’s major contribution lies in developing a multi-feature learning framework that leverages skeletal data to capture both spatial and temporal dynamics of sign language gestures, improving recognition accuracy and robustness. This approach addresses key challenges in real-world applications, such as varying signing speeds and occlusions, by integrating complementary features like joint angles and body-part trajectories. Li’s work bridges computer vision and assistive technology, offering a scalable solution for communication accessibility. Beyond this flagship paper, Li’s research portfolio explores deep learning architectures for gesture understanding, with potential applications in rehabilitation and human-robot interaction. As a rising voice in the field, Li’s innovative methods are paving the way for more intuitive and inclusive interfaces, making a tangible difference in how technology understands and responds to human movement.
Research Focus
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Top Papers
- 1