Yuanshi Ren
Papers
1
Total Citations
7
H-Index
1
About
Yuanshi Ren is a researcher at the forefront of action recognition and sports biomechanics, with a particular focus on table tennis motion analysis. Their most-cited work, "Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks" (2024, 7 citations), introduces a novel deep learning approach that captures bat trajectory patterns from variable-length input sequences. This contribution bridges computer vision and sports science, enabling more accurate biomechanical analysis and auxiliary training systems for athletes. Ren's research has direct applications in smart homes, gaming, virtual reality, and security monitoring, demonstrating the versatility of their methods. By addressing the challenge of varying-length motion data, they have advanced the field of human activity recognition, making it more adaptable to real-world scenarios. Their work is particularly impactful for developing table tennis robots and motion-sensing games, where precise trajectory understanding is critical. With a growing citation record, Yuanshi Ren is establishing themselves as a key contributor to the intersection of deep learning and sports analytics.
Research Focus
Key Achievements
Top Papers
- 1