Jie Ren

Papers

Total Citations

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About

Jie Ren is an emerging researcher specializing in motion recognition and deep learning applications in sports technology. Their work sits at the intersection of computer vision, neural network design, and human motion analysis, with a particular focus on advancing intelligent systems for athletic performance evaluation and training. Ren's most notable contribution to date is a pioneering study on table tennis motion recognition using bat trajectory data, introducing an innovative varying-length-input convolutional neural network (CNN) architecture. This approach addresses a key challenge in sports motion analysis — handling the inherent variability in movement sequences — and opens new possibilities for biomechanical analysis, automated coaching systems, and immersive technologies such as virtual reality and motion-sensing games. The work also has direct implications for the development of table tennis robots and smart training platforms. Though early in their citation trajectory with 7 citations since 2024, Ren's research addresses technically demanding problems with real-world applicability across domains including smart homes, security monitoring, and gaming. Students and practitioners exploring AI-driven sports analytics or human-computer interaction will find Ren's contributions a valuable and timely reference in this rapidly evolving field.