Ruizhi Su
Papers
1
Total Citations
6
H-Index
1
About
Ruizhi Su is an emerging researcher in the field of human-computer interaction (HCI), with a specialized focus on electrooculography (EOG)-based systems. His primary research areas include eye movement classification, deep learning architectures, and assistive technologies. Su’s most notable contribution is the development of the Residual Self-Calibrated Network with Multi-Scale Channel Attention, a novel deep learning framework designed to significantly improve the accuracy of EOG-based eye movement classification. This work addresses a critical bottleneck in EOG-HCI technology, which has applications in assistive robots, augmented reality, and gaming. By integrating self-calibration and multi-scale attention mechanisms, Su’s model enhances feature extraction and classification performance, offering a robust solution for real-world HCI systems. Though early in his career, his 2024 paper has already garnered 6 citations, signaling growing interest and impact in the field. Su’s work stands out for its technical innovation and practical relevance, promising to advance the reliability and usability of non-invasive eye-tracking interfaces. His research is particularly valuable for students and engineers developing next-generation assistive technologies and immersive interactive systems.
Research Focus
Key Achievements
Top Papers
- 1