Tse–Yu Pan
Papers
1
Total Citations
26
H-Index
1
About
Tse–Yu Pan is a researcher whose work sits at the intersection of affective computing, physiological signal processing, and deep learning. His primary research focus is on emotion recognition using biosignals, particularly Galvanic Skin Response (GSR), which captures the electrical properties of human skin to reveal underlying emotional states. His most cited work, "Emotion Recognition from Galvanic Skin Response Signal Based on Deep Hybrid Neural Networks" (2020), has garnered 26 citations and introduces a novel framework that combines deep hybrid neural networks to decode emotional cues from GSR data. This contribution is significant for advancing non-invasive, real-time emotion detection systems, with potential applications in human-computer interaction, mental health monitoring, and adaptive learning environments. Pan’s approach bridges physiological sensing with modern AI, offering a robust method for recognizing emotions that goes beyond traditional self-report measures. His work stands out for its technical rigor and practical relevance, making him a notable figure in the growing field of affective computing. For students and researchers, Pan’s research exemplifies how deep learning can unlock new insights from physiological signals, paving the way for more empathetic and responsive technologies.
Research Focus
Key Achievements
Top Papers
- 1