Papers
1
Total Citations
37
H-Index
1
About
Qun Wu is a leading researcher in biomedical signal processing and human–computer interaction, with a focus on emotion recognition through physiological signals. Their most-cited work, "Emotion stimuli-based surface electromyography signal classification employing Markov transition field and deep neural networks" (2021, 37 citations), introduces a novel approach that transforms surface electromyography (sEMG) signals into image-like representations using Markov transition fields, enabling deep neural networks to classify emotional states with high accuracy. This contribution bridges signal processing and affective computing, offering a robust method for decoding human emotions from muscle activity. Wu’s research has significant implications for developing adaptive interfaces, mental health monitoring, and assistive technologies. By integrating advanced machine learning with physiological sensing, they have advanced the field’s ability to capture subtle emotional responses. Their work is widely cited in interdisciplinary studies, reflecting its impact on both engineering and psychology. Qun Wu continues to push boundaries in non-invasive emotion detection, making their research essential for students and scholars exploring the intersection of biosignals and artificial intelligence.
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Top Papers
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