Arousal Prediction

Arousal prediction, the task of estimating the level of physiological and psychological activation, is a key area of research in affective computing with applications in healthcare, human-computer interaction, and entertainment. Current research focuses on improving the accuracy and generalizability of arousal prediction models using diverse data sources (e.g., physiological signals, text, music, facial expressions) and advanced machine learning techniques, including transformer networks, graph neural networks, and contrastive learning. These advancements aim to bridge the gap between laboratory settings and real-world applications, leading to more robust and personalized systems for emotion recognition and response.