Affective State

Affective state research focuses on understanding and modeling human emotions and feelings, aiming to improve mental health monitoring, human-computer interaction, and other applications. Current research utilizes diverse data sources, including smartphone sensor data, text analysis (often employing large language models), physiological signals (like fNIRS), and visual data from cameras and smart glasses, with machine learning algorithms like recurrent neural networks and convolutional neural networks playing a central role in analysis and prediction. This field is significant for its potential to improve mental health interventions, personalize learning experiences, and enhance the design of more empathetic and responsive technologies.