Affective Computing: Theories, Applications, and Predictive Models

This lecture explores the era of affectivism, analyzing NSF funding trends in emotion research, the impact of affective states on education and medicine, and the use of facial expressions and email analysis in affective computing. It delves into emotion theories like James-Lange, Cannon-Bard, Schachter-Singer, and dimensional theories, as well as basic emotions and emotion wheels. The lecture covers the detection of emotions through cardiac activity, skin conductance, and facial expressions, discussing heart rate variability, skin conductance measures, and the use of various devices for emotion prediction. It also presents methods like variational autoencoders, convolutional neural networks, and fully-connected networks for affective state prediction based on physiological signals and visual data.