Title: A Controlled Set-Up Experiment to Establish Personalized Baselines for Real-Life Emotion Recognition,
Abstract: We design, conduct and present the results of a highly personalized baseline
emotion recognition experiment, which aims to set reliable ground-truth
estimates for the subject's emotional state for real-life prediction under
similar conditions using a small number of physiological sensors. We also
propose an adaptive stimuli-selection mechanism that would use the user's
feedback as guide for future stimuli selection in the controlled-setup
experiment and generate optimal ground-truth personalized sessions
systematically. Initial results are very promising (85% accuracy) and variable
importance analysis shows that only a few features, which are easy-to-implement
in portable devices, would suffice to predict the subject's emotional state.