2017/03/19 by Kollia, Varvara, Tayebi, Noureddine
#FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (stat.ML)
paper · doi:10.48550/arxiv.1703.06537
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.