2016/07/20 by Kollia, Varvara
#FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (stat.ML)
paper · doi:10.48550/arxiv.1607.05832
This paper addresses the problem of emotion recognition from physiological signals. Features are extracted and ranked based on their effect on classification accuracy. Different classifiers are compared. The inter-subject variability and the personalization effect are thoroughly investigated, through trial-based and subject-based cross-validation. Finally, a personalized model is introduced, that would allow for enhanced emotional state prediction, based on the physiological data of subjects that exhibit a certain degree of similarity, without the requirement of further feedback.