Arousal and Emotional Valence-level Estimations using Non-contact Environmental Sensing

This paper proposes an arousal and emotional valence-level estimations method using non-contact environmental sensing, which aims to solve some of the problems of conventional emotion estimation methods such as stress and discomfort caused by wearing measurement devices and invasion of privacy caused by using facial images. Construct models using machine and deep learning to estimate arousal and emotional values from environmental data. Although the arousal level estimation accuracy is somewhat low at about 0.68, emotional valence achieves about 0.85 with machine learning with decision trees.