Modeling affective state using learning vector quantization

Emotions and stress are vital for our day to day functioning. The busy life we live, can however, cause emotional unbalance. Smart devices can help us remain balanced by providing early warnings, for example, in case of high levels of stress. A key enabler for such devices is to be able to measure emotions and stress. This thesis studies how a certain type of self-learning computer system can be used in this so-called affective domain and explores emotion recognition based upon three different methods of measuring: the body (physiology), the face (facial expressions) and the brain (cognitive processes). By doing so, this thesis combines theory and application at the boundary of computing science and psychology, also termed affective computing. The research performed shows that computers, based upon these self-learning systems, can detect emotions from photos of facial expressions and can detect stress from cardiac signals, both with high accuracy. The methods used, also provide models based upon which new knowledge can be gained. As an example, the mouth and eyes were found most vital for recognizing facial expressions and it was found that emotion recognition from physiology can be improved by adding measurements of specific heart frequencies.