| Abstract: |
One of the main applications of affective computing remains supporting e-learning process. Therefore, apart from human mentoring, automatic emotion recognition is also applied in monitoring learning activities. Specific context of e-learning, that happens at home desk or anywhere (mobile e-learning), adds additional challenge to emotion recognition, e.g. temporal unavailability and noise in input channels. Nowadays, affective computing has provided many solutions for emotion recognition. There are numerous emotion recognition algorithms which differ on input information channels, representation emotion model on output and classification method. The most common approach is to combine the emotion information channels. Using multiple input channels proved to be the most accurate and reliable, however there is no standard architecture proposed for this kind of solutions. This paper presents outline of the author's PhD thesis, which concentrates on integration of emotional states in educational applications with consideration of uncertainty. The paper presents state of art, the architecture of integration, performer experiments and planned simulations. |