In this paper we present a novel affective modelling approach to be utilised by Affective Computing systems. This approach is a combination of the well known Arousal Valence model of emotion and the newly introduced Affective Trajectories Hypothesis. An adaptive data driven fuzzy method is proposed in order to extract personalized emotion models, and successfully visualise the associations of these models’ basic elements, to different emotional labels, using easily interpretable fuzzy rules. Namely we explore how the combinations of arousal, valence, prediction of the future, and the experienced outcome after this prediction, enable us to differentiate between different emotional labels. We use the results obtained from a user study consisting of an online survey, to demonstrate the potential applicability of this affective modelling approach, and test the effectiveness and stability of its adaptive element, which accounts for individual differences between the users. We also propose a basic architecture in order for this approach to be used effectively by AC systems, and finally we present an implementation of a personalised learning system which utilises the suggested framework. This implementation is tested through a pilot experimental session consisting of a tutorial on fuzzy logic which was conducted under an activity-led and problem based learning context.
|Publication status||Published - 2016|
|Event||International Conference on Internet of Things and Big Data - Rome, Italy|
Duration: 23 Apr 2016 → 25 Apr 2016
|Conference||International Conference on Internet of Things and Big Data|
|Abbreviated title||IoTBD 2016|
|Period||23/04/16 → 25/04/16|
Bibliographical noteThe full text is currently unavailable on the repository.
- Adaptive Fuzzy Systems
- Emotion Modelling
- Affective Trajectories
- Arousal Valence
- Affective Computing
- Personalised Learning
Karyotis, C., Doctor, F., Iqbal, R., James, A., & Chang, V. (2016). A Fuzzy Modelling Approach of Emotion for Affective Computing Systems. 453-460. Paper presented at International Conference on Internet of Things and Big Data, Rome, Italy. https://doi.org/10.5220/0005945604530460