A Fuzzy Modelling Approach of Emotion for Affective Computing Systems

Charalampos Karyotis, Faiyaz Doctor, Rahat Iqbal, Anne James, V. Chang

    Research output: Contribution to conferencePaperpeer-review

    3 Citations (Scopus)


    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.
    Original languageEnglish
    Publication statusPublished - 2016
    EventInternational Conference on Internet of Things and Big Data - Rome, Italy
    Duration: 23 Apr 201625 Apr 2016


    ConferenceInternational Conference on Internet of Things and Big Data
    Abbreviated titleIoTBD 2016

    Bibliographical note

    The full text is currently unavailable on the repository.


    • Adaptive Fuzzy Systems
    • Emotion Modelling
    • Affective Trajectories
    • Arousal Valence
    • Affective Computing
    • Personalised Learning


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