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A Fuzzy Modelling Approach of Emotion for Affective Computing Systems

A Fuzzy Modelling Approach of Emotion for Affective Computing Systems
A Fuzzy Modelling Approach of Emotion for Affective Computing Systems
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.

Adaptive fuzzy systems, Emotion Modelling, Affective Trajectories, Arousal Valence, Affective Computing, Personalised Learning.
Karyotis, Charalampos
c5d364d8-d10f-443d-adb6-aa3faad7f396
Doctor, Faiyaz
25dbcfed-785e-415b-a7f3-32332c832700
Iqbal, Rahat
2744f938-f729-4043-95be-cb9718f83220
James, Anne
57efa9ae-7c1e-4968-9e81-9769313ae2e7
Chang, Victor
a7c75287-b649-4a63-a26c-6af6f26525a4
Karyotis, Charalampos
c5d364d8-d10f-443d-adb6-aa3faad7f396
Doctor, Faiyaz
25dbcfed-785e-415b-a7f3-32332c832700
Iqbal, Rahat
2744f938-f729-4043-95be-cb9718f83220
James, Anne
57efa9ae-7c1e-4968-9e81-9769313ae2e7
Chang, Victor
a7c75287-b649-4a63-a26c-6af6f26525a4

Karyotis, Charalampos, Doctor, Faiyaz, Iqbal, Rahat, James, Anne and Chang, Victor (2016) A Fuzzy Modelling Approach of Emotion for Affective Computing Systems. The first international conference on Internet of Things and Big Data, Rome, Italy. 22 - 25 Apr 2016. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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More information

Accepted/In Press date: 1 March 2016
Venue - Dates: The first international conference on Internet of Things and Big Data, Rome, Italy, 2016-04-22 - 2016-04-25
Keywords: Adaptive fuzzy systems, Emotion Modelling, Affective Trajectories, Arousal Valence, Affective Computing, Personalised Learning.
Organisations: Electronics & Computer Science, Electronic & Software Systems

Identifiers

Local EPrints ID: 390191
URI: http://eprints.soton.ac.uk/id/eprint/390191
PURE UUID: afce7fec-45bc-4b5e-beb2-78f980bbc689

Catalogue record

Date deposited: 19 Mar 2016 14:08
Last modified: 15 Jul 2020 16:33

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Contributors

Author: Charalampos Karyotis
Author: Faiyaz Doctor
Author: Rahat Iqbal
Author: Anne James
Author: Victor Chang

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