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. 2020 Dec 29;21(1):163.
doi: 10.3390/s21010163.

Personality-Based Affective Adaptation Methods for Intelligent Systems

Krzysztof Kutt  1 , Dominika Drążyk  1 , Szymon Bobek  1 , Grzegorz J Nalepa  1
Affiliations

Personality-Based Affective Adaptation Methods for Intelligent Systems

Krzysztof Kutt et al. Sensors (Basel). .

Abstract

In this article, we propose using personality assessment as a way to adapt affective intelligent systems. This psychologically-grounded mechanism will divide users into groups that differ in their reactions to affective stimuli for which the behaviour of the system can be adjusted. In order to verify the hypotheses, we conducted an experiment on 206 people, which consisted of two proof-of-concept demonstrations: a "classical" stimuli presentation part, and affective games that provide a rich and controllable environment for complex emotional stimuli. Several significant links between personality traits and the psychophysiological signals (electrocardiogram (ECG), galvanic skin response (GSR)), which were gathered while using the BITalino (r)evolution kit platform, as well as between personality traits and reactions to complex stimulus environment, are promising results that indicate the potential of the proposed adaptation mechanism.

Keywords: adaptation; affective computing; emotion detection; personality assessment; wearable sensors.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Trends in the IADS set stimuli ratings.
Figure 2
Figure 2
“Valence-arousal faces” widget as in the study (in Polish). X axis has labels “negative”, “neutral”, and “positive”, while the Y axis has labels: “high arousal” and “low arousal”. The picture is presented with a negative filter.
Figure 3
Figure 3
“5-faces” widget. The picture is presented with a negative filter.
Figure 4
Figure 4
An example of the “Affective SpaceShooter 2” gameplay, asteroids falling, and an affective picture in the background [56].
Figure 5
Figure 5
An example of the “Freud me out 2” gameplay [56].
Figure 6
Figure 6
The split of ratings in the emospace widget into clusters (left). Cluster _0_ introduced as an intercept for emospace-related analyses (right).
Figure 7
Figure 7
Mixed model of the influence of binary stimuli conditions on: the emoscale widget response (left), the emospace valence vector response (center), and the emospace arousal vector response (right).
Figure 8
Figure 8
Mixed model of binary stimuli conditions on the emospace widget response.
Figure 9
Figure 9
Mixed model of the influence of specific stimuli conditions on: the emoscale widget response (left), the emospace valence vector response (center), and the emospace arousal vector response (right).
Figure 10
Figure 10
Mixed model of the influence of specific stimuli conditions on the emospace widget response.
Figure 11
Figure 11
Multinomial logistic regression models concerning the clustered emospace widget responses.
See this image and copyright information in PMC

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