Personality-Based Affective Adaptation Methods for Intelligent Systems
Jagiellonian Human-Centered Artificial Intelligence Laboratory (JAHCAI) and Institute of Applied Computer Science, Jagiellonian University, 31-007 Krakow, Poland
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Sensors 2021, 21(1), 163; https://doi.org/10.3390/s21010163
Received: 9 November 2020 / Revised: 8 December 2020 / Accepted: 18 December 2020 / Published: 29 December 2020
(This article belongs to the Special Issue Multimodal Sensing for Understanding Behavior and Personality)
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.
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Keywords:
affective computing; adaptation; emotion detection; personality assessment; wearable sensors
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MDPI and ACS Style
Kutt, K.; Drążyk, D.; Bobek, S.; Nalepa, G.J. Personality-Based Affective Adaptation Methods for Intelligent Systems. Sensors 2021, 21, 163. https://doi.org/10.3390/s21010163
AMA Style
Kutt K, Drążyk D, Bobek S, Nalepa GJ. Personality-Based Affective Adaptation Methods for Intelligent Systems. Sensors. 2021; 21(1):163. https://doi.org/10.3390/s21010163
Chicago/Turabian StyleKutt, Krzysztof; Drążyk, Dominika; Bobek, Szymon; Nalepa, Grzegorz J. 2021. "Personality-Based Affective Adaptation Methods for Intelligent Systems" Sensors 21, no. 1: 163. https://doi.org/10.3390/s21010163
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