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Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

1
Department of Cybernetics and Artificial Intelligence, Technical University in Košice, Letná 9, 040 01 Košice, Slovakia
2
Department of Computer Science, Czech Technical University in Prague, 166 36 Prague, Czech Republic
3
The Biorobotics Institute, Scuola Superiore Sant’Anna, 560 25 Pisa, Italy
*
Author to whom correspondence should be addressed.
This paper is an extension of our previous work published in Certicky, M.; Certicky, M.; Sincak, P.; Cavallo, F. Modeling user experience in electronic entertainment using psychophysiological measurements. In Proceeding of the 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), Kosice, Slovakia, 23–25 Augest 2018.
Sensors 2019, 19(5), 989; https://doi.org/10.3390/s19050989
Received: 28 December 2018 / Revised: 1 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
(This article belongs to the Section Intelligent Sensors)
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Abstract

Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced ∼96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers. View Full-Text
Keywords: machine learning; psychophysiological measures; user experience; modeling; digital entertainment; enjoyment; heart rate; respiratory activity; electroencephalography; galvanic skin response machine learning; psychophysiological measures; user experience; modeling; digital entertainment; enjoyment; heart rate; respiratory activity; electroencephalography; galvanic skin response
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Čertický, M.; Čertický, M.; Sinčák, P.; Magyar, G.; Vaščák, J.; Cavallo, F. Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment. Sensors 2019, 19, 989.

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