Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00329437" target="_blank" >RIV/68407700:21230/19:00329437 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/s19050989" target="_blank" >https://doi.org/10.3390/s19050989</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s19050989" target="_blank" >10.3390/s19050989</a>

Result language

angličtina

Original language name

Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Volume of the periodical

19

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

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UT code for WoS article

000462540400008

EID of the result in the Scopus database

2-s2.0-85063528994