Peng, Xiaolan, Xie, Xurong, Huang, Jin et al. (6 more authors) (2023) ChallengeDetect:Investigating the Potential of Detecting In-Game Challenge Experience from Physiological Measures. In: CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023, 23-28 Apr 2023 Conference on Human Factors in Computing Systems - Proceedings . Association for Computing Machinery, Inc , DEU , pp. 1-29.
Abstract
Challenge is the core element of digital games. The wide spectrum of physical, cognitive, and emotional challenge experiences provided by modern digital games can be evaluated subjectively using a questionnaire, the CORGIS, which allows for a post hoc evaluation of the overall experience that occurred during game play. Measuring this experience dynamically and objectively, however, would allow for a more holistic view of the moment-to-moment experiences of players. This study, therefore, explored the potential of detecting perceived challenge from physiological signals. For this, we collected physiological responses from 32 players who engaged in three typical game scenarios. Using perceived challenge ratings from players and extracted physiological features, we applied multiple machine learning methods and metrics to detect challenge experiences. Results show that most methods achieved a detection accuracy of around 80%. We discuss in-game challenge perception, challenge-related physiological indicators and AI-supported challenge detection to inform future work on challenge evaluation.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Funding Information: This work was supported by the National Natural Science Foundation of China (Grant no. 62132010, 62172397), CAS Project for Young Scientists in Basic Research (Grant No.YSBR-040) and Youth Innovation Promotion Association CAS (Grant no. 2023119,2020113). We thank the reviewers for their input in improving this work. Publisher Copyright: © 2023 Owner/Author. |
Keywords: | machine learning,perceived challenge,physiological signals,player experience,video games |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Sciences (York) > Chemistry (York) |
Depositing User: | Pure (York) |
Date Deposited: | 07 Aug 2023 09:30 |
Last Modified: | 18 Dec 2024 00:41 |
Published Version: | https://doi.org/10.1145/3544548.3581232 |
Status: | Published |
Publisher: | Association for Computing Machinery, Inc |
Series Name: | Conference on Human Factors in Computing Systems - Proceedings |
Identification Number: | 10.1145/3544548.3581232 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202173 |