ChallengeDetect: Investigating the Potential of Detecting In-Game Challenge Experience from Physiological Measures

TY - GEN

T1 - ChallengeDetect

T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023

AU - Peng, Xiaolan

AU - Xie, Xurong

AU - Huang, Jin

AU - Jiang, Chutian

AU - Wang, Haonian

AU - Denisova, Alena

AU - Chen, Hui

AU - Tian, Feng

AU - Wang, Hongan

N1 - 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.

PY - 2023/4/19

Y1 - 2023/4/19

N2 - 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.

AB - 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.

KW - machine learning

KW - perceived challenge

KW - physiological signals

KW - player experience

KW - video games

UR - http://www.scopus.com/inward/record.url?scp=85160014006&partnerID=8YFLogxK

U2 - 10.1145/3544548.3581232

DO - 10.1145/3544548.3581232

M3 - Conference contribution

AN - SCOPUS:85160014006

T3 - Conference on Human Factors in Computing Systems - Proceedings

SP - 1

EP - 29

BT - CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery, Inc

Y2 - 23 April 2023 through 28 April 2023

ER -