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

Xiaolan Peng, Xurong Xie, Jin Huang, Chutian Jiang, Haonian Wang, Alena Denisova, Hui Chen, Feng Tian*, Hongan Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationCHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450394215
Publication statusPublished - 19 Apr 2023
Event2023 CHI Conference on Human Factors in Computing Systems, CHI 2023 - Hamburg, Germany
Duration: 23 Apr 202328 Apr 2023

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Conference2023 CHI Conference on Human Factors in Computing Systems, CHI 2023

Bibliographical note

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.


  • machine learning
  • perceived challenge
  • physiological signals
  • player experience
  • video games

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