User experience estimation of VR exergame players by recognising their affective state could enable us to personalise and optimise their experience. Affect recognition based on psychophysiological measurements has been successful for moderate intensity activities. High intensity VR exergames pose challenges as the effects of exercise and VR headsets interfere with those measurements. We present two experiments that investigate the use of different sensors for affect recognition in a VR exergame. The first experiment compares the impact of physical exertion and gamification on psychophysiological measurements during rest, conventional exercise, VR exergaming, and sedentary VR gaming. The second experiment compares underwhelming, overwhelming and optimal VR exergaming scenarios. We identify gaze fixations, eye blinks, pupil diameter and skin conductivity as psychophysiological measures suitable for affect recoginition in VR exergaming and analyse their utility in determining affective valence and arousal. Our findings provide guidelines for researchers of affective VR exergames.
Original languageEnglish
Title of host publicationProceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York, U.S.A.
PublisherAssociation for Computing Machinery
Number of pages15
ISBN (Electronic)9781450367080
Publication statusAccepted/In press - 16 Jan 2020

Publication series

NameCHI: Conference on Human Factors and Computing Systems
PublisherACM Press
ISSN (Electronic)1062-9432


  • VR exergaming, affect recognition, psychophysiological correlates, high intensity exercise

ASJC Scopus subject areas

  • Human-Computer Interaction

Cite this

Barathi, S. C., Proulx, M., O'Neill, E., & Lutteroth, C. (Accepted/In press). Affect Recognition using Psychophysiological Correlates in High Intensity VR Exergaming. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Vol. 2020-April, pp. 1-15). (CHI: Conference on Human Factors and Computing Systems). New York, U.S.A.: Association for Computing Machinery. https://doi.org/10.1145/3313831.3376596