Projects per year
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 recognition in VR exergaming and analyse their utility in determining affective valence and arousal. Our findings provide guidelines for researchers of affective VR exergames.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 665992
|Title of host publication||CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems|
|Place of Publication||New York, U.S.A.|
|Publisher||Association for Computing Machinery|
|Number of pages||15|
|Publication status||Published - 21 Apr 2020|
|Name||Conference on Human Factors in Computing Systems - Proceedings|
- VR exergaming
- affect recognition
- high intensity exercise
- psychophysiological correlates
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Human-Computer Interaction
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1/04/14 → 30/09/22
Project: Research-related funding
Cosker, D., Bilzon, J., Campbell, N., Cazzola, D., Colyer, S., Fincham Haines, T., Hall, P., Kim, K. I., Lutteroth, C., McGuigan, P., O'Neill, E., Richardt, C., Salo, A., Seminati, E., Tabor, A. & Yang, Y.
1/09/15 → 28/02/21
Project: Research council
- Department of Computer Science - Head of Department
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for the Analysis of Motion, Entertainment Research & Applications
- Centre for Networks and Collective Behaviour
- Centre for Autonomous Robotics (CENTAUR)
- EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa)
- Centre for Doctoral Training in Decarbonisation of the Built Environment (dCarb)
- REal and Virtual Environments Augmentation Labs (REVEAL)
- Human-Computer Interaction
Person: Research & Teaching
Datasets and Analyses for "Affect Recognition using Psychophysiological Correlates in High Intensity VR Exergaming"