Abstract:
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 psy- chophysiological measurements has been successful for mod- erate 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 psychophysiologi- cal measurements during rest, conventional exercise, VR ex- ergaming, and sedentary VR gaming. The second experiment compares underwhelming, overwhelming and optimal VR ex- ergaming 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.
Reference:
Soumya C. Barathi, Michael Proulx, Eamonn O'Neill, and Christof Lutteroth. 2020. Affect Recognition using Psychophysiological Correlates in High Intensity VR Exergaming. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–15. DOI:https://doi.org/10.1145/3313831.3376596