Datasets and analyses for the paper "Affect Recognition using Psychophysiological Correlates in High Intensity VR Exergaming" published at CHI 2020.
We present the datasets of 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.
The datasets and analyses consist of the following:
1. two CSV sheets containing the quantitative and qualitative data of the Experiments I and II;
2. two JASP files with ANOVAS and t-tests for Experiments I and II;
3. two R scripts with correlation and regression analyses for Experiments I and II.