Datasets and Analyses for "Affect Recognition using Psychophysiological Correlates in High Intensity VR Exergaming"
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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.
本数据集及配套分析内容对应发表于CHI 2020的论文《高强度虚拟现实运动游戏中基于心理生理关联的情感识别》（Affect Recognition using Psychophysiological Correlates in High Intensity VR Exergaming）。 本研究公开了两项实验的数据集，旨在探索不同传感器在虚拟现实运动游戏（VR Exergaming）情感识别任务中的应用。第一项实验对比了静息状态、常规运动、虚拟现实运动游戏以及久坐型虚拟现实游戏四种场景下，身体负荷与游戏化设计对心理生理测量指标的影响。第二项实验则对比了低负荷、高负荷与最优负荷三种虚拟现实运动游戏场景。本研究筛选出注视点、眨眼次数、瞳孔直径与皮肤电导率作为适用于虚拟现实运动游戏情感识别的心理生理指标，并分析了这些指标在判定情感效价与唤醒度中的应用价值。本研究结果可为虚拟现实运动游戏情感交互领域的研究者提供参考依据。 本数据集及配套分析内容包含如下组成部分： 1. 两份CSV格式表格，分别包含第一项与第二项实验的定量与定性数据； 2. 两份JASP文件，分别对应第一项与第二项实验的方差分析（ANOVA）与t检验分析内容； 3. 两份R脚本，分别用于第一项与第二项实验的相关性分析与回归分析。
