Seeking Accessible Physiological Metrics to Detect Cybersickness in VR

Abstract

Virtual reality is predicted to change the way we use technologies in education. However, currently restricting the lack of integration is high cost, unconvincing learning data, complexity of the technologies, and persistently, cybersickness. There are a number of theories as to the causes of cybersickness, but none are infallible. Moreover, many of the evaluation methods and empirical studies are highly specialized physiological analyses requiring sophisticated measuring equipment. Such studies can be difficult to prepare and present unnatural conditions for users engaged in a VR experience. In this research the Empatica E4 wearable device and its ecosystem were utilized to record physiological metrics of heart rate variability and electrodermal activity during customized computer-based and VR tasks to detect the onset of cybersickness. Although inconclusive, the metrics of NNMean, SDNN, RMSSD in HRV data, and SCR width and Peak EDA in EDA data are proposed for further analysis as potential indicators of cybersickness.