Seeking Accessible Physiological Metrics to Detect Cybersickness in VR

Seeking Accessible Physiological Metrics to Detect Cybersickness in VR

Takurou Magaki (Future University, Hakodate, Japan) and Michael Vallance (Future University, Hakodate, Japan)
Copyright: © 2020 |Pages: 18
DOI: 10.4018/IJVAR.2020010101

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.
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Literature Review

Overcoming cybersickness has been problematic for developers of VR since its inception and remains an obstacle for widespread adoption of VR in such scenarios as mainstream education and industrial training. Nausea, asthenopia, temperature increase, sweating and dizziness can be negative experiences when engaged in a VR activity (Dennison et al., 2016). At a high level of anxiety, for instance, users can experience nausea (i.e. extreme cybersickness) which can eventually lead to vomiting. The underlying mechanism of cybersickness is not yet clear though and there is no consensus on the causes of cybersickness (Davis et al., 2015; Rebenitsch et al., 2016). Theories researched have included sensory mismatch, poison theory, postural instability, and evolutionary theory (Hale and Stanney, 2018). Sensory mismatch, also known as vection, is the most widely accepted theory (Dennison et al., 2016) and is caused by the vestibular system (which senses motion and spatial orientation in an attempt to maintain postural stability) not matching the immediate visual system. A common example to illustrate sensory mismatch is to consider a person sitting at the window of a stationary train feeling in motion when observing a parallel train moving away from the platform. This is attributed to factors such as the user’s position within the VR experience and the user’s field of view (Tambovtsev et al., 2016). Cybersickness is also effected by a sense of touch and position (termed Somatosensory), balance (the Vestibular system in the ear passage), and muscles that control eye movements (termed Oculomotor). As Davis et al. (2015) highlight: “the issue is complicated as experiences of cybersickness vary greatly between individuals, the technologies being used, the design of the environment and the tasks being performed.”

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