Automatic Detection of Cybersickness from Physiological Signal in a Virtual Roller Coaster Simulation

Abstract

Virtual reality (VR) systems often induce motion sickness like discomfort known as cybersickness. The standard approach for detecting cybersickness includes collecting both subjective and objective measurements, while participants are exposed to VR. With the recent advancement of machine learning, we can train deep neural networks to detect cybersickness severity from subjective (e.g., self-reported sickness periodically) and objective measurements. In this study, we collected physiological data from 31 participants while they were immersed in VR. Self-reported verbal sickness was collected at each minute interval for labeling the physiological data. Finally, a simple neural network was proposed to detect cybersickness severity.