Tracking motion sickness in dynamic VR environments with EDA signals

Virtual reality (VR) has emerged as a mainstream commercial technology with multiple sensory channels, including advanced user interfaces (human-computer). However, the number of users complaining of VR motion sickness (MS) has been rapidly increasing. In this study, VR MS was investigated using subjective questionnaires: simulator sickness questionnaire (SSQ), virtual reality sickness questionnaire (VRSQ), and fast motion sickness scale (FMS) and objective physiological methods: electrodermal activity (EDA). To track VR MS based on symptoms, a model was constructed by applying a polynomial regression method. In addition, this study attempted to determine whether there are differences in MS according to the degree of immersion in a VR environment. Twenty participants (10 males and 10 females) were exposed to a dynamic VR environment through a simple operation method using a head-mounted display (HMD). The coefficients of determination of the objective measurement components were high before and after the rest. In particular, both the SSQ and VRSQ were highest for disorientation, that is, dizziness disorder and difficulty concentrating. This suggests that the timing of the EDA signal data extraction with respect to the rest may be critical. In addition, subjects placed in an immersive VR environment performed the experiment in an unstable and dynamic posture; accordingly, the VR MS score was higher for subjects who participated in the experiment in a more dynamic position. Therefore, we confirmed the possibility of a significant association between VR sickness and postural stability. This study demonstrates the possibility of tracking MS in a dynamic VR environment according to intervals and symptoms using EDA signals, and the stability of posture should be considered in immersed VR environments. Relevance to industry: The findings here provide insights into the model of tracking motion sickness using the EDA signal to prevent or reduce VR motion sickness. The EDA signal model can benefit the design or development of an immersive VR environment. •Developed motion sickness (MS) tracking model for dynamic VR using subjective measurements and physiological indicators.•The EDA signal model was best able to track symptoms such as disorientation and disorder, among the many MS symptoms.•At rest, the EDA was not suitable for representing general MS.•Track MS based on the symptoms and timing of data extraction was important.