# VR Sickness Evaluation Method Using Recurrence Period Density Entropy
## 1 Introduction  
Recent advancements in various content offerings have led to a growing demand for immersive and enjoyable experiences. Owing to this demand, content in virtual reality (VR) environments is gaining attention. In a VR environment, the content offers viewers a high level of immersion and interest. However, a drawback is the occurrence of VR sickness, which makes prolonged viewing difficult. Similar to traditional motion sickness, VR sickness arises from different causes according to the sensory conflict and postural instability theories [1, 2]. The sensory conflict theory attributes motion sickness to a perceptual conflict caused by a mismatch in experiences between the vestibular and sensory organs. The postural instability theory defines that motion sickness is triggered by an unstable posture. Research on VR sickness is focused on understanding its state based on the aforementioned theories, with particular emphasis on actively advancing methods to measure and confirm VR sickness. The methods for measuring VR sickness and confirming its status typically fall into two categories: subjective and objective. Methods utilizing subjective data often measure VR sickness experiences using tools such as the simulation sickness questionnaire (SSQ), motion sickness susceptibility questionnaire (MSSQ), and VR sickness questionnaire (VRSQ) [3, 4, 5]. The SSQ is the most commonly used tool for measuring VR sickness [3]. Before and after viewing the content, participants are surveyed to assess their condition, allowing for the identification of changes in their VR sickness. The MSSQ [4] is primarily used to assess the participants' experiences of motion sickness, focusing on identifying their preexisting vulnerabilities. The VRSQ [5] proposed by [PERSON] is an advanced survey derived from the SSQ. Similar to the SSQ, the VRSQ also assesses changes in VR sickness through surveys but is specifically tailored to VR environments, unlike the traditional SSQ. Methods utilizing subjective data (surveys) such as these allow for a relatively straightforward determination of VR sickness occurrence. However, they face the challenge of individual variability in assessing the severity of motion sickness, as personal thresholds fluctuate moment-by-moment, making them difficult to use as criteria for comparison. Therefore, methods that utilize objective data have been proposed. These methods often involve measuring the VR sickness using physiological data, which mainly include pupil dilation, electroencephalography (EEG), and electrocardiography (ECG). [PERSON] [6] suggested that VR sickness induced by viewing content through a head-mounted display (HMD) is closely related to visual information processing, and, thus, speculated a correlation with the pupil state. Consequently, using infrared cameras, this study confirmed that following VR sickness experiences, the average and standard deviation of pupil diameter increased, and irregular pupil rhythm patterns characterized by a decrease in the proportion of pupil rhythm consistency emerged. [PERSON] [7] confirmed that when the resolution of the VR content decreased during viewing, the participants' pupil diameter significantly increased. In addition, [PERSON] [8] observed significant differences in delta, theta, and alpha waves in the frontal and central regions of the brain before and after a VR experience. Other studies [9; 10] have similarly confirmed a quantitative correlation between VR sickness and delta and theta waves, which are the predominantly used brain-wave areas during the experience. [PERSON] [11] utilized ECG data to validate the correlation between key features of heart rate data (e.g., SDNN and pNN50) and SSQ scores) to classify sickness based on these findings. Notably, biosignals are directly associated with VR sickness and can be used as measurement indicators. However, because of the varying processing methods for each biosignal, it is challenging to establish standardized metrics for VR sickness (e.g., biodata-based scores and quantification).