Rotational and Translational Velocity and Acceleration Thresholds for the Onset of Cybersickness in Virtual Reality

This paper determined rotational and translational velocity and acceleration thresholds for the onset of cybersickness. Cybersickness causes discomfort and discourages the widespread use of virtual reality systems for both recreational and professional use. Visual motion or optic flow is known to be one of the main causes of cybersickness due to the sensory conflict it creates with the vestibular system. The aim of this experiment is to detect rotational and translational velocity and acceleration thresholds that cause the onset of cybersickness. Participants were exposed to a moving particle field in virtual reality for a few seconds per run. The field moved in different directions (longitudinal, lateral, roll, and yaw), with different velocity profiles (steady and accelerating), and different densities. Using a staircase procedure, that controlled the speed or acceleration of the field, we detected the threshold at which participant started to feel temporary symptoms of cybersickness. The optic flow was quantified for each motion type and by modifying the number of features, the same amount of optic flow was present in each scene. Having the same optic flow in each scene allows a direct comparison of the thresholds. The results show that the velocity and acceleration thresholds for rotational optic flow were significantly lower than for translational optic flow. The thresholds suggestively decreased with the decreasing particle density of the scene. Finally, it was found that all the rotational and translational thresholds strongly correlate with each other. While the mean values of the thresholds could be used as guidelines to develop virtual reality applications, the high variability between individuals implies that the individual tuning of motion controls would be more effective to reduce cybersickness while minimizing the impact on the experience of immersion

Dataset for predicting cybersickness from a virtual navigation task

This work presents a dataset collected to predict cybersickness in virtual reality environments. The data was collected from navigation tasks in a virtual environment designed to induce cybersickness. The dataset consists of many data points collected from diverse participants, including physiological responses (EDA and Heart Rate) and self-reported cybersickness symptoms. The paper will provide a detailed description of the dataset, including the arranged navigation task, the data collection procedures, and the data format. The dataset will serve as a valuable resource for researchers to develop and evaluate predictive models for cybersickness and will facilitate more research in cybersickness mitigation

Machine learning methods for the study of cybersickness: a systematic review

This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness

Psychometric evaluation of the Simulator Sickness Questionnaire as a measure of cybersickness

Some users of virtual reality (VR) technology experience negative symptoms, known as cybersickness, sometimes severe enough to cause discontinuation of VR use. Despite decades of research, there has been relatively little progress understanding the underlying causal mechanisms of cybersickness. Review of the measures used to assess cybersickness symptoms, particularly the subjective psychological components of cybersickness, indicated that extant questionnaires may exhibit psychometric problems that could affect interpretation of results. In the present study, new data were collected (N = 202) to evaluate the psychometric properties of the Simulator Sickness Questionnaire (SSQ), the most commonly reported measure of cybersickness symptoms, in the context of virtual reality. Findings suggest that the SSQ, as commonly used, is not applicable to VR. An alternative approach to measure cybersickness is suggested. Overall, incidence and severity of cybersickness was very low and participants rated the VR experience as highly entertaining

Examination of Cybersickness in Virtual Reality: The Role of Individual Differences, Effects on Cognitive Functions & Motor Skills, and Intensity Differences During and After Immersion

Background: Given that VR is applied in multiple domains, understanding the effects of cyber-sickness on human cognition and motor skills and the factors contributing to cybersickness gains urgency. This study aimed to explore the predictors of cybersickness and its interplay with cognitive and motor skills. Methods: 30 participants, 20-45 years old, completed the MSSQ and the CSQ-VR, and were immersed in VR. During immersion, they were exposed to a roller coaster ride. Before and after the ride, participants responded to CSQ-VR and performed VR-based cognitive and psychomotor tasks. Post-VR session, participants completed the CSQ-VR again. Results: Motion sickness susceptibility, during adulthood, was the most prominent predictor of cybersickness. Pupil dilation emerged as a significant predictor of cybersickness. Experience in videogaming was a significant predictor of both cybersickness and cognitive/motor functions. Cybersickness negatively affected visuospatial working memory and psychomotor skills. Overall cybersickness', nausea and vestibular symptoms' intensities significantly decreased after removing the VR headset. Conclusions: In order of importance, motion sickness susceptibility and gaming experience are significant predictors of cybersickness. Pupil dilation appears as a cybersickness' biomarker. Cybersickness negatively affects visuospatial working memory and psychomotor skills. Cybersickness and its effects on performance should be examined during and not after immersion.Comment: 32 Pages, 4 figures, 14 Tables. The article has been submitted to Virtual Worlds Journa

Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.Comment: Tp appear in the CCNC 2019 Conferenc