Investigation of visually induced motion sickness in dynamic 3D contents based on subjective judgment, heart rate variability, and depth gaze behavior

VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research

Researchers have used machine learning approaches to identify motion sickness in VR experience. These approaches would certainly benefit from an accurately labeled, real-world, diverse dataset that enables the development of generalizable ML models. We introduce 'VR.net', a dataset comprising 165-hour gameplay videos from 100 real-world games spanning ten diverse genres, evaluated by 500 participants. VR.net accurately assigns 24 motion sickness-related labels for each video frame, such as camera/object movement, depth of field, and motion flow. Building such a dataset is challenging since manual labeling would require an infeasible amount of time. Instead, we implement a tool to automatically and precisely extract ground truth data from 3D engines' rendering pipelines without accessing VR games' source code. We illustrate the utility of VR.net through several applications, such as risk factor detection and sickness level prediction. We believe that the scale, accuracy, and diversity of VR.net can offer unparalleled opportunities for VR motion sickness research and beyond.We also provide access to our data collection tool, enabling researchers to contribute to the expansion of VR.net.

A Deep Motion Sickness Predictor Induced by Visual Stimuli in Virtual Reality

In a virtual reality (VR) environment, where visual stimuli predominate over other stimuli, the user experiences cybersickness because the balance of the body collapses due to self-motion. Accordingly, the VR experience is accompanied by unavoidable sickness referred to as visually induced motion sickness (VIMS). In this article, our primary purpose is to simultaneously estimate the VIMS score by referring to the content and calculate the temporally induced VIMS sensitivity. To seek our goals, we propose a novel architecture composed of two consecutive networks: 1) neurological representation and 2) spatiotemporal representation. In the first stage, the network imitates and learns the neurological mechanism of motion sickness. In the second stage, the significant feature of the spatial and temporal domains is expressed over the generated frames. After the training procedure, our model can calculate VIMS sensitivity for each frame of the VR content by using the weakly supervised approach for unannotated temporal VIMS scores. Furthermore, we release a massive VR content database. In the experiments, the proposed framework demonstrates excellent performance for VIMS score prediction compared with existing methods, including feature engineering and deep learning-based approaches. Furthermore, we propose a way to visualize the cognitive response to visual stimuli and demonstrate that the induced sickness tends to be activated in a similar tendency, as done in clinical studies.

Studying freeway merging conflicts using virtual reality technology

INTRODUCTION

This research aims to investigate the perceptions and reactions of drivers regarding freeway merging situation, utilizing a new approach with the basis of a multilevel simulation platform which incorporates virtual reality (VR) technology.

METHODS

A VR driving environment integrated with traffic micro-simulation was developed to evaluate driving behaviors and the impact of merging decisions in terms of traffic conflicts. The driving experiments were conducted under a variety of circumstances, including varying traffic flows and the presence of ramp metering. The Surrogate Safety Assessment Model (SSAM) was utilized to extract the number of conflicts from the micro-simulation results.

RESULTS

The final results indicated that the probability of conflict has a positive correlation with traffic flow, while conflict frequency at freeway merges is affected by the presence of ramp metering due to its potentiality to enhance driver decisions and reduce the drivers' pressure when they make maneuvers. Practical Applications: The findings reveal that the proposed VR simulation platform is a useful tool to improve the safety of freeway merging. It has the potential to enhance driver skills and can also be used in the study of human-machine interaction.

Analysis on Mitigation of Visually Induced Motion Sickness by Applying Dynamical Blurring on a User's Retina

Visually induced motion sickness (MS) experienced in a 3D immersive virtual environment (VE) limits the widespread use of virtual reality (VR). This paper studies the effects of a saliency detection-based approach on the reduction of MS when the display on a user's retina is dynamic blurred. In the experiment, forty participants were exposed to a VR experience under a control condition without applying dynamic blurring, and an experimental condition applying dynamic blurring. The experimental results show that the participants under the experimental condition report a statistically significant reduction in the severity of MS symptoms on average during the VR experience compared to those under the control condition, which demonstrates that the proposed approach may alleviate visually induced MS in VR and enable users to remain in a VE for a longer period of time.