Visual-vestibular conflicts (VVCs) are a primary contributor to visually induced motion sickness (VIMS) in head-mounted displays

Outside of self-report surveys, there are no proven, reliable methods to quantify visual discomfort or visually induced motion sickness symptoms when using head-mounted displays. While valuable tools, self-report surveys suffer from potential biases and low sensitivity due to variability in how respondents may assess and report their experience. Consequently, extreme visual-vestibular conflicts are generally used to induce discomfort symptoms large enough to measure reliably with surveys (e.g., stationary participants riding virtual roller coasters). An emerging area of research is the prediction of discomfort survey results from physiological and behavioral markers. However, the signals derived from experimental paradigms that are explicitly designed to be uncomfortable may not generalize to more naturalistic experiences where comfort is prioritized. In this work we introduce a custom VR headset designed to introduce significant near-eye optical distortion (i.e., pupil swim) to induce visual discomfort during more typical VR experiences. We evaluate visual comfort in our headset while users play the popular VR title Job Simulator and show that eye-tracked dynamic distortion correction improves visual comfort in a multi-session, within-subjects user study. We additionally use representational similarity analysis to highlight changes in head and gaze behavior that are potentially more sensitive to visual discomfort than surveys.

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.

This paper presents a novel solution for estimating simulator sickness in HMDs using machine learning and 3D motion data, informed by user-labeled simulator sickness data and user analysis. We conducted a novel VR user study, which decomposed motion data and used an instant dial-based sickness scoring mechanism. We were able to emulate typical VR usage and collect user simulator sickness scores. Our user analysis shows that translation and rotation differently impact user simulator sickness in HMDs. In addition, users’ demographic information and self-assessed simulator sickness susceptibility data are collected and show some indication of potential simulator sickness. Guided by the findings from the user study, we developed a novel deep learning-based solution to better estimate simulator sickness with decomposed 3D motion features and user profile information. The model was trained and tested using the 3D motion dataset with user-labeled simulator sickness and profiles collected from the user study. The results show higher estimation accuracy when using the 3D motion data compared with methods based on optical flow extracted from the recorded video, as well as improved accuracy when decomposing the motion data and incorporating user profile information.

When using a see-through augmented reality head-mounted display system (AR HMD), a user’s perception of virtual content may be degraded by a variety of perceptual artifacts resulting from the architecture of rendering and display pipelines. In particular, virtual content that is rendered to appear stationary in the real world (worldlocked) can be susceptible to spatial and temporal 3D position errors. A subset of these errors, termed jitter, result from mismatches between the spatial localization, rendering, and display pipelines, and can manifest as perceived motion of intended-to-be stationary content. Here, we employ psychophysical methods to quantify the perceptibility of jitter artifacts in an AR HMD. For some viewing conditions, participants perceived jitter that was smaller than the pixel pitch of the testbed (i.e., subpixel jitter). In general, we found that jitter perceptibility increased as viewing distance increased and decreased as background luminance increased. We did not find that the contrast ratio of virtual content, age, or experience with AR/VR modulatedjitter perceptibility. Taken together, this study quantifies the degree of jitter that a user can perceive in an AR HMD and demonstrates that it is critical to consider the capabilities and limits of the human visual system when designing the next generation of spatial computing platforms.