An official website of the United States government
Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work.
more »
« less
This content will become publicly available on May 20, 2026
Opto-diversity and Eye Tracking: Assumptions about ocular alignment in virtual reality eye tracking exclude users with strabismus and amblyopia
Immersive, interactive virtual reality (VR) experiences rely on eye tracking data for a variety of applications. However, eye trackers assume that the user's eyes move in a coordinated way. We investigate how the violation of this assumption impacts the performance and subjective experience of users with strabismus and amblyopia. Our investigation follows a case study approach by analyzing in depth the qualitative and quantitative data collected during an interactive VR game by a small number of users with these visual impairments. Our findings reveal the ways in which assumptions about the default functioning of the eye can discourage or even exclude otherwise enthusiastic users from immersive VR. This study thus opens a new frontier for eye tracking research and practice.
more »
« less
- Award ID(s):
- 2206950
- PAR ID:
- 10627233
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Applied Perception
- ISSN:
- 1544-3558
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Virtual reality (VR) simulations have been adopted to provide controllable environments for running augmented reality (AR) experiments in diverse scenarios. However, insufficient research has explored the impact of AR applications on users, especially their attention patterns, and whether VR simulations accurately replicate these effects. In this work, we propose to analyze user attention patterns via eye tracking during XR usage. To represent applications that provide both helpful guidance and irrelevant information, we built a Sudoku Helper app that includes visual hints and potential distractions during the puzzle-solving period. We conducted two user studies with 19 different users each in AR and VR, in which we collected eye tracking data, conducted gaze-based analysis, and trained machine learning (ML) models to predict user attentional states and attention control ability. Our results show that the AR app had a statistically significant impact on enhancing attention by increasing the fixated proportion of time, while the VR app reduced fixated time and made the users less focused. Results indicate that there is a discrepancy between VR simulations and the AR experience. Our ML models achieve 99.3% and 96.3% accuracy in predicting user attention control ability in AR and VR, respectively. A noticeable performance drop when transferring models trained on one medium to the other further highlights the gap between the AR experience and the VR simulation of it.more » « less
-
In the realm of virtual reality (VR) research, the synergy of methodological advancements, technical innovation, and novel applications is paramount. Our work encapsulates these facets in the context of spatial ability assessments conducted within a VR environment. This paper presents a comprehensive and integrated framework of VR, eye-tracking, and electroencephalography (EEG), which seamlessly combines measuring participants’ behavioral performance and simultaneously collecting time-stamped eye tracking and EEG data to enable understanding how spatial ability is impacted in certain conditions and if such conditions demand increased attention and mental allocation. This framework encompasses the measurement of participants’ gaze pattern (e.g., fixation and saccades), EEG data (e.g., Alpha, Beta, Gamma, and Theta wave patterns), and psychometric and behavioral test performance. On the technical front, we utilized the Unity 3D game engine as the core for running our spatial ability tasks by simulating altered conditions of space exploration. We simulated two types of space exploration conditions: (1) microgravity condition in which participants’ idiotropic (body) axis is in statically and dynamically misaligned with their visual axis; and (2) conditions of Martian terrain that offers a visual frame of reference (FOR) but with limited and unfamiliar landmarks objects. We specifically targeted assessing human spatial ability and spatial perception. To assess spatial ability, we digitalized behavioral tests of Purdue Spatial Visualization Test: Rotations (PSVT: R), the Mental Cutting Test (MCT), and the Perspective Taking Ability (PTA) test and integrated them into the VR settings to evaluate participants’ spatial visualization, spatial relations, and spatial orientation ability, respectively. For spatial perception, we applied digitalized versions of size and distance perception tests to measure participants’ subjective perception of size and distance. A suite of C# scripts orchestrated the VR experience, enabling real-time data collection and synchronization. This technical innovation includes the integration of data streams from diverse sources, such as VIVE controllers, eye-tracking devices, and EEG hardware, to ensure a cohesive and comprehensive dataset. A pivotal challenge in our research was synchronizing data from EEG, eye tracking, and VR tasks to facilitate comprehensive analysis. To address this challenge, we employed the Unity interface of the OpenSync library, a tool designed to unify disparate data sources in the fields of psychology and neuroscience. This approach ensures that all collected measures share a common time reference, enabling meaningful analysis of participant performance, gaze behavior, and EEG activity. The Unity-based system seamlessly incorporates task parameters, participant data, and VIVE controller inputs, providing a versatile platform for conducting assessments in diverse domains. Finally, we were able to collect synchronized measurements of participants’ scores on the behavioral tests of spatial ability and spatial perception, their gaze data and EEG data. In this paper, we present the whole process of combining the eye-tracking and EEG workflows into the VR settings and collecting relevant measurements. We believe that our work not only advances the state-of-the-art in spatial ability assessments but also underscores the potential of virtual reality as a versatile tool in cognitive research, therapy, and rehabilitation.more » « less
-
Understanding how individuals focus and perform visual searches during collaborative tasks can help improve user engagement. Eye tracking measures provide informative cues for such understanding. This article presents A-DisETrac, an advanced analytic dashboard for distributed eye tracking. It uses off-the-shelf eye trackers to monitor multiple users in parallel, compute both traditional and advanced gaze measures in real-time, and display them on an interactive dashboard. Using two pilot studies, the system was evaluated in terms of user experience and utility, and compared with existing work. Moreover, the system was used to study how advanced gaze measures such as ambient-focal coefficient K and real-time index of pupillary activity relate to collaborative behavior. It was observed that the time a group takes to complete a puzzle is related to the ambient visual scanning behavior quantified and groups that spent more time had more scanning behavior. User experience questionnaire results suggest that their dashboard provides a comparatively good user experience.more » « less
-
Blascheck, Tanja; Bradshaw, Jessica; Vrzakova, Hana (Ed.)Virtual Reality (VR) technology has advanced to include eye-tracking, allowing novel research, such as investigating how our visual system coordinates eye movements with changes in perceptual depth. The purpose of this study was to examine whether eye tracking could track perceptual depth changes during a visual discrimination task. We derived two depth-dependent variables from eye tracker data: eye vergence angle (EVA) and interpupillary distance (IPD). As hypothesized, our results revealed that shifting gaze from near-to-far depth significantly decreased EVA and increased IPD, while the opposite pattern was observed while shifting from far-to-near. Importantly, the amount of change in these variables tracked closely with relative changes in perceptual depth, and supported the hypothesis that eye tracker data may be used to infer real-time changes in perceptual depth in VR. Our method could be used as a new tool to adaptively render information based on depth and improve the VR user experience.more » « less
