Inaccurate spatial tracking in extended reality (XR) devices leads to virtual object jitter, misalignment, and user discomfort, fundamentally limiting immersive experiences and natural interactions. In this work, we introduce a novel testbed that enables simultaneous, synchronized evaluation of multiple XR devices under identical environmental and kinematic conditions. Leveraging this platform, we present the first comprehensive empirical benchmarking of five state-of-the-art XR devices across 16 diverse scenarios. Our results reveal substantial intra-device performance variation, with individual devices exhibiting up to 101% increases in error when operating in featureless environments. We also demonstrate that tracking accuracy strongly correlates with visual conditions and motion dynamics. We also observe significant inter-device disparities, with performance differences of up to$2.8 \times$, which are closely linked to hardware specifications such as sensor configurations and dedicated processing units. Finally, we explore the feasibility of substituting a motion capture system with the Apple Vision Pro as a practical ground truth reference. While the Apple Vision Pro delivers highly accurate relative pose error estimates ($R^{2}=0.830$), its absolute pose error estimation remains limited ($R^{2}=0.387$), highlighting both its potential and its constraints for rigorous XR evaluation. This work establishes the first standardized framework for comparative XR tracking evaluation, providing the research community with reproducible methodologies, comprehensive benchmark datasets, and open-source tools that enable systematic analysis of tracking performance across devices and conditions, thereby accelerating the development of more robust spatial sensing technologies for XR systems.

Augmented Reality (AR) systems, while enhancing task performance through real-time guidance, pose risks of inducing cognitive tunneling—a hyperfocus on virtual content that compromises situational awareness (SA) in safety-critical scenarios. This paper investigates SA in AR-guided cardiopulmonary resuscitation (CPR), where responders must balance effective compressions with vigilance to unpredictable hazards (e.g., patient vomiting). We developed an AR app on a Magic Leap 2 that overlays real-time CPR feedback (compression depth and rate) and conducted a user study with simulated unexpected incidents (e.g., bleeding) to evaluate SA, in which SA metrics were collected via observation and questionnaires administered during freeze-probe events. Eye tracking analysis revealed that higher SA levels were associated with greater saccadic amplitude and velocity, and with reduced proportion and frequency of fixations on virtual content. To predict SA, we propose FixGraphPool, a graph neural network that structures gaze events (fixations, saccades) into spatiotemporal graphs, effectively capturing dynamic attentional patterns. Our model achieved 83.0 % accuracy ($\mathrm{F} 1=81.0 \%$), outperforming feature-based machine learning and state-of-the-art time-series models by leveraging domain knowledge and spatial-temporal information encoded in ET data. These findings demonstrate the potential of eye tracking for SA modeling in AR and highlight its utility in designing AR systems that ensure user safety and situational awareness.

We present AR-TMT, an AR adaptation of the Trail Making Test designed to evaluate how different types of distractors affect user attention in the AR environment. Built on the Magic Leap 2, AR-TMT captures gaze behavior as users perform a cognitively demanding visual search task under two distraction conditions: bottom-up (visually salient) and top-down (semantically misleading). The system visualizes gaze patterns and performance, revealing distinct attentional responses to the two distraction types. This demo could give insights on cognitive security in XR by offering a empirical findings of visual attention vulnerabilities with regard to distraction types and informing the design of distraction-aware, cognitively resilient AR systems.

Cardiopulmonary Resuscitation (CPR) is a critical procedure where noticing unexpected patient changes is as vital as performing quality compressions. While Augmented Reality (AR) applications have shown promise in guiding CPR, they often focus on metrics such as compression rate and depth, which can negatively impact rescuers by inducing attentional tunneling—a phenomenon where intense focus on virtual elements causes a user to miss critical events in their physical environment. This demonstration presents an AR-guided CPR system to investigate this effect, using a mannequin with embedded LED lights that illuminate randomly during the task. Attendees will experience two variations of our AR application: one with CPR quality feedback in the form of a speed gauge and related text information, and another that adds a dynamic, in-context compression depth visualizer. By recording the reaction time to the LED stimulus and analyzing gaze allocation on virtual elements in our AR app, we will demonstrate how AR can induce attentional tunneling, even with good design intentions.

Despite the growing use of AR in safety-critical domains, the field lacks a systematic understanding of how different types of distraction affect user behavior in AR environments. To address this gap, we present AR-TMT, an AR adaptation of the Trail Making Test that spatially renders targets for sequential selection on the Magic Leap 2. We implemented distractions in three categories: top-down, bottom-up, and spatial distraction based on Wolfe’s Guided Search model, and captured performance, gaze, motor behavior, and subjective load measures to analyze user attention and behavior. A user study with 34 participants revealed that top-down distraction degraded performance through semantic interference, while bottom-up distraction disrupted initial attentional engagement. Spatial distraction destabilized gaze behavior, leading to more scattered and less structured visual scanning patterns. We also found that performance was correlated with attention control (R2 =.20–.35) under object-based distraction conditions, where distractors possessed task-relevant features. The study offers insights into distraction mechanisms and their impact on users, providing opportunities for generalization to ecologically relevant AR tasks while underscoring the need to address the unique demands of AR environments.

Object detection (OD) is crucial for numerous emerging visual sensing applications. As OD models trained on unrepresentative data usually yield poor performance, collecting high-quality data in the local environment is recognized to be essential for improving model accuracy. Yet, the question of how to collect this data is currently largely overlooked; unsupported data collection tends to produce datasets with a significant proportion of redundant or uninformative data, hindering effective model training. To address this challenge, we design a real-time data importance estimation method and integrate it into BiGuide, a bi-level image data acquisition system we create for OD tasks. BiGuide assesses the importance of the captured images in real-time based on informativeness and diversity estimations and dynamically guides users in collecting useful data via image-level and object instance-level guidance. We prototype BiGuide in an edge-based architecture using commodity smartphones as mobile clients, and evaluate its performance via an IRB-approved study with 20 users. Our evaluation demonstrates that OD models trained on the data collected by BiGuide outperform models trained on the data collected by two baseline systems, achieving detection accuracy improvements of up to 33.07% and 14.57%, respectively. Over 85% of the users found BiGuide fast, helpful, and easy to understand and follow.

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.