Artificial Intelligence (AI) and indoor sensing increasingly support decision-making in spatial environments. However, traditional visualization methods impose a substantial mental workload when viewers translate this digital information into real-world spaces, leading to inappropriate reliance on AI. Embedded visualizations in Augmented Reality (AR), by integrating information into physical environments, may reduce this workload and foster more appropriate reliance on AI. To assess this, we conducted an empirical study (N = 32) comparing an AR embedded visualization (X-ray) and 2D Minimap in AI-assisted, time-critical spatial target selection tasks. Surprisingly, evidence shows that the embedded visualization led to greater inappropriate reliance on AI, primarily as over-reliance, due to factors like perceptual challenges, visual proximity illusions, and highly realistic visual representations. Nonetheless, the embedded visualization showed benefits in spatial mapping. We conclude by discussing empirical insights, design implications, and directions for future research on human-AI collaborative decision in AR.

Cybersickness remains a major barrier to the widespread adoption of virtual reality (VR) technologies, motivating researchers to investigate its causes and mitigation strategies through comparative human-subjects studies. These experiments may employ either within- or between-subjects designs. Although within-subjects designs require fewer participants and potentially offer higher statistical power, researchers need to wash out carryover effects of cybersickness symptoms before each session to avoid confounding the results. Prior studies have employed washout periods of varying lengths; some required testing conditions on different days, while others allowed only short breaks of less than 15 minutes. Although shorter washout periods are more convenient for experimenters, their impact on study outcomes has not been systematically investigated. In this work, we conducted a meta-analysis to evaluate the effects of study design and washout period length on cybersickness self-reports measured with the Simulator Sickness Questionnaire (SSQ). We found that short washout periods reduce statistical power compared to long washout periods or between-subjects designs. Based on these findings, we provide guidelines to help researchers design more reliable cybersickness studies and improve the generalizability of their results.

Spatiotemporal sensitivity to high frequency information declines with increased peripheral eccentricity. Foveated rendering exploits this by decreasing the spatial resolution of rendered images in peripheral vision, reducing the rendering cost by omitting high frequency details. As foveation levels increase, the rendering quality is reduced, and traditional foveated rendering systems tend not to preserve samples that were previously rendered at high spatial resolution in previous frames. Additionally, prior research has shown that saccade landing positions are distributed around a target location rather than landing at a single point, and that even during fixations, eyes perform small microsaccades around a fixation point. This creates an opportunity for sampling from temporally neighbouring frames with differing foveal locations to reduce the required rendered size of the foveal region while achieving a higher perceived image quality. We further observe that the temporal presentation of pixels frame-to-frame can be viewed as a data stream, presenting a random sampling problem. Following this intuition, we propose a Weighted Reservoir Sampling technique to efficiently maintain a reservoir of the perceptually relevant high quality pixel samples from previous frames and incorporate them into the computation of the current frame. This allows the renderer to render a smaller region of foveal pixels per frame by temporally reusing pixel samples that are still relevant to reconstruct a higher perceived image quality, while allowing for higher levels of foveation. Our method operates on the output of foveated rendering, and runs in under 1 ms at 4K resolution, making it highly efficient and integrable with real-time VR and AR foveated rendering systems.

Subjective measures, such as the Simulator Sickness Questionnaire (SSQ), Fast Motion Sickness Questionnaire (FMS), and discomfort scores, are widely used to assess cybersickness, but they often interrupt the user experience and are prone to bias. To overcome these limitations, researchers have also investigated objective indicators, though some approaches, such as using physiological data, can be cumbersome and impractical. Based on the loss of complexity hypothesis, which suggests that certain conditions, such as disease or aging, can produce a reduction of complexity in physiological system dynamics, we conducted an initial investigation of the relationship between movement complexity and cybersickness. We analyzed motion tracking collected from two previous cybersickness studies using the d95 score, a complexity metric derived using principal component analysis. The results revealed a systematic relationship between movement complexity and cybersickness across both experiments. Higher discomfort scores were associated with a reduction in complexity, thereby supporting the loss of complexity hypothesis. Furthermore, the 9-DOF complexity measure, which includes both physical head movement and virtual camera motion, was a more sensitive indicator than the 6-DOF measure computed from physical movements alone. These initial findings suggest that movement complexity may be a useful objective indicator for future cybersickness research.

This paper investigates the influence of X-ray vision metaphors on distance estimation in optical-see-through augmented reality (AR) in action space. A within-subjects study $(\mathrm{N}=30)$ was conducted to evaluate depth judgments across five conditions, including a novel “invisible mesh” technique. Participants performed a series of blind walking tasks that required estimating the depth of AR objects displayed at multiple distance ranges in front or behind a physical occluding surface. Although quantitative results regarding the impact of different X-ray vision metaphors on distance perception were inconclusive, participant feedback revealed a diversity of strategies and preferences. Overall, the findings suggest that no single metaphor was considered universally superior, and multiple X-ray vision metaphors may be suitable for different users and situations. This research contributes to understanding of X-ray vision techniques and informs the design considerations for AR systems aiming to enhance depth perception and user experience.

The decoupled relationship between the optical and inertial information in virtual reality is commonly acknowledged as a major factor contributing to cybersickness. Based on laws of physics, we noticed that a slope naturally affords acceleration, and the gravito-inertial force we experience when we are accelerating freely on a slope has the same relative direction and approximately the same magnitude as the gravity we experience when standing on the ground. This provides the opportunity to simulate a slope by manipulating the orientation of virtual objects accordingly with the accelerating optical flow. In this paper, we present a novel space deformation technique that deforms the virtual environment to replicate the structure of a slope when the user accelerates virtually. As a result, we can restore the physical relationship between the optical and inertial information available to the user. However, the changes to the geometry of the virtual environment during space deformation remain perceptible to users. Consequently, we created two different transition effects, pinch and tilt, which provide different visual experiences of ground bending. A human subject study (N=87) was conducted to evaluate the effects of space deformation on both slope perception and cyber-sickness. The results confirmed that the proposed technique created a strong feeling of traveling on a slope, but no significant differences were found on measures of discomfort and cybersickness.

Moving target recognition is a critical task for a variety of applications, ranging from environmental monitoring to regional security protection. Recently, many deep learning (DL) methods have been proposed to recognize the seismic features of moving targets. However, the established DL algorithms are mainly challenged by time-consuming feature extraction and lack of robustness. In this article, a novel moving target recognition method [Compression Observation-Seismic DL (CO-SDL)] is proposed to solve the above two problems simultaneously. CO-SDL first uses a measurement matrix to project the seismic signal onto a compressed domain and obtain compressed seismic measurements. This operation removes redundant data while retaining valuable seismic information and suppressing noise energy. Following that, CO-SDL efficiently and stably extracts deep nonlinear features from compressed seismic measurements and then accurately classifies the feature vectors. To evaluate the proposed method, a comprehensive seismic dataset is developed. This dataset covers six types of common moving targets, and the SNR ranges of all signal types are greater than 15 dB. The proposed method and the benchmark methods are tested on this dataset. Experimental results prove that the presented CO-SDL method is ten times faster than the state-of-the-art methods with comparable accuracy. Furthermore, the CO-SDL method shows the strongest robustness.