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Mobile augmented reality (AR) has a wide range of promising applications, but its efficacy is subject to the impact of environment texture on both machine and human perception. Performance of the machine perception algorithm underlying accurate positioning of virtual content, visual-inertial SLAM (VI-SLAM), is known to degrade in low-texture conditions, but there is a lack of data in realistic scenarios. We address this through extensive experiments using a game engine-based emulator, with 112 textures and over 5000 trials. Conversely, human task performance and response times in AR have been shown to increase in environments perceived as textured. We investigate and provide encouraging evidence for invisible textures, which result in good VI-SLAM performance with minimal impact on human perception of virtual content. This arises from fundamental differences between VI-SLAM-based machine perception, and human perception as described by the contrast sensitivity function. Our insights open up exciting possibilities for deploying ambient IoT devices that display invisible textures, as part of systems which automatically optimize AR environments. 

Virtual content instability caused by device pose tracking error remains a prevalent issue in markerless augmented reality (AR), especially on smartphones and tablets. However, when examining environments which will host AR experiences, it is challenging to determine where those instability artifacts will occur; we rarely have access to ground truth pose to measure pose error, and even if pose error is available, traditional visualizations do not connect that data with the real environment, limiting their usefulness. To address these issues we present SiTAR (Situated Trajectory Analysis for Augmented Reality), the first situated trajectory analysis system for AR that incorporates estimates of pose tracking error. We start by developing the first uncertainty-based pose error estimation method for visual-inertial simultaneous localization and mapping (VI-SLAM), which allows us to obtain pose error estimates without ground truth; we achieve an average accuracy of up to 96.1% and an average FI score of up to 0.77 in our evaluations on four VI-SLAM datasets. Next, we present our SiTAR system, implemented for ARCore devices, combining a backend that supplies uncertainty-based pose error estimates with a frontend that generates situated trajectory visualizations. Finally, we evaluate the efficacy of SiTAR in realistic conditions by testing three visualization techniques in an in-the-wild study with 15 users and 13 diverse environments; this study reveals the impact both environment scale and the properties of surfaces present can have on user experience and task performance. 

Demand is growing for markerless augmented reality (AR) experiences, but designers of the real-world spaces that host them still have to rely on inexact, qualitative guidelines on the visual environment to try and facilitate accurate pose tracking. Furthermore, the need for visual texture to support markerless AR is often at odds with human aesthetic preferences, and understanding how to balance these competing requirements is challenging due to the siloed nature of the relevant research areas. To address this, we present an integrated design methodology for AR spaces, that incorporates both tracking and human factors into the design process. On the tracking side, we develop the first VI-SLAM evaluation technique that combines the flexibility and control of virtual environments with real inertial data. We use it to perform systematic, quantitative experiments on the effect of visual texture on pose estimation accuracy; through 2000 trials in 20 environments, we reveal the impact of both texture complexity and edge strength. On the human side, we show how virtual reality (VR) can be used to evaluate user satisfaction with environments, and highlight how this can be tailored to AR research and use cases. Finally, we demonstrate our integrated design methodology with a case study on AR museum design, in which we conduct both VI-SLAM evaluations and a VR-based user study of four different museum environments. 

Recent advances in eye tracking have given birth to a new genre of gaze-based context sensing applications, ranging from cognitive load estimation to emotion recognition. To achieve state-of-the-art recognition accuracy, a large-scale, labeled eye movement dataset is needed to train deep learning-based classifiers. However, due to the heterogeneity in human visual behavior, as well as the labor-intensive and privacy-compromising data collection process, datasets for gaze-based activity recognition are scarce and hard to collect. To alleviate the sparse gaze data problem, we present EyeSyn, a novel suite of psychology-inspired generative models that leverages only publicly available images and videos to synthesize a realistic and arbitrarily large eye movement dataset. Taking gaze-based museum activity recognition as a case study, our evaluation demonstrates that EyeSyn can not only replicate the distinct pat-terns in the actual gaze signals that are captured by an eye tracking device, but also simulate the signal diversity that results from different measurement setups and subject heterogeneity. Moreover, in the few-shot learning scenario, EyeSyn can be readily incorporated with either transfer learning or meta-learning to achieve 90% accuracy, without the need for a large-scale dataset for training. 

Meditation, a mental and physical exercise which helps to focus attention and reduce stress has gained more popularity in recent years. However, meditation requires a concerted effort and regular practice. To explore the feasibility of using Augmented Reality (AR) Devices to assist in meditating, we recruited ten subjects to perform a five-minute meditation task integrated into AR devices. Heart Rate, Heart Rate Variability, and skin conductance response (SCR) are analyzed based on an Electrocardiogram (ECG), Electrodermal activity to monitor the physiological changes during and after a meditation session. Additionally, participants filled out surveys containing the Perceived Stress Questionnaire (PSQ), a clinically validated survey designed to evaluate stress levels before and after meditation to analyze the change in stress levels. Finally, we found significant differences in Heart Rate and Mean SCR Recovery Time for participants between the three study procedure periods (before, during, and after guided meditation). 

Mobile augmented reality (AR) has the potential to enable immersive, natural interactions between humans and cyber-physical systems. In particular markerless AR, by not relying on fiducial markers or predefined images, provides great convenience and flexibility for users. However, unwanted virtual object movement frequently occurs in markerless smartphone AR due to inaccurate scene understanding, and resulting errors in device pose tracking. We examine the factors which may affect virtual object stability, design experiments to measure it, and conduct systematic quantitative characterizations across six different user actions and five different smartphone configurations. Our study demonstrates noticeable instances of spatial instability in virtual objects in all but the simplest settings (with position errors of greater than 10cm even on the best-performing smartphones), and underscores the need for further enhancements to pose tracking algorithms for smartphone-based markerless AR. 

Mobile Augmented Reality (AR) provides immersive experiences by aligning virtual content (holograms) with a view of the real world. When a user places a hologram it is usually expected that like a real object, it remains in the same place. However, positional errors frequently occur due to inaccurate environment mapping and device localization, to a large extent determined by the properties of natural visual features in the scene. In this demonstration we present SceneIt, the first visual environment rating system for mobile AR based on predictions of hologram positional error magnitude. SceneIt allows users to determine if virtual content placed in their environment will drift noticeably out of position, without requiring them to place that content. It shows that the severity of positional error for a given visual environment is predictable, and that this prediction can be calculated with sufficiently high accuracy and low latency to be useful in mobile AR applications. 

In this work, we present GazeGraph, a system that leverages human gazes as the sensing modality for cognitive context sensing. GazeGraph is a generalized framework that is compatible with different eye trackers and supports various gaze-based sensing applications. It ensures high sensing performance in the presence of heterogeneity of human visual behavior, and enables quick system adaptation to unseen sensing scenarios with few-shot instances. To achieve these capabilities, we introduce the spatial-temporal gaze graphs and the deep learning-based representation learning method to extract powerful and generalized features from the eye movements for context sensing. Furthermore, we develop a few-shot gaze graph learning module that adapts the `learning to learn' concept from meta-learning to enable quick system adaptation in a data-efficient manner. Our evaluation demonstrates that GazeGraph outperforms the existing solutions in recognition accuracy by 45% on average over three datasets. Moreover, in few-shot learning scenarios, GazeGraph outperforms the transfer learning-based approach by 19% to 30%, while reducing the system adaptation time by 80%.