Wearable AI systems aim to provide timely assistance in daily life, but existing approaches often rely on user initiation or predefined task knowledge, neglecting users' current mental states.We introduce ProMemAssist, a smart glasses system that models a user's working memory (WM) in real-time using multi-modal sensor signals.Grounded in cognitive theories of WM, our system represents perceived information as memory items and episodes with encoding mechanisms, such as displacement and interference.This WM model informs a timing predictor that balances the value of assistance with the cost of interruption.In a user study with 12 participants completing cognitively demanding tasks, ProMemAssist delivered more selective assistance and received higher engagement compared to an LLM baseline system.Qualitative feedback highlights the benefits of WM modeling for nuanced, context-sensitive support, offering design implications for more attentive and useraware proactive agents.

During free exploration of an unknown virtual scene, users often miss important parts, leading to incorrect or incomplete environment knowledge and a potential negative impact on performance in later tasks. This is addressed by wayfinding aids such as compasses, maps, or trails, and automated exploration schemes such as guided tours. However, these approaches either do not actually ensure exploration success or take away control from the user. Therefore, we present an interactive assistance interface to support exploration that guides users to interesting and unvisited parts of the scene upon request, supplementing their own, free exploration. It is based on an automated analysis of object visibility and viewpoint quality and is therefore applicable to a wide range of scenes without human supervision or manual input. In a user study, we found that the approach improves users' knowledge of the environment, leads to a more complete exploration of the scene, and is also subjectively helpful and easy to use.

Viewpoint quality estimation methods allow the determination of the most informative position in a scene. However, a single position usually cannot represent an entire scene, requiring instead a set of several viewpoints. Measuring the quality of such a set of views, however, is not trivial, and the computation of an optimal set of views is an NP-hard problem. Therefore, in this work, we propose three methods to estimate the quality of a set of views. Furthermore, we evaluate three approaches for computing an approximation to the optimal set (two of them new) regarding effectiveness and efficiency.

The manual adjustment of travel speed to cover medium or large distances in virtual environments may increase cognitive load, and manual travel at high speeds can lead to cybersickness due to inaccurate steering. In this work, we present an approach to quickly pass regions where the environment does not change much, using automated suggestions based on the computation of common visibility. In a user study, we show that our method can reduce cybersickness when compared with manual speed control.

Orientation and wayfinding in architectural Immersive Virtual Environments (IVEs) are non-trivial, accompanying tasks which generally support the users' main task. World in Miniatures (WIMs) - essentially 3D maps containing a scene replica - are an established approach to gain survey knowledge about the virtual world, as well as information about the user's relation to it. However, for large-scale, information-rich scenes, scaling and occlusion issues result in diminishing returns. Since there typically is a lack of standardized information regarding scene decompositions, presenting the inside of self-contained scene extracts is challenging. Therefore, we present an automatic WIM generation workflow for arbitrary, realistic in- and outdoor IVEs in order to support users with meaningfully selected and scaled extracts of the IVE as well as corresponding context information. Additionally, a 3D user interface is provided to manually manipulate the represented extract.

To use the full potential of immersive data analysis when wearing a head-mounted display, the user has to be able to navigate through the spatial data. We collected, developed and evaluated 5 different handsfree navigation methods that are usable while seated in the analyst's usual workplace. All methods meet the requirements of being easy to learn and inexpensive to integrate into existing workplaces. We conducted a user study with 23 participants which showed that a body leaning metaphor and an accelerometer pedal metaphor performed best within the given task.

When moving through a tracked immersive virtual environment, it is sometimes useful to deviate from the normal one-to-one mapping of real to virtual motion. One option is the application of rotation gain, where the virtual rotation of a user around the vertical axis is amplified or reduced by a factor. Previous research in head-mounted display environments has shown that rotation gain can go unnoticed to a certain extent, which is exploited in redirected walking techniques. Furthermore, it can be used to increase the effective field of regard in projection systems. However, rotation gain has never been studied in CAVE systems, yet. In this work, we present an experiment with 87 participants examining the effects of rotation gain in a CAVE-like virtual environment. The results show no significant effects of rotation gain on simulator sickness, presence, or user performance in a cognitive task, but indicate that there is a negative influence on spatial knowledge especially for inexperienced users. In secondary results, we could confirm results of previous work and demonstrate that they also hold for CAVE environments, showing a negative correlation between simulator sickness and presence, cognitive performance and spatial knowledge, a positive correlation between presence and spatial knowledge, a mitigating influence of experience with 3D applications and previous CAVE exposure on simulator sickness, and a higher incidence of simulator sickness in women.

In this work, we present an approach for tracking the feet of multiple users in CAVE-like systems with under-floor projection. It is based on low-cost consumer cameras, does not require users to wear additional equipment, and can be installed without modifying existing components. If the brightness of the floor projection does not contain too much variation, the feet of several people can be reliably tracked and assigned to individuals.

In this work, we report on a pilot study we conducted, and on a study design, to examine the effects and applicability of rotation gain in CAVE-like virtual environments. The results of the study will give recommendations for the maximum levels of rotation gain that are reasonable in algorithms for enlarging the virtual field of regard or redirected walking.