Immersive videos (IVs) provide 360° environments that create a strong sense of presence and spatial exploration. Unlike traditional videos, IVs distribute information across multiple directions, making comparison cognitively demanding and highly dependent on interaction techniques. With the growing adoption of IVs, effective comparison techniques have become an essential yet underexplored area of research. Inspired by the “sliding” concept in 2D media comparison, we integrate two established comparison strategies from the literature—toggle and side-by-side—to support IV comparison with greater flexibility. For an in-depth understanding of different strategies, we adapt and implement five IV comparison techniques across VR and 2D environments: SlideInVR, ToggleInVR, SlideIn2D, ToggleIn2D, and SideBySideIn2D. We then conduct a user study (N = 20) to examine how these techniques shape users’ perceptions, strategies, and workflows. Our findings provide empirical insights into the strengths and limitations of each technique, underscoring the need to switch between comparison approaches across scenarios. Notably, participants consistently rate SlideInVR and SlideIn2D as the most flexible and favorite methods for IV comparison.

Bare hand interaction in augmented or virtual reality (AR/VR) systems, while intuitive, often results in errors and frustration. However, existing methods, such as a static icon or a dynamic tutorial, can only inform simple and coarse hand gestures and lack corrective feedback. This paper explores various visualizations for enhancing precise hand interaction in VR. Through a comprehensive two-part formative study with 11 participants, we identified four types of essential information for visual guidance and designed different visualizations that manifest these information types. We further distilled four visual designs and conducted a controlled lab study with 15 participants to assess their effectiveness for various single- and double-handed gestures. Our results demonstrate that visual guidance significantly improved users’ gesture performance, reducing time and workload while increasing confidence. Moreover, we found that the visualization did not disrupt most users’ immersive VR experience or their perceptions of hand tracking and gesture recognition reliability.

Significant tool support exists for the development of mixed reality (MR) applications; however, there is a lack of tools for analyzing MR experiences. We elicit requirements for future tools through interviews with 8 university research, instructional, and media teams using AR/VR in a variety of domains. While we find a common need for capturing how users perform tasks in MR, the primary differences were in terms of heuristics and metrics relevant to each project. Particularly in the early project stages, teams were uncertain about what data should, and even could, be collected with MR technologies. We designed the Mixed Reality Analytics Toolkit (MRAT) to instrument MR apps via visual editors without programming and enable rapid data collection and filtering for visualizations of MR user sessions. With MRAT, we contribute flexible interaction tracking and task definition concepts, an extensible set of heuristic techniques and metrics to measure task success, and visual inspection tools with in-situ visualizations in MR. Focusing on a multi-user, cross-device MR crisis simulation and triage training app as a case study, we then show the benefits of using MRAT, not only for user testing of MR apps, but also performance tuning throughout the design process.

Alcoholics Anonymous (AA) is the largest grassroots peer support group for any health condition. While AA meeting attendance is particularly important for people who are newly sober, newcomers often have trouble finding meetings because of a lack of global up-to-date meeting list due to preference for regional autonomy in AA's organizational structure. Detection of regional webpages containing meetings and extraction of day, time, and address of meetings from those pages are essential steps in making the information available and up-to-date in a global meeting list. However, varied structure of the webpages and the meetings pose challenges in achieving the goal with traditional information retrieval methods. In this paper we propose HAIR: a semi-automated human-aided information retrieval technique and explore its potential to solve this problem. We describe future directions in developing this critical tool and discuss major implications of our work in pointing to the importance of context-specific rather than context-agnostic semi-automated in-formation retrieval techniques by conceptualizing the proposed methods and results in a broader context.

Mediated social touch technologies aim to transmit the sense of touch between two or more physically distributed partners. Previous work in CSCW has focused mostly on vibrotactile actuation, though there is also significant recent interest in exploring a wider variety of haptic actuation modalities. In this paper, we explore Shape Memory Alloys as a novel means for constriction and heat activation. We demonstrate the feasibility of this approach by implementing the SqueezeBands system, which augments social gestures over videochat with haptic actuation. We describe an evaluation of the system with 57 pairs of participants, collaborating on tasks either high or low emotional salience. Our results demonstrate that SqueezeBands encourage greater and more diverse demonstrations of touch and that they may be particularly appropriate for easing mental and physical demand in high emotion tasks. We end with a discussion of the opportunities and challenges in leveraging Shape Memory Alloy actuation for mediated social touch.