Extended reality head-mounted displays are increasingly employed as research instruments for collecting behavioral data, yet logged signals typically traverse multiple processing stages–firmware, proprietary runtimes, APIs, engine plugins, and application logic–before reaching datasets. Each stage can introduce delay, transform signals, or restrict access to raw measurements, meaning identical physical events may appear differently depending on where in this pipeline data are captured. Despite these implications, many studies treat data logging as an implementation detail, often omitting disclosure of access points, software versions, or sampling strategies, thereby compromising reproducibility and cross-study comparability. This paper proposes that extended reality data collection can be treated as a layer-aware design problem. Four contributions are presented: (1) a taxonomy identifying five pipeline layers (hardware/firmware, runtime/middleware, engine/framework, application, and network) with distinct temporal and fidelity characteristics; (2) an analysis of how layer selection affects latency, sampling rates, and signal transformations; (3) practical guidelines for layer-aware data logging; and (4) a concrete instantiation for the Meta Quest Pro, accompanied by an open-source Unity plugin enabling layer-aware logging across modalities. These contributions provide a practical framework for transparent, reproducible, and methodologically rigorous extended reality behavioral data collection.

Populating virtual words with realistic pedestrian behaviours is challenging but essential to ensure natural interactions with users. In pedestrian encounters, successful collision avoidance requires adapting speed and/or locomotor trajectory based on situational- and personal-characteristics. Personal characteristics such as the age of a pedestrian can easily be observed, but whether it affects avoidance behaviours is unknown. This is however an important question to ensure realism and variety of the simulations. The purpose of the current study was to examine the influence of a virtual pedestrian’s (VP) age-related appearance and gait profile on avoidance behaviours during a circumvention task. We expected that older adult (OA) gait characteristics and/or appearance would result in more cautious behaviours. Young adults (YA; n=17, 23.6 ± 2.7yrs) navigated a virtual street using a HMD. Individuals walked 8m towards a goal, while avoiding an approaching VP who would approach and steer towards the participant’s left, right, or continue straight, while exhibiting different age-related appearances and gait profiles: 1. YA appearance, YA gait; 2. OA appearance, OA gait; 3. OA appearance, YA gait; and 4. YA appearance, OA gait. Results indicate that clearance was larger when the appearance of VP resembled an OA, and when the VP walked like an OA compared to a YA. Larger clearance distances observed with OA characteristics may be due to societal norms associated with the principle of parental respect as well as a cautious strategy for any potential instability (wavering) in balance commonly observed in OA. This research sheds light on how age-related cues influence pedestrian interactions, with implications for the design of populated virtual environments.

From education to medicine to entertainment, a wide range of industrial and academic fields now utilize eXtended Reality (XR) technologies. This diversity and growing use are boosting research and leading to an increasing number of XR experiments involving human subjects. The main aim of these studies is to understand the user experience in the broadest sense, such as the user cognitive and emotional states. Behavioral data collected during XR experiments, such as user movements, gaze, actions, and physiological signals constitute precious assets for analyzing and understanding the user experience. While they contribute to overcome the intrinsic flaws of explicit data such as post-experiment questionnaires, the required acquisition and analysis tools are costly and challenging to develop, especially for 6DoF (Degrees of Freedom) XR experiments. Moreover, there is no common format for XR behavioral data, which restrains data-sharing, and thus hinders wide usages across the community, replicability of studies, and the constitution of large datasets or meta-analysis. In this context, we present PLUME, an open-source software toolbox (PLUME Recorder, PLUME Viewer, PLUME Python) that allows for the exhaustive record of XR behavioral data (including synchronous physiological signals), their offline interactive replay and analysis (with a standalone application), and their easy sharing due to our compact and interoperable data format. We believe that PLUME can greatly benefit the scientific community by making the use of behavioral and physiological data available for the greatest, contributing to the reproducibility and replicability of XR user studies, enabling the creation of large datasets, and contributing to a deeper understanding of user experience.

Nonverbal communication is paramount in daily life, as well as in populated virtual reality (VR) environments. In this paper, we focused on gaze behaviour, which is key to initiate and drive social interactions. Previous work on photographs and on virtual agents showed the importance of gaze, even in the presence of multiple stimuli, by demonstrating the stare-in-the-crowd effect: humans detect faster and observe gazes directed towards them longer than the averted ones. While previous studies focused on static scenarios, which fail in representing the complexity of real-life social interactions, we propose to explore the stare-in-the-crowd effect in dynamic situations. To this end, we designed a within-subject experiment where 21 users navigated a virtual street through an idle or moving crowd of virtual agents. Agents’ gaze was manipulated to display averted, directed, or shifting gaze. We analysed the user’s gaze (fixations, dwell time) and locomotor behaviours (path decisions, proximity to agents) as well as their social anxiety. Results showed that the stare-in-the-crowd effect is preserved when walking through both types of crowd, and that social anxiety decreases gaze interaction time and affects proximity behaviours in case of agents with directed gazes. However, virtual agents’ gaze did not elicit significant changes on users’ locomotion. These findings highlight the importance of considering virtual agents’ gaze when creating VR environments, and open future work perspectives to better understand factors that would strengthen or decrease this effect at gaze and locomotor levels.

Human social interactions rely on multisensory cues. In this regard, visual and auditory cues are paramount during the initiation of an interaction. In this preliminary work, we propose an approach to let Intelligent Virtual Agents (IVAs) simulating sound perception capabilities. Our model targets to control IVAs' reactive behaviour through their analysis of perceived other agents' emitted sounds. For that, we explored auditory features close to the human system.

Nonverbal cues are paramount in real-world interactions. Among these cues, gaze has received much attention in the literature. In particular, previous work has shown a search asymmetry between directed and averted gaze towards the observer using photographic stimuli, with faster detection and longer fixation towards directed gaze by the observer. This is known as the stare-in-the-crowd effect. In this study, we investigate whether stare-in-the crowd effect is preserved in Virtual Reality (VR). To this end, we designed a within-subject experiment where 30 human users were immersed in a virtual environment in front of an audience of 11 virtual agents following 4 different gaze behaviours. We analysed the user’s gaze behaviour when observing the audience, computing fixations and dwell time. We also collected the users’ social anxiety score using a post-experiment questionnaire to control for some potential influencing factors. Results show that the stare-in-the-crowd effect is preserved in VR, as demonstrated by the significant differences between gaze behaviours, similarly to what was found in previous studies using photographic stimuli. Additionally, we found a negative correlation between dwell time towards directed gazes and users’ social anxiety scores. Such results are encouraging for the development of expressive and reactive virtual humans, which can be animated to express natural interactive behaviour.

Non-verbal communication body cues are paramount to interact. In this preliminary work, we explore ways to let Intelligent Virtual Agents (IVAs) simulating nonverbal communication capabilities. We propose an approach to control IVAs' reactive behaviour from the analysis of other agents' apparent motions, in a situation of "observed" IVAs that act and "observers" that react. For that, first a viewpoint-driven analysis of the observed agent's motion is done, and then a synthesis of this analysis induces the observers' reaction.

Building Information Modelling (BIM) is an up-and-coming methodology and technology used in the Architecture, Engineering and Construction (AEC) industry, that allows data centralization and stakeholders' collaboration. But to check the accuracy of the work done on the worksite, it is necessary first to go on site and then to modify the BIM model. This paper presents a mixed reality (MR) application based on BIM data and drone videos, allowing off-site construction supervision. It permits to make annotations about differences between what has been planned in BIM and what has been built, using superimposition of the two sources. Then these ones can be transferred to the BIM model for corrections. Finally, we evaluate our work with building construction experts, providing to them a questionnaire to grade the application and to get feedback. Our major result is that as for them the application does really help to do construction supervisions; however, they suggest that the application should provide more interactions with the 3D model and with the videos.

Nowadays, virtual reality (VR) is widely used in the AEC (architecture, engineering and construction) industry. One crucial issue is how to reuse Building Information Modeling (BIM) models in VR applications. This paper presents an approach for a smart adaptation of BIM models for using in VR scene, by following the needs expressed by building projects actors. The main adaptation consists in filtering BIM data to keep the necessary ones for VR, according to the user objectives. Moreover, VR system should be chosen by taking into account the purpose of usage of the VR model. This approach is applied to a study case of a nursery building project.