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.

Many studies have investigated how interpersonal differences between users influence their experience in Virtual Reality (VR) and it is now well recognized that user's subjective experiences and responses to the same VR environment can vary widely. In this study, we focus on player traits, which correspond to users' preferences for game mechanics, arguing that players react differently when experiencing VR scenarios. We developed three scenarios in the same VR environment that rely on different game mechanics, and evaluate the influence of the scenarios, the player traits and the time of practice of the VR environment on users' perceived flow. Our results show that 1) the type of scenario has an impact on specific dimensions of flow; 2) the scenarios have different effects on flow depending on the order they are performed, the flow preconditions being stronger when performed at last; 3) almost all dimensions of flow are influenced by the player traits, these influences depending on the scenario, 4) the Aesthetic trait has the most influences in the three scenarios. We finally discuss the findings and limitations of the present study that we believe have strong implications for the design of scenarios in VR experiences.

With the ever-growing use of extended reality (XR) technologies, researchers seek to understand the factors leading to a higher quality of experience. Measurements of the quality of experience (e.g., presence, immersion, flow) are usually assessed through questionnaires completed after the experience. To cope with shortcomings and limitations of this kind of assessment, some recent studies tend to use physiological measures and interaction traces generated in the virtual world in replacement or in addition to questionnaires. Those physiological and behavioral measurements are still complex to implement, and existing studies difficult to replicate, because of a lack of easy and efficient tools to collect and visualize such data produced during XR experiments. In this paper, we present XRE-cho, a Unity package that allows for the recording, replaying and visualization of user behavior and interactions during XR sessions; recorded data allows to replay the whole experience, as it includes movements of the XR device, controllers and interacted objects, as well as eye tracking data (e.g., gaze position, pupils diameter). The capabilities of this tool are illustrated in a user study, where 12 participants' data have been collected and visualized with XREcho. Source code for XREcho is publicly available on GitHub.1