Anna Ferrarotti

Virtual Reality is gaining increasing relevance as a system to monitor joint kinematics during rehabilitation and as a tool to improve patient adherence to rehabilitation protocols. This study aims to evaluate the performance of Meta Quest 3 and Meta Quest Pro by comparing their tracking accuracy against the Qualisys™ stereophotogrammetric reference system in static and dynamic conditions. In static conditions, translational and rotational accuracy were assessed using two custom-made setups: one assessing displacements along three axes (from 0 to 505 mm) and another covering rotations from 0° to 180°. Dynamic performance was evaluated through three clinically relevant upper-limb movements (flexion, external rotation, and abduction) executed by NAO humanoid robot at three different durations (S₁ = 1s, S₂ = 2s, S₄ = 4s) to simulate varying patient conditions. Results showed a maximum translational mean absolute error of 4.7 ± 2.8 mm at 505 mm along the X-axis and a maximum rotational error of 2.8 ± 1.2° at 90° around the Y-axis for Meta Quest 3. In dynamic conditions, the highest mean absolute percentage error was 17.0 ± 2.9% for Meta Quest 3 during abduction at S₄. Overall, the Meta Quest 3 and Meta Quest Pro, featuring inside-out tracking technology, demonstrate promising accuracy, supporting their potential use as reliable tools for Virtual Reality-based motor rehabilitation.

The evaluation of the quality perceived by a user when experiencing extended reality is a multifaceted challenge. Studies in the state-of-the-art show that it depends on several factors such as user comfort, level of immersivity, cognitive load, and content quality. In addition, extended reality systems are moving towards the realization of collaborative and multi-user applications. In this scenario, additional aspects should be taken into account such as the social presence and the quality of interaction. Currently, to the best of our knowledge, the literature lacks common practices or standards for designing experimental tests that encompass all these aspects. In this work, we aim at providing practical guidelines for assessing the quality of experience in multi-user applications. Therefore, we focus on a flexible task that can be applied to many extended reality technologies. We provide a thorough set of technological scenarios for the application of the proposed methodology and we compare the perceived quality of experience in different technological settings by analyzing the results of a multi-lab subjective study.

Virtual Reality’s rise has highlighted eye gaze as a key interaction method. Data reliability becomes critical in this context, with gaze accuracy and precision serving as leading indicators of data quality. This study compared the spatial accuracy and precision of Meta Quest Pro and HTC Vive Focus 3 headsets using eye movement data collected from 30 users under head-free and head-constrained conditions. The targets were placed at different depths from the users. The analysis revealed inconsistencies between manufacturer-provided data, obtained under ideal conditions, and data collected in different settings. Moreover, the results showed greater spatial accuracy for Meta Quest Pro, and higher spatial precision for HTC Vive Focus 3. The aim of this study is to offer an extensive examination of the performance of these systems, thus assisting researchers in choosing suitable eye-tracking technology for diverse applications.

Training in the Occupational Safety and Health (OSH) sector is crucial for minimizing workplace hazards and ensuring employee well-being. Virtual Reality (VR) emerges as a training tool that can enhance learning outcomes and simulate hazardous scenarios safely. However, several aspects must be taken into consideration when implementing VR-based training solutions. The paper investigates how to effectively design, develop, integrate, and validate a VR OSH training tool. To this aim, a comprehensive guideline of 9 key elements articulated in 29 items is proposed. Every element and item is retrieved from analyzing the existing literature on the topic using a systematic approach. The result is a comprehensive guide to consider all these aspects from the outset of design, in a cohesive, complete, and tailored manner. This formalization is intended to facilitate the advancement of research and implementation of these solutions, which to date have been largely confined to prototypes or lack real practical application.

This demo presents an interactive communication system based on immersive media for analyzing the users’ Quality of Experience in collaborative tasks. The interaction between users is studied in an asymmetric scenario where a peer-to-peer communication has been set up between a PC and a Virtual Reality headset. Two application scenarios have been considered: a Block Building task and a Treasure Hunt game. The two users will cooperate to perform the two tasks. The goal is to study the relation between the type of transmitted information (i.e., audio and video or audio only) and the quality and quantity of interaction. During the demo, participants will have the opportunity to try one of the designed applications.

Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.