Multi-user virtual and mixed reality systems require understanding how users behave, interact, and collaborate within these spaces. Single-user approaches focus on recognizing an individual's actions in isolation. However, multi-user scenarios require multiple participants to understand and react to each other's actions in a shared environment. In this paper, a scoping review was conducted to identify cue-based approaches for user activity recognition in multi-user VR and MR systems. The scoping review included 20 studies and explores how cues are developed and integrated in systems, their effect on user experience, and what opportunities and challenges exist in design and integration of cue-based approaches for user activity recognition. 

Numerous individuals grapple with chronic or recurrent pain, prompting the exploration for non-pharmacological remedies in human-computer interaction. Redirecting attention from nociceptive signals can effectively alleviate pain, but the path through fine tactile perception is rarely explored. This paper introduce Electronic Tactile Flow, a novel interaction paradigm utilizing an 8*8 electrotactile array to modulate pain through cognitive engagement and Flow theory. We investigated the analgesic effects of top-down (goal-directed) versus bottom-up (stimulus-driven) attention. Results from our user study (N=42) indicate that top-down engagement significantly reduces pain perception compared to passive stimulation. Furthermore, we implemented an adaptive difficulty mechanism that sustains users in an optimal Flow state, which was found to amplify pain relief and immersion. This work presents the first integration of electrotactile interfaces with attentional modulation, offering a promising framework for designing personalized, cognitively interactive pain interventions in HCI. 

Traditional network resource management methods primarily rely on network key performance indicators to allocate resources, often neglecting user experience and interaction quality. However, in immersive XR applications, real-time user interaction and quality of experience (QoE) significantly impact resource requirements. Therefore, we propose an event-based adaptive multi-user XR network resource management system that considers both real-time network conditions and user interaction information. The Azure Digital Twins (ADT) platform creates twins for both users and the network, enabling real-time data updates. The system also employs event-based Azure Functions to generate priority signals and write the generated priority decisions back to the ADT model. This ADT architecture is modular and scalable, reflecting the state of the multi-user XR environment in near real-time, thereby enabling user-aware priority decisions for future network resource management. Experimental results show an average control-loop reaction time of 1-2 seconds under a simple decision logic. This outlines the technical requirements for event-based network resource management in MU-XR scenarios and demonstrates, through a small-scale implementation on Microsoft Azure, how such a mechanism can be realized in practice. 

Integrating artificial intelligence (AI) with immersive analytics (IA) represents a promising means of leveraging advanced computational techniques to enhance data visualization and analysis. This study examines the state-of-the-art of AI-IA integration by addressing three key research issues: the significant application domains, the AI techniques used and their combinations, and current challenges and future directions. Results of reviewing 43 relevant studies reveal that AI-IA integration is still in its early stages, as existing research has mainly focused on a limited range of data types and application scenarios. By analyzing the application domains, this systematic literature review supports previous findings of important applications in the fields of education, manufacturing, and healthcare. At the same time, it identifies emerging applications that have progressed from XR and AI domains to AI-IA integration, such as sports events, assistive systems, urban planning, and disaster management. We contribute to extending established visual analytics (VA) pipelines into XR environments with integrated AI techniques. AI techniques are identified as contributing in five ways to this IA pipeline. Our contribution also includes identifying four key challenges and seven opportunities for future exploration. The review concludes that combining AI and IA holds the potential to create innovative applications using advanced AI and immersive visualization techniques. We present an overview of these applications and address key issues for future development. 

Users are more likely to experience visually induced motion sickness (VIMS) during passive motion in virtual reality (VR), particularly in passive virtual driving scenarios. To solve this challenge, we propose a Multi-sensory Simulation of Wind Sensation (MSSWS) method to alleviate VIMS symptoms by enhancing the user's sense of embodiment (SoE). The method utilizes low-fidelity airflow simulation to align vestibular perception with visual, auditory, and tactile cues. Then, we developed an interactive wind simulation helmet that can generate low-fidelity airflow along four directional axes around the user's head, and implemented a passive virtual motorcycle riding environment with synchronized visual and auditory feedback matching the airflow patterns. A user experiment was conducted to systematically evaluate the effectiveness of MSSWS in enhancing SoE and reducing VIMS under various conditions, including helmet activation states, speed variations, and movement directions, with additional validation conducted in a Cave Automatic Virtual Environment (CAVE). Experimental results show that MSSWS significantly enhances sense of presence and SoE while substantially reducing VIMS during passive navigation. Notably, MSSWS shows significantly greater effects on VIMS in high-risk conditions characterized by rapid speed changes and multi-directional movement. 

This study investigates the application of Nielsen’s 10 usability heuristics in the design of digital Sustainable Product Development (SPD) tools using a participatory design approach. Participants included representatives from three companies and SPD experts from academia. During the workshops, the ten usability criteria were presented, after which participants engaged in a brainstorming session to generate practical recommendations for SPD tool design based on their professional experience. The results suggest that SPD practitioners consider these heuristics relevant for SPD tool development. Future research will evaluate the effectiveness of integrating these guidelines into the design process and ongoing improvements to SPD tools.

Introduction Extended Reality (XR) applications have been widely adopted in gaming, healthcare, professional training, and social interaction. Among them, networked multi-user XR (MU-XR) systems are increasingly important. They allow users to communicate and collaborate remotely by streaming real-time data. Moreover, MU-XR systems are becoming increasingly interactive, offering a variety of modes and sensors for user interaction and even allowing users to share their social engagements. Hence, global network conditions increasingly shape user experience (UX). However, current performance engineering remains largely network-centric and predominantly evaluates network capabilities from an individual-user perspective, thereby overlooking how global network resource management (NRM) affects multi-user interaction, multi-user UX, and immersion.

Methods This has led to a lack of a unified framework that can connect multi-user experiences from an application perspective with underlying NRM. To address this gap, this paper conducts a systematic literature review (SLR) of 56 studies published between 2015 and 2025, analyzing UX evaluation methods, multi-user network performance metrics, and NRM-related network functions.

Results The findings reveal that most studies focus on latency minimization to ensure a real-time, immersive collaborative experience, while only a few address network-level support for resource allocation and synchronization for user groups that require immersion. Among the UX evaluation methods, only 6 of the 56 studies conducted user studies, indicating a clear lack of empirical support.

Discussion Furthermore, this SLR proposes a new model for structuring NRM requirements across various MU-XR scenarios, starting at the XR application level, identifying immersion techniques at the XR level, and connecting them to global network performance, aiming to bridge MU-XR applications and their NRM requirements.

High Dynamic Range (HDR) panoramic video is crucial to enhance immersive experience in Virtual Reality (VR). However, a hurdle is that panoramic cameras often struggle with limited dynamic range and motion blur. Inspired by the event-driven sensing of the human eye, this paper explores the potential of the event cameras to enhance panoramic HDR video reconstruction. As a pioneering research endeavor, we first starts by designing a novel hybrid imaging platform equipped with preprocessing pipelines for event-panorama synchronization, alignment, and HDR ground truth generation. Based on the platform, we then introduce Ev-Pano, the first event-panorama HDR video covering diverse indoor scenes for panoramic HDR video reconstruction. We hope Ev-Pano will establish a foundation to support event-guided panoramic HDR imaging and VR research community. With Ev-Pano, we further propose a novel approach that employs a weighting function-based luminance fusion to enable events to recover missing textures in LDR panoramic videos for panoramic HDR video reconstruction. We conduct extensive experiments to demonstrate the effectiveness of our approach. The results show the best performance of ours than prior arts. Meanwhile, a user study on an HDR-capable head-mounted display (Apple Vision Pro) shows feasible perceptual quality (which is closer to the HDR ground truth) of the reconstructed panoramic HDR videos. The codes and part of the dataset can be accessed via the anonymized link https://anonymous.4open.science/r/Ev-Pano-D2D2/. 

Multi-user extended reality (XR) is an advanced technology that immerses users simultaneously into the meta-verse, aiming to provide a real-time interaction experience in different scenarios. However, different users' network conditions cause data transmission to be asynchronous. It forces users' behavior in the metaverse to go out of sync, seriously impacting the user experience. This paper presents a framework for human-centred synchronization of networks and metaverse applications, often denoted as Intelligent Realities, using Digital Twin (DT) technologies for the networks (network DTs) as well as for the individual users' XR parts (XR DTs). Our framework uses users' attention as a key input to dynamically allocate network resources and align content, thereby keeping widely distributed users in sync. To motivate this framework, we surveyed recent DT-based XR and network systems and found a significant gap: few studies work on synchronizing systems between network DTs and XR DTs. This highlights the need to explore integrated solutions for networked real-time multi-user interaction in the metaverse. 

Extended reality (XR) technologies are increasingly being used in different application areas. One such area is for healthcare, which has seen significant developments over the last few years. However, its use for healthcare education is still in its infancy. This paper presents a case study, which explores the use of virtual reality (VR) technology in the healthcare domain. In particular, an application targeting education of various conditions healthcare providers might meet in older adult care is evaluated using different subjective evaluations methodologies, with nursing students and professional healthcare staff. The overall results show promising directions and use of new technology applications in this domain, but also highlights some of the potential problems with its adoption.