Traditional training methods in industrial environments often lack real-time guidance and interactive feedback, which can make knowledge sharing challenging. Augmented Situated Visualization (SV) can improve industrial training by providing in-depth, spatially relevant instructions, which are particularly valuable when safety procedures are crucial. This work describes a tool for SV deployed on a commercial headset and investigates how two different SV patterns, 2D labels and 3D ghosts, impact user experience, workload, discomfort, task completion time, and memory recall in a training scenario for machine maintenance.

Conversational agents are becoming integrated into virtual reality (VR) applications, enabling voice-based interaction within immersive environments. Yet, how users initiate communication with these agents remains an underexplored aspect of interaction design. We present a comparative study of four activation methods for initiating speech-based interaction with a conversational agent in VR. A custom-built prototype and task-oriented dialogue scenario supported a within-subjects study designed to explore how each method influences user experience. Through qualitative analysis of interview data, we identify key trade-offs related to control, naturalness, privacy, and social appropriateness. Participants’ preferences were shaped by situational factors, task demands, and the spatial positioning of the agent. We offer empirical insights and design recommendations for developing adaptive, multimodal, and inclusive conversational interfaces in VR, with broader implications for spatial computing and human-AI interaction.

Sepsis mortality risk prediction has been explored in the machine learning (ML) community as a use case that could benefit from data-driven decision support systems in intensive care units (ICU). However, most of the work focuses on training models to reach a high classification performance, and there are limited efforts on technical artifacts that explore the practical utility of such predictive models. Therefore, we present the tool SepsisVision, an example of a web-based explainable user interface (XUI) to support sepsis mortality risk through static explanatory and interactive exploratory interfaces to navigate the behavior of an ML model through different kinds of explanations: SHAP-based local and global feature attributions, distribution comparison, and counterfactual analysis. Tool available at: https://sepsisvision.streamlit.app/.

Immersive technologies offer advantages for the visualization of and interaction with complex setups within manufacturing maintenance processes. The present work catalogs different applications of AR/VR in manufacturing maintenance practices as an extended version of a workshop paper presented at the International Workshop on eXtended Reality for Industrial and Occupational Supports (XRIOS). Through a scoping review in three computing and engineering digital libraries, we outline the key attributes of immersive solutions (N_p) for industrial maintenance, categorizing functional prototypes with ten parameters related to interaction, visualization, and research methods. Moreover, we conducted a workshop with three manufacturing experts discussing the future of maintenance interfaces. By bringing forth their recommendations and insights, we targeted a key training challenge in maintenance. We designed and implemented a situated visualization prototype for a safety-critical procedure with real-time, in-depth, spatially relevant instructions. We compared the effects of 2D labels and 3D ghosts in a VR-simulated AR environment. In a preliminary between-subjects evaluation study (N_u = 24), we measured usability, workload, simulator sickness, completion time, and delayed recall. Although we did not find statistically significant differences between conditions, 3D ghosts showed slightly lower perceived workload and discomfort levels, along with shorter completion times. On the other hand, 2D labels produced higher usability. Overall, we contribute by a mapping out the state of the art and discovering knowledge gaps within immersive maintenance, presenting a design and preliminary user study that adheres to our recommendations.

Depression remains a prevalent condition in primary care due to its heterogeneous symptoms. To support early screening, this study presents DepreScan, an interactive web-based clinical decision support system powered by machine learning and explainable artificial intelligence. Trained on a representative health survey dataset, DepreScan uses interpretable models and multiple explanation techniques—including SHAP plots and simplified decision trees—to assist healthcare practitioners in screening depression risk. A mixed-methods user study with 16 clinicians assessed the system’s trustworthiness, usability, and perceived clinical utility. Results indicate moderate to high acceptance and trust, particularly for SHAP-based global feature explanations, with differences in measured trust between two related questionnaires. The study highlights the importance of aligning ML explainability with healthcare professionals’ mental models and the findings inform the design of user-centered XAI tools for mental health decision support in primary care.

There is an increasing need to include explainability on the machine learning (ML) models. Among the various approaches, counterfactual (CF) explanations allow the design of what-if scenarios and the interactive exploration of ML model behavior on sensitive decision-making domains. However, the generation of CF for tabular and time-series data requires technical skills that are not always available to the end-users of ML-powered systems. Therefore, we propose a modular web-based tool to easily generate, visualize, and interact with CF on any tabular or time-series dataset. The EXTREMUM platform provides access to state-of-the-art CF algorithms, where users can train ML models and explore CF on their tabular or time-series datasets with an intuitive user interface. The project is instantiated on two tabular datasets within healthcare and five time-series datasets with various domains. The open-source repository lets ML researchers adapt the existing ML tool to new application domains: https://gitea.dsv.su.se/DataScienceGroup/EXTREMUM-demo.

Using spatial audio in digital media improves the user’s cognitive load, sense of embodiment, and perceived immersion. There is a lack of systems that enable interactive manipulation of spatial audio through hand-tracking in see-through head-mounted displays (HMD). We contribute with a novel single-user experience to explore real-time music-making through HMD, proposing a solution to map audio characteristics to hand movements and gestures. This work aims to support studies on audio quality perception and spatial audio design for mixed-reality (MR) applications. Mainly focused on upcoming scenarios where HMD with dynamic spatial mapping must adapt visual and audio feedback when the users move along physical spaces with varying shapes and materials.

Immersive technologies like Mixed Reality (MR) are increasingly explored to support urban monitoring tasks as they may replace physical screens with digital content placed spatially in a room and rendered via headsets. Moreover, they enable new interactions techniques, such as hand and gaze input. However, the selection of moving targets, a crucial task when monitoring urban maps in immersive systems, remains unexplored. We study how two selection techniques (mid-air poke vs. gaze-pinch) influence task performance and subjective experience when selecting moving targets in MR virtual screens within arm's reach. We conducted a user study (N=12) to analyze how three variables (selection technique, data card style, and number of targets) influence task performance and subjective experience in a simulated MR monitoring platform for truck fleet management. Results showed that pinch is preferred for lower task completion times but yields a higher physical workload than gaze-pinch. Our work may help the design when adapting desktop tasks involving low user motion but frequent interaction with virtual elements into MR tasks.

Designing affect-based personalized technology involves dealing with large datasets. Machine learning (ML) algorithms are employed to predict affect and similar human factors from in-game metrics, behavioral patterns, or physiological responses. The classification performance is usually presented as a global point estimate without providing user-specific interpretations. This approach is incompatible with effective personalization in games because it disregards the variability of body responses between players. This paper proposes a methodology to classify subjective human factors from large multimodal data. A public VR dataset (CEAP-360VR) was used to extensively compare three ML classifiers and five feature importance techniques. The produced models could reduce the original feature space by 82% (from 113 to 20 features) without compromising predictive performance (F1 score). A random forest (RF) using forward sequential feature selection (fSFS) yielded the best prediction of binary valence (F1=0.761) and arousal (F1=0.748). Finally, feature importance rankings are discussed with emphasis on global and user-specific patterns that may improve affect recognition. The proposed methodology is envisioned to help game designers and researchers create customized user-centric games and VR experiences inferring possible explanations from multimodal datasets.

Research in cross-reality systems has studied transitional interfaces that let an immersive system tune the degree of virtual objects from physical reality to virtual reality (VR). Portals have been effective metaphors to enable transitions between the real world, mixed reality (MR), and VR. However, there is a lack of implementations that enable co-located collaboration with immersive systems. This paper describes a cross-reality system for synchronous co-located collaborative tasks. It is instantiated in a performative experience where users perform interactive immersive activities. The novelty relies on the possibility of users sharing the physical space but being perceptually separated in different states of the reality-virtuality continuum. This work aims to advance immersive collaborative design with cross-reality interaction and understand how to design participatory performative experiences that can switch between physical and virtual environments.