Hands-free control offers natural and intuitive interaction with devices, particularly in scenarios where traditional input methods are impractical. We introduce an extensible framework that integrates eye tracking, object detection, and gesture recognition to study intended and unintended interactions with Internet of Things (IoT) devices. To develop our framework, we conducted a structured experiment with 9 participants, focusing on identifying natural and intuitive interaction behaviors in different situations. The results showed that users intuitively combined gaze- and head-based gestures, showing the potential of head/gaze combinations as input mechanisms, specifically for directional movements. On this basis, we propose a system for hands-free interaction and control of IoT devices with intuitive gaze- and head-based gestures. We report on our promising findings as well as on limitations with respect to accurately distinguishing intention in real-world conditions. All our code is publicly available, ensuring the reproducibility and extension of our findings.

In pilot training, deviation from standard procedures is a significant concern. To provide student pilots with objective feedback in post-flight debriefing, we captured pilots’ view and gaze with the Pupil Core eye-tracker. Then we conducted a preliminary evaluation to test the feasibility of existing scene segmentation models for gaze-mapping. We used an OpenCV baseline model for coarse inside vs. outside-analysis, a fine-tuned Detectron2 model for specific instrument segmentation, and Segment Anything Models (SAM 2 and SAM 3) for human-in-the-loop analysis. The baseline was fast but fragile, failing in common flight scenarios; the Detectron2 model was powerful but inflexible and unsuitable for general use; and SAM 3 was promising, offering generalizability for post-flight analysis despite noisy digital displays. A qualitative preliminary evaluation of SAM with Visual Flight Rules shows that it can be beneficial in eye movement analysis. We identified poor data quality in bright cockpit environments and ergonomics as main limitations.

Board games often involve strategic decision making and procedural planning tasks. Such tasks require learners to make decisions based on dynamically evolving game state and changing information that is situated in a physical environment. Recommender systems can filter available information and provide learners with personalized and actionable suggestions that simplify their decision making while playing board games. Such recommendations can further be spatially aligned with relevant physical elements through Mixed Reality (MR). We present an MR system called GLAMRec for an engine-building strategy board game. GLAMRec provides personalized, transparent recommendations by integrating user data, real-time game state tracking, and ontology-based reasoning during a complex board game, which we use as a proxy environment for procedural learning tasks. We interviewed six board game designers to improve the GLAMRec and conducted a within-subjects design user study (N=32) to investigate how personalized explanations affect explanation satisfaction, user experience, and trust. We found that personalized recommendations significantly improve explanation satisfaction and hedonic user experience without affecting trust ratings, recommendation compliance, and game performance. These findings suggest that personalization primarily shaped perception of enjoyment rather than measurable learning outcomes or trust.

Personalization of online realities is today ubiquitous to support decision making or reduce information overload.Recently, through the expanding capabilities and pervasiveness of Mixed Reality and Ubiquitous Computing technologies, we observe increasing personalization also of physical reality.This might yield more convenient, efficient and inclusive everyday interactions.However, it may readily lead to serious societal consequences such as the loss of shared worlds and the emergence of perceptual filter bubbles.To mitigate such harms while retaining the benefits of personalization, it is important to understand how ubiquitous personalization systems may operate responsibly.Responding to this need, we propose a conceptual model that overcomes the limitations of established personalization models and expands their applicable scope to physical, virtual, and hybrid environments.We validated our model in relation to existing literature and show how it provides a conceptual foundation for the analysis and study of responsible personalization systems that create individually and societally beneficial Personalized Realities.

Inspired by the concepts of diminishing reality and ad-blocking in browsers, this study investigates the perceived benefits and concerns of blocking physical, real-world content, particularly ads, through Extended Reality (XR). To understand how users perceive this concept, we first conducted a user study (n=18) with an adblocking prototype to gather initial insights. The results revealed a mixed willingness to adopt XR blockers, with participants appreciating aspects such as customizability, convenience, and privacy. Expected benefits included enhanced focus and reduced stress, while concerns centered on missing important information and increased feelings of isolation. Hence, we investigated the user acceptance of different ad-blocking visualizations through a follow-up online survey (n=120), comparing six concepts based on related work. The results indicated that the XR ad-blocker visualizations play a significant role in how and for what kinds of advertisements such a concept might be used, paving the path for future feedback-driven prototyping.

Head-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they provide insights into user attention, intentions, and activities, and allow novel interaction methods based on this information. However, in physical environments, the implications of using gaze-enabled AR for human activity recognition have not been explored in detail. In an experimental study with the Microsoft HoloLens 2, we collected gaze data from 20 users while they performed three activities: Reading a text, Inspecting a device, and Searching for an object. We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved up to 89.6% activity-recognition accuracy. Based on the recognized activity, our system—GEAR—then provides users with relevant AR feedback. Due to the sensitivity of the personal (gaze) data GEAR collects, the system further incorporates a novel solution based on the Solid specification for giving users fine-grained control over the sharing of their data. The provided code and anonymized datasets may be used to reproduce and extend our findings, and as teaching material.

Humans only recognize a few items in a scene at once and memorize three to seven items in the short term. Such limitations can be mitigated using cognitive offloading (e.g., sticky notes, digital reminders). We studied whether a gaze-enabled Augmented Reality (AR) system could facilitate cognitive offloading and improve object retrieval performance. To this end, we developed NeighboAR, which detects objects in a user's surroundings and generates a graph that stores object proximity relationships and user's gaze dwell times for each object. In a controlled experiment, we asked N=17 participants to inspect randomly distributed objects and later recall the position of a given target object. Our results show that displaying the target together with the proximity object with the longest user gaze dwell time helps recalling the position of the target. Specifically, NeighboAR significantly reduces the retrieval time by 33%, number of errors by 71%, and perceived workload by 10%.

Head-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they allow novel interaction methods and provide insights into user attention, intentions, and activities. However, only few studies have used gaze-enabled AR displays for human activity recognition (HAR). In an experimental study, we collected gaze data from 10 users on a HoloLens 2 (HL2) while they performed three activities (i.e., read, inspect, search). We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved an up to 98.7% activity-recognition accuracy. On the HL2, we provided users with an AR feedback that is relevant to their current activity. We present the components of our system (GEAR) including a novel solution to enable the controlled sharing of collected data. We provide the scripts and anonymized datasets which can be used as teaching material in graduate courses or for reproducing our findings.