Kenan Bektas
Title
Dr.
Last Name
Bektas
First name
Kenan
Email
kenan.bektas@unisg.ch
ORCID
Phone
+41 71 224 27 63
31 results
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Item type:Publication, Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload(Taylor and Francis (United Kingdom), 2026-06-30); ; Privacy policies function as both legal documents and information sources for users, but their length and complexity often discourage engagement. In this paper, we investigate whether a personalised approach can address this issue by prioritising information that concerns individual users most while maintaining a policy’s legal compliance on disclosure. We first explored whether personal characteristics can be used to predict a person’s most concerned category and, hence, serve as a baseline for personalisation. We then conducted an eye-tracking experiment and interviews (n = 30) to understand the effectiveness of personalised reordering of privacy policies. In the interviews, many participants perceived personalised reordering as helpful, although others raised concerns about the invasion of privacy through this personalisation. The eye-tracking results indicate that personalised reordering leads to higher engagement for the first few sentences of a privacy policy. Based on our findings, we present design recommendations for creating legally compliant forms of privacy disclosures that encourage user engagement as well as discussions and implications on privacy disclosure compliance.Type:journal articleJournal:Behaviour & Information Technology - Some of the metrics are blocked by yourconsent settings
Item type:Publication, NeighboAR: Efficient Object Retrieval using Proximity-and Gaze-based Object Grouping with an AR SystemHumans 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%.Type:journal articleJournal:Proceedings of the ACM on Human-Computer InteractionVolume:8Issue:ETRADOI:10.1145/3655599Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gaze-enabled activity recognition for augmented reality feedback(2024-03-16); ; ; ; Andrew DuchowskiHead-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.Type:journal articleJournal:Computers & GraphicsVolume:119Issue:Special Section on Eye Gaze VISAScopus© Citations 24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MR Object Identification and Interaction: Fusing Object Situation Information from Heterogeneous Sources(ACM, 2023-09-28); ; The increasing number of objects in ubiquitous computing environments creates a need for effective object detection and identification mechanisms that permit users to intuitively initiate interactions with these objects. While multiple approaches to such object detection-including through visual object detection, fiducial markers, relative localization, or absolute spatial referencing-are available, each of these suffers from drawbacks that limit their applicability. In this paper, we propose ODIF, an architecture that permits the fusion of object situation information from such heterogeneous sources and that remains vertically and horizontally modular to allow extending and upgrading systems that are constructed accordingly. We furthermore present BLEARVIS, a prototype system that builds on the proposed architecture and integrates computer-vision (CV) based object detection with radio-frequency (RF) angle of arrival (AoA) estimation to identify BLE-tagged objects. In our system, the front camera of a Mixed Reality (MR) head-mounted display (HMD) provides a live image stream to a vision-based object detection module, while an antenna array that is mounted on the HMD collects AoA information from ambient devices. In this way, BLEARVIS is able to differentiate between visually identical objects in the same environment and can provide an MR overlay of information (data and controls) that relates to them. We include experimental evaluations of both, the CV-based object detection and the RF-based AoA estimation, and discuss the applicability of the combined RF and CV pipelines in different ubiquitous computing scenarios. This research can form a starting point to spawn the integration of diverse object detection, identification, and interaction approaches that function across the electromagnetic spectrum, and beyond.Type:journal articleJournal:Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)Volume:7Issue:3DOI:10.1145/3610879Scopus© Citations 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The systematic evaluation of an embodied control interface for virtual reality(PLOS ONE, 2021-12-07); Scopus© Citations 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Telelife: The Future of Remote LivingIn recent years, everyday activities such as work and socialization have steadily shifted to more remote and virtual settings. With the COVID-19 pandemic, the switch from physical to virtual has been accelerated, which has substantially affected almost all aspects of our lives, including business, education, commerce, healthcare, and personal life. This rapid and large-scale switch from in-person to remote interactions has exacerbated the fact that our current technologies lack functionality and are limited in their ability to recreate interpersonal interactions. To help address these limitations in the future, we introduce “Telelife,” a vision for the near and far future that depicts the potential means to improve remote living and better align it with how we interact, live and work in the physical world. Telelife encompasses novel synergies of technologies and concepts such as digital twins, virtual/physical rapid prototyping, and attention and context-aware user interfaces with innovative hardware that can support ultrarealistic graphics and haptic feedback, user state detection, and more. These ideas will guide the transformation of our daily lives and routines soon, targeting the year 2035. In addition, we identify opportunities across high-impact applications in domains related to this vision of Telelife. Along with a recent survey of relevant fields such as human-computer interaction, pervasive computing, and virtual reality, we provide a meta-synthesis in this paper that will guide future research on remote living.Type:journal articleScopus© Citations 35 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Personalized Recommendations in Mixed Reality Enhance Explanation Satisfaction and Hedonic User Experience in Board Game Learning(Association for Computing Machinery, 2026-03-23); ; 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.Type:conference paperScopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Studying Webcam-based Gaze Estimation and Mouse Coordination for Cognitive Inferences(A, 2026-06-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ClearSkies: A Preliminary Study of Gaze-Mapped Scene Segmentation in Training Aircraft Cockpits(2026-06-01); ; In pilot training, deviation from standard procedures is a significant concern. To provide student pilots with objective feedback in postflight 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 gazemapping1. 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.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Eye Tracking Study on the Effects of Dark and Light Themes on User Performance and Workload(2025-05-26); ; ; The visual theme of a dashboard, whether light or dark, is a prominent design choice with potential implications for user experience. This research investigates the effect of visual theme on user performance and workload during decision-making tasks on dashboards. In a within-subjects experiment, we measured the effect of dark and light themes and task complexity (easy, medium and hard), on task completion time, accuracy, confidence, fixation counts, pupil dilation, and workload. The dark mode improves accuracy, confidence, and average fixation count for medium task complexity levels, suggesting its utility in specific scenarios. In dark mode, the relative pupil dilation was higher, but the perceived workload was lower than in light mode. These findings highlight the need to study the interrelation between objective workload measurements and subjective questionnaires. This study advances empirical foundation for theme selection in data-driven interfaces of varying complexity.Type:conference paper