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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.

Meihe Xu, Jannis Strecker-Bischoff, Clément Guitton, Kenan Bektaş, Aurelia Tamò-Larrieux, and Simon Mayer. 2026. Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload. Behaviour & Information Technology (2026). https://doi.org/10.1080/0144929X.2026.2692098

@article{Xu2026LegallyCompliant,
author = {Xu, Meihe and Strecker-Bischoff, Jannis and Guitton, Cl\'{e}ment and Bekta\c{s}, Kenan and Tam\`{o}-Larrieux, Aurelia and Mayer, Simon},
title = {Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload},
journal = {Behaviour \& Information Technology},
year = {2026},
publisher = {Taylor \& Francis},
doi = {10.1080/0144929X.2026.2692098},
url = {https://doi.org/10.1080/0144929X.2026.2692098},
abstract = {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.},
keywords = {personalisation, privacy policies, personalised law, eye tracking, privacy disclosure, user study}
}

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.

Kenan Bektaş, Tobias Ettling, Simon Mayer, and Jannis Strecker-Bischoff. 2026. Magic Gaze: Enabling Seamless Control of IoT Devices Through Eye Tracking. In 2026 Symposium on Eye Tracking Research and Applications (ETRA ’26), June 01–04, 2026, Marrakesh, Morocco. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3797246.3804834

@inproceedings{10.1145/3797246.3804834,
author = {Bekta\c{s}, Kenan and Ettling, Tobias and Mayer, Simon and Strecker-Bischoff, Jannis},
title = {Magic Gaze: Enabling Seamless Control of IoT Devices Through Eye Tracking},
year = {2026},
isbn = {9798400725197},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3797246.3804834},
doi = {10.1145/3797246.3804834},
abstract = {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 code1 is publicly available, ensuring the reproducibility and extension of our findings.},
booktitle = {Proceedings of the 2026 Symposium on Eye Tracking Research and Applications},
articleno = {91},
numpages = {8},
location = {
},
series = {ETRA '26}
}

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.

Sebastian Oes, Kenan Bektaş, Jannis Strecker-Bischoff, and Simon Mayer. 2026. ClearSkies: A Preliminary Study of Gaze-Mapped Scene Segmentation in Training Aircraft Cockpits. In 2026 Symposium on Eye Tracking Research and Applications (ETRA ’26), June 01–04, 2026, Marrakesh, Morocco. ACM, New York, NY, USA, 3 pages. https://doi.org/10.1145/3797246.3805852

@inproceedings{10.1145/3797246.3805852,
author = {Oes, Sebastian and Bektas, Kenan and Strecker-Bischoff, Jannis and Mayer, Simon},
title = {ClearSkies: A Preliminary Study of Gaze-Mapped Scene Segmentation in Training Aircraft Cockpits},
year = {2026},
isbn = {9798400725197},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3797246.3805852},
doi = {10.1145/3797246.3805852},
abstract = {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-mapping1. 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.},
booktitle = {Proceedings of the 2026 Symposium on Eye Tracking Research and Applications},
articleno = {33},
numpages = {3},
location = {
},
series = {ETRA '26}
}