Tobias Ettling
Title
M.Sc. HSG
Last Name
Ettling
First name
Tobias
Email
tobias.ettling@unisg.ch
ORCID
Phone
+41 71 224 7736
2 results
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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 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Magic Gaze: Enabling Seamless Control of IoT Devices Through Eye Tracking(2026-06-01); ; ; 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.Type:conference contribution