Using Eye-Tracking Insights to Redesign Maritime Navigation Instruments

Grbić, L.; Koren, M.; Mišlov, I.; Lovnički, K. · 2026 · Crossref

DOI: 10.5821/mt.15924

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the critical issue of human–machine interaction on modern ship bridges, specifically focusing on the ergonomic deficiencies of Integrated Navigation Systems (INS) and Electronic Chart Display and Information Systems (ECDIS). As maritime traffic increases and vessel technology becomes more sophisticated, the safe performance of navigators relies heavily on interface design. The authors argue that while functional capabilities of these systems have advanced, their ergonomic design has not kept pace, leading to inconsistencies such as poor symbol placement, inadequate color contrast, inefficient alerting systems, and visual clutter. These design flaws can cause navigators to overlook critical data or misinterpret vessel positions, thereby increasing the risk of navigational error. The research is motivated by the need to identify specific ergonomic modifications that can mitigate these risks, filling a gap in the current understanding of how interface design directly contributes to human error. The study employs a rigorous experimental design using a full-mission bridge simulator to collect eye-tracking data from navigators performing realistic tasks. Participants wear eye-tracking glasses that record gaze position, fixation duration, saccadic transitions, scanpath distribution across critical Areas of Interest (including ECDIS, radar, conning displays, and external views), and pupillary responses indicative of cognitive workload. These physiological metrics are analyzed alongside task performance indicators, such as route monitoring accuracy, alarm acknowledgment time, collision-avoidance decision-making efficiency, and the ability to detect chart dangers. The experimental scenarios are developed based on commonly reported ergonomic challenges in professional practice, such as overlapping shading layers and difficulty distinguishing depth contours. The study also aims to compare performance differences between experienced and novice navigators to determine which ergonomic flaws disproportionately affect less experienced officers. The paper outlines the research design and expected analytical approach rather than presenting final empirical results, as the experimental phase is currently ongoing. The authors anticipate that the analysis will reveal specific mechanisms through which interface design contributes to human error. Expected outcomes include detailed ergonomic redesign proposals for navigational equipment. By implementing these modifications, manufacturers can potentially reduce the probability of human error. Furthermore, the findings are intended to inform maritime academies, allowing them to integrate these insights into simulator-based training to enhance situational awareness and cognitive resilience among trainees. The significance of this work lies in its application of human-centered design principles to maritime navigation, an area where standardized ergonomic guidelines remain underdeveloped. By linking eye-tracking insights with navigator feedback and performance outcomes, the study seeks to provide evidence-based recommendations for improving navigation instrument ergonomics. This approach not only addresses immediate safety concerns but also contributes to the broader field of maritime education and training by highlighting the impact of interface design on cognitive load and decision-making efficiency. The research underscores the importance of aligning technological advancements with human factors to ensure safe and effective maritime operations.

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StageOutcomeToolModelPromptAttemptsCompleted
discover success Crossref 1 2026-08-09
archive success canonical_url 1 2026-08-09
extract success pdftotext 4 2026-08-10
clean success clean 2 2026-08-10
chunk success chunk 2 2026-08-10
embed success embed Qwen/Qwen3-Embedding-8B 2 2026-08-10
promote success 1 2026-08-09
summarize success llm qwen3.6-27b-nvidia summ-v5 2 2026-08-10
tag success vector_similarity 17 2026-08-11
verify success 2 2026-08-10

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

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