Aug 2026· Proceedings of the Human Factors and Ergonomics Society Annual Meeting· 0 citations· 19 references
TL;DR
A publicly available, goal-driven, interactive guide called Generalized Eye Tracking Metrics (GEMs), which supports the learning of metrics, relevant terminology, and analysis approaches as it relates to the user’s research questions and experimental setup.
Abstract
Eye tracking is a non-invasive method to measure an individual’s cognitive state and visual attention allocation patterns, both of which provide valuable insight into their real-time needs when completing complex tasks. With the availability of low-cost systems and the wealth of available information, eye tracking is ever more common in human factors research and applications. However, access to comprehensive, approachable, and adaptable information on various eye tracking metrics is limited. Therefore, we created a publicly available, goal-driven, interactive guide called Generalized Eye Tracking Metrics (GEMs). It supports the learning of (1) metrics, (2) relevant terminology, and (3) analysis approaches, as it relates to the user’s research questions and experimental setup. Our findings from focus groups, interviews, and usability testing among novices and experts indicated that GEMs is a highly usable information source created by and for eye tracking researchers.
It is concluded that combining eye tracking and Thinking Aloud is feasible without compromising data quality and meaningful for comprehensive HCI evaluation.
Tina Frenzel, Inka Schmitz, Lisa Kerwien et al.· Message Understanding Confer...· 0 citations
Estimating viewing distance from gaze behavior is essential for understanding user intent and enabling distance-aware interactive systems. However, most existing eye-tracking datasets have been collected in constrained settings, such as laboratory environments or static tasks. Consequently, they only partially capture viewing behaviors in real-world situations where viewing distance changes with natural head and body movements. We introduce GazeDepth, an eye-tracking dataset collected from 19 participants using a wearable tracker during tasks reflecting real-world scenarios. GazeDepth includes fixed-distance viewing scenarios with constant observer-target distances at near (33 cm), middle (50 cm), and far (300 cm), as well as variable-distance viewing scenarios in which participants shift gaze among targets at different depths in indoor and outdoor environments. The dataset provides synchronized gaze data, pupil size, 3D eye-vectors, and head-motion signals, along with distance labels. Statistical analyses showed that distance-related gaze features, such as vergence angle and estimated viewing distance, differed consistently across viewing-distance categories. In addition, classification models trained on GazeDepth further demonstrated that the dataset captures gaze characteristics that distinguish the three viewing-distance categories, supporting gaze-based distance inference and distance-aware interaction in realistic scenarios.
Dohwan Kim, Yejin Choi, Seungbok Lee et al.· 0 citations
To overcome the limitations of conventional design assessment in capturing instant cognition and visual dynamics, this paper proposes a multimodal evaluation method for visual design effectiveness based on eye-tracking technology. By integrating dynamic region-of-interest sequence analysis with retrospective cognitive attribution, the method captures both the spatial flow and temporal dynamics of visual attention. A contextual task is designed to simulate realistic viewing behavior while maintaining experimental control, guiding participants through goal-oriented browsing under standardized presentation conditions. Dynamic region-of-interest sequence analysis is then used to measure gaze retracing rate and evaluate the clarity of information hierarchy. Retrospective interviews and visual entropy analysis are combined to link objective eye-movement trajectories with subjective semantic interpretation. Experimental results show that the method effectively differentiates design effectiveness under different contrast ratios. The high-contrast group has the shortest initial fixation time (180 ± 38 ms) and the lowest gaze retracing rate (26.1 ± 5.9%). Gaze retracing rate is negatively correlated with semantic comprehension accuracy (r = -0.81), supporting continuous evaluation from perception to cognition.
Xinzhen Zhuo, Zhixiong Hu, Yang Lin· Advanced Electromagnetics· 0 citations
Geometric eye trackers can provide the spatial accuracy required for gaze-based interaction and multimodal studies, but their measurements remain sensitive to residual session-specific calibration error. Research on correcting this error is difficult to compare because methods are typically evaluated with different devices, target layouts, and error definitions. We present a calibration-focused dataset containing 163 trials from 12 participants, with separate 18-point fitting and 32-point test grids, and use it to evaluate global, local, and composite correction functions under a common spatial-extrapolation protocol. We further introduce a lightweight neural refiner that combines ranked predictions from complementary calibrators. On this controlled dataset, post-vendor correction reduces the mean angular error from $1.53^\circ$ to $1.03^\circ$ with the strongest classical composite and to $0.96^\circ$ with the refiner. In a closed-loop gaze task, lower residual error is associated with higher performance across four online correction conditions. These results provide a reproducible data-quality benchmark for using gaze as a behavioral signal in interactive modeling.
Jia-Qi Liu, Zi-Xuan Wang, Yuhong Zhang et al.· 0 citations
The ability to comprehend and interpret visualizations has garnered increasing attention in recent years. Individuals with specialized knowledge and experience in a particular field exhibit distinct patterns in how they perceive, process, and retain information related to that area. Extensive research across various domains has demonstrated that expertise confers perceptual and cognitive benefits in engaging with domain-relevant information. Eye-tracking methodology has provided significant insight into some of the perceptual mechanisms underlying performance differences between experts and novices in various professional settings, including aviation, car driving, medicine, arts, cartographic research and sports. The opportunity to access tacit expert knowledge, to improve efficiency of task performance and potentially to enhance safety measures has gained growing attention over the past years. This article conducts a systematic literature review to consolidate literature describing the application of gaze behavior as an adjunct tool to traditional teaching methods. Building on the proposed conceptual framework, we systematically reviewed the training potential of eye movements. Our results reveal that gaze-trained individuals exhibit a performance advantage over traditionally trained counterparts and that the impact and feasibility of expert skills transfer through gaze behaviors are moderated by different factors. We summarize the benefits of using expert gaze as a visual cue (e.g. EMMEs) in terms of attention-modeling processes, human performance and safety in work environment.
Elena Lupia, A. Bortolotti, Riccardo Palumbo· Trends in Psychology· 0 citations
Tracking of the eyes and head have occupied a crucial role in human computer interaction, virtual and augmented reality, accessibility systems and cognitive studies. This paper gives a critical review on gaze estimation and head pose tracking algorithms, data sets, and performance determinants. This article compares classical and deep learning-based methods, such as convolutional neural networks, transformer models, geometric, hybrid, and multi-task learning methods. The article also tests the robustness in the external conditions like blinking, occlusion, and changes in illumination, and cross-subject individual differences. The most important publicly available datasets are compared regarding the type of input, diversity of participants, the size of data, recording distance, and environmental conditions. This article also contrasts new webcam and specialized eye-tracking systems, and, in doing so, point out trade-offs between accuracy, cost and applicability in real-time. The review also stresses on the role of domain adaptation, contrastive learning, and multi-modal inputs in generalizing across domains. In general, the article contains an attempt to present a comprehensive picture of the existing situation to researchers and practitioners to assist them in designing and choosing gaze and head pose estimation systems to be used in various applications.
Khalid Sami Yousif, Emad A. Mohammed· ITEGAM- Journal of Engineeri...· 0 citations
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