Relevant bibliographies by topics / Methods of eye tracking

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Academic literature on the topic 'Methods of eye tracking'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Methods of eye tracking"

1

Narcizo, Fabricio Batista, Fernando Eustáquio Dantas dos Santos, and Dan Witzner Hansen. "High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods." Vision 5, no. 3 (2021): 41. http://dx.doi.org/10.3390/vision5030041.

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This study investigates the influence of the eye-camera location associated with the accuracy and precision of interpolation-based eye-tracking methods. Several factors can negatively influence gaze estimation methods when building a commercial or off-the-shelf eye tracker device, including the eye-camera location in uncalibrated setups. Our experiments show that the eye-camera location combined with the non-coplanarity of the eye plane deforms the eye feature distribution when the eye-camera is far from the eye’s optical axis. This paper proposes geometric transformation methods to reshape the eye feature distribution based on the virtual alignment of the eye-camera in the center of the eye’s optical axis. The data analysis uses eye-tracking data from a simulated environment and an experiment with 83 volunteer participants (55 males and 28 females). We evaluate the improvements achieved with the proposed methods using Gaussian analysis, which defines a range for high-accuracy gaze estimation between −0.5∘ and 0.5∘. Compared to traditional polynomial-based and homography-based gaze estimation methods, the proposed methods increase the number of gaze estimations in the high-accuracy range.
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Li, Zhaowei, Peiyuan Guo, and Chen Song. "A Review of Main Eye Movement Tracking Methods." Journal of Physics: Conference Series 1802, no. 4 (2021): 042066. http://dx.doi.org/10.1088/1742-6596/1802/4/042066.

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Sangeetha, S. K. B. "A survey on Deep Learning Based Eye Gaze Estimation Methods." September 2021 3, no. 3 (2021): 190–207. http://dx.doi.org/10.36548/jiip.2021.3.003.

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In recent years, deep-learning systems have made great progress, particularly in the disciplines of computer vision and pattern recognition. Deep-learning technology can be used to enable inference models to do real-time object detection and recognition. Using deep-learning-based designs, eye tracking systems could determine the position of eyes or pupils, regardless of whether visible-light or near-infrared image sensors were utilized. For growing electronic vehicle systems, such as driver monitoring systems and new touch screens, accurate and successful eye gaze estimates are critical. In demanding, unregulated, low-power situations, such systems must operate efficiently and at a reasonable cost. A thorough examination of the different deep learning approaches is required to take into consideration all of the limitations and opportunities of eye gaze tracking. The goal of this research is to learn more about the history of eye gaze tracking, as well as how deep learning contributed to computer vision-based tracking. Finally, this research presents a generalized system model for deep learning-driven eye gaze direction diagnostics, as well as a comparison of several approaches.
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Meksuła, Dariusz, Konrad Mikołajczyk, Małgorzata Plechawska-Wójcik, and Magdalena Borys. "The use of eye-tracking methods for evaluation of responsive web application." Journal of Computer Sciences Institute 6 (March 30, 2018): 20–27. http://dx.doi.org/10.35784/jcsi.634.

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The purpose of this article is to present the eye-tracking method in the context of evaluation of responsive web application dedicated to manage students practices. To determine usability of the application the eye-tracking method has been chosen due to its high objectivity. Using this method one can unequivocally determine the user gaze direction, which interface elements enchained his attention and which ones were completely skipped. As part of the work were prepared scenarios with tasks for the research participants, questionnaires and time of the tasks execution where measured. Collected data were presented as heat maps and scanpaths. The paper covers also actual review of bibliography of the eye-tracking area and detailed description of the eye-tracking method. What is more the paper gives description of test subject, conducted test and discusses results.
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Vass, Caroline, Dan Rigby, Kelly Tate, Andrew Stewart, and Katherine Payne. "An Exploratory Application of Eye-Tracking Methods in a Discrete Choice Experiment." Medical Decision Making 38, no. 6 (2018): 658–72. http://dx.doi.org/10.1177/0272989x18782197.

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Background. Discrete choice experiments (DCEs) are increasingly used to elicit preferences for benefit-risk tradeoffs. The primary aim of this study was to explore how eye-tracking methods can be used to understand DCE respondents’ decision-making strategies. A secondary aim was to explore if the presentation and communication of risk affected respondents’ choices. Method. Two versions of a DCE were designed to understand the preferences of female members of the public for breast screening that varied in how risk attributes were presented. Risk was communicated as either 1) percentages or 2) icon arrays and percentages. Eye-tracking equipment recorded eye movements 1000 times a second. A debriefing survey collected sociodemographics and self-reported attribute nonattendance (ANA) data. A heteroskedastic conditional logit model analyzed DCE data. Eye-tracking data on pupil size, direction of motion, and total visual attention (dwell time) to predefined areas of interest were analyzed using ordinary least squares regressions. Results. Forty women completed the DCE with eye-tracking. There was no statistically significant difference in attention (fixations) to attributes between the risk communication formats. Respondents completing either version of the DCE with the alternatives presented in columns made more horizontal (left-right) saccades than vertical (up-down). Eye-tracking data confirmed self-reported ANA to the risk attributes with a 40% reduction in mean dwell time to the “probability of detecting a cancer” ( P = 0.001) and a 25% reduction to the “risk of unnecessary follow-up” ( P = 0.008). Conclusion. This study is one of the first to show how eye-tracking can be used to understand responses to a health care DCE and highlighted the potential impact of risk communication on respondents’ decision-making strategies. The results suggested self-reported ANA to cost attributes may not be reliable.
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Baghdadi, Golnaz, and Ali Motie Nasrabadi. "Investigating classification methods to improve eye-tracking systems operation." International Journal of Biomedical Engineering and Technology 10, no. 2 (2012): 145. http://dx.doi.org/10.1504/ijbet.2012.049365.

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7

Goldberg, Joseph H. "Eye Movement-Based Interface Evaluation: What can and Cannot be Assessed?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 37 (2000): 625–28. http://dx.doi.org/10.1177/154193120004403721.

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Interface evaluation by eye tracking-derived data is discussed in this review and synthesis paper. While analysis of eye movements during interface use is becoming more popular, there is little basis for justification of eye tracking methods. A review of traditional interface assessment methods and criteria is provided, to establish areas where eye tracking may potentially impact interface evaluations. Studies are then reviewed, that have used eye tracking-derived measures for performance assessment as interfaces are manipulated. A synthesis is then provided, suggesting that eye tracking-based analysis could have a positive impact in evaluations of consistency, resources, visual clarity, and flexibility, and should have difficulty in ascertaining interface compatibility and locus of control.
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Wilson, Katie A., Pamela L. Heinselman, and Ziho Kang. "Exploring Applications of Eye Tracking in Operational Meteorology Research." Bulletin of the American Meteorological Society 97, no. 11 (2016): 2019–25. http://dx.doi.org/10.1175/bams-d-15-00148.1.

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Abstract Eye-tracking technology can observe where and how someone’s eye gaze is directed, and therefore provides information about one’s attention and related cognitive processes in real time. The use of eye-tracking methods is evident in a variety of research domains, and has been used on few occasions within the meteorology community. With the goals of Weather Ready Nation in mind, eye-tracking applications in meteorology have so far supported the need to address how people interpret meteorological information through televised forecasts and graphics. However, eye tracking has not yet been applied to learning about forecaster behavior and decision processes. In this article, we consider what current methods are being used to study forecasters and why we believe eye tracking is a method that should be incorporated into our efforts. We share our first data collection of an NWS forecaster’s eye gaze data, and explore the types of information that these data provide about the forecaster’s cognitive processes. We also discuss how eye-tracking methods could be applied to other aspects of operational meteorology research in the future, and provide motivation for further exploration on this topic.
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9

Godfroid, Aline, Paula Winke, and Kathy Conklin. "Exploring the depths of second language processing with eye tracking: An introduction." Second Language Research 36, no. 3 (2020): 243–55. http://dx.doi.org/10.1177/0267658320922578.

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In this paper, we review how eye tracking, which offers millisecond-precise information about how language learners orient their visual attention, can be used to investigate a variety of processes involved in the multifaceted endeavor of second language acquisition (SLA). In particular, we review the last 15 years of research in SLA, in which applied linguists have exploited the information gleaned from eye-tracking metrics to advance the field. As we explain, eye-tracking researchers within SLA have diversified which aspects of SLA they investigate and are entering new territory by pairing eye-tracking metrics with other data-collection methods for data-triangulation purposes. Eye tracking in SLA is also an inherently interdisciplinary endeavor, for which research partnerships with computer scientists hold special promise in the areas of automated language assessment and the evaluation of cognitive functioning and processing. We describe how the papers in this special issue on eye tracking in Second Language Research push the boundaries by: (a) ensuring greater standardization of how eye tracking is used in SLA (Godfroid and Hui, 2020); (b) embedding eye-tracking metrics within a mixed-methods design for more valid and complete data interpretation (Andringa, 2020; Michel et al., 2020); (c) using eye trackers to investigate the nuanced differences in cognitive processes involved across multimodal input and feedback types in SLA (Conklin et al., 2020; McDonough et al., 2020).
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Vladimirov, I. Yu, and A. V. Chistopolskaya. "Eye-tracking and cognitive monitoring as the methods of insight process objectification." Experimental Psychology (Russia) 12, no. 1 (2019): 167–79. http://dx.doi.org/10.17759/exppsy.2019120113.

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Current article highlights the results of the research of specific mechanisms of insight problem solving. It is based on the analysis of eye movement record data made by eye-tracker. The recorded data included average pupil diameter [mm] and fixation duration [ms]; the distribution of averaged eye movement values within the areas of interest during the manipulations with problem space was analyzed. The eye movement data was compared to the cognitive monitoring method data. The specificity of insight problems in comparison with non-insight (algorithmized) problems was validated. Several qualitative features of insight problem solving and the organization of problem space were revealed. Additionally, the priority of visual processing during insight problem solving was discovered: fixation duration increased in the “main problem” AOI.
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