Abstract
The basic building block of any eye tracking research is the eye fixations. These eye fixations depend on more fine data gathered by the eye tracker device, the raw gaze data. There are many algorithms that can be used to transform the raw gaze data into eye fixation. However, these algorithms require one or more thresholds to be set. A knowledge of the most appropriate values for these thresholds is necessary in order for these algorithms to generate the desired output. This paper examines the effect of a set of different settings of the two thresholds required for the identification-dispersion threshold type of algorithms: the dispersion and duration thresholds on the generated eye fixations. Since this work is at its infancy, the goal of this paper is to generate and visualize the result of each setting and leave the choice for the readers to decide on which setting fits their future eye tracking research.
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The author would like to thank Dr. Ziho Kang for allowing the use of his Human Factors and Simulation Lab equipment.
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Alhashim, A.G. (2020). Eye Movement Classification Algorithms: Effect of Settings on Related Metrics. In: Stephanidis, C., Kurosu, M., Degen, H., Reinerman-Jones, L. (eds) HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence. HCII 2020. Lecture Notes in Computer Science(), vol 12424. Springer, Cham. https://doi.org/10.1007/978-3-030-60117-1_1
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DOI: https://doi.org/10.1007/978-3-030-60117-1_1
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