Abstract
The paper describes and compares several novel alternatives for labeling gaze ethograms data to estimate the activity that users carry out in front of computers with the use of the onboard camera. Gaze ethograms are basically discrete functions of time, therefore, the problem can be formulated by applying statistical and machine learning inspired methods to reduce the amount of information on a specific activity. To compare the proposed methods we carry out several experiments with experimental subjects in an office-like environment with no special lighting conditions. The result is a set of recommendations that allow to classify the activities with high precision.
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Acknowledgments
This work has been partially supported by FEDER funds through MINECO project TIN2017-85827-P.
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de Lope, J., Graña, M. (2020). Comparison of Labeling Methods for Behavioral Activity Classification Based on Gaze Ethograms. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_12
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DOI: https://doi.org/10.1007/978-3-030-61705-9_12
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