Eye tracking is considered one of the most salient methods to study the cognitive demands of humans in human computer interactive systems, due to the unobtrusiveness, flexibility and the development of inexpensive eye trackers. In this work, we evaluate the applicability of these low cost eyetrackers to study pupillary response to varying memory loads and luminance conditions. Specifically, we examine a low-cost eye tracker, the Gazepoint GP3, and objectively evaluate its ability to differentiate pupil dilation metrics under different cognitive loads and luminance conditions. The classification performance is computed in the form of a receiver operating characteristic (ROC) curve and the results indicate that Gazepoint provides a reliable eye tracker to human computer interaction applications requiring pupil dilation studies.
CITATION STYLE
Mannaru, P., Balasingam, B., Pattipati, K., Sibley, C., & Coyne, J. T. (2017). Performance evaluation of the gazepoint GP3 eye tracking device based on pupil dilation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10284 11th International Conference, AC 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part I, pp. 166–175). Springer Verlag. https://doi.org/10.1007/978-3-319-58628-1_14
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