There is a newer version of the record available.

Published January 28, 2022 | Version 0
Dataset Open

COLET: A Dataset for Cognitive workLoad estimation based on Eye-Tracking

Authors/Creators

  • 1. Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH) and the Laboratory of Optics and Vision, School of Medicine, University of Crete, GR-710 03 Heraklion, Greece
  • 2. Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), GR-700 13 Heraklion, Crete, Greece
  • 3. Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR-451 15, Ioannina, Greece and the Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR-451 10, Ioannina, Greece
  • 4. Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR-451 10, Ioannina, Greece
  • 5. Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH) and the Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR-710 04 Heraklion, Crete, Greece

Description

Cognitive workload is an important component in performance psychology, ergonomics, and human factors. Unfortunately, benchmarks and publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad state estimation based on Eye-Tracking dataset is presented. Forty-seven (47) individuals' eye movements were monitored as they solved puzzles involving visual search tasks of varying complexity and duration. The authors give an in-depth study of the participants' performance during the experiments while eye and gaze features were derived from low-level eye recorded metrics, and their relationships with the experiment tasks were investigated. Finally, the results from the classification of cognitive workload levels solely based on eye and gaze data, by employing and testing a set of machine learning algorithms are provided. The dataset is made available to the public.

IMPORTANT NOTE: This is version 0. The next version which will contain the eye-tracking data is going to be uploaded after the article's publication.

Notes

This is version 0. The next version which will contain the eye-tracking data is going to be uploaded after the article's publication.

Files

COLET_v0.zip

Files (24.9 MB)

Name Size Download all
md5:ca1f93ee27e66f1fd240a929d5bb91fe
24.9 MB Preview Download