COLET: A Dataset for Cognitive workLoad estimation based on Eye-Tracking
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.
Please cite the following work:
Ktistakis, E., Skaramagkas, V., Manousos, D., Tachos, N. S., Tripoliti, E., Fotiadis, D. I., & Tsiknakis, M. (2022). Colet: A dataset for cognitive workload estimation based on eye-tracking. Computer Methods and Programs in Biomedicine, 106989. https://doi.org/10.1016/j.cmpb.2022.106989