COLET: A Dataset for Cognitive WorkLoad Estimation Based on Eye-Tracking

Abstract

The cognitive workload is an important component in performance psychology, ergonomics, and human factors. Unfortunately, publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad 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. The results from the classification of cognitive workload levels solely based on eye data, by employing and testing a set of machine learning algorithms are also provided. The dataset is available to the academic community.

Note:
Funding Information: This project has received funding from the European Unionasˆ Horizon 2020 research and innovation program under grant agree ment No 826429 (Project: SeeFar).

Declaration of Interests: None to declare.

Ethics Approval Statement: The experimental protocol (110/12-02-2021) was submitted and approved by the Ethical Committee of the Foundation for Research and Technology Hellas (FORTH). Subsequently, all participants read and signed an Information Consent Form.

Keywords: Cognitive workload, Workload classification, Eye movements, Machine Learning, Eye-tracking, Affective computing