Abstract
Assessment of cognitive workload level is important to understand human mental fatigue, especially in the case of performing intellectual tasks. The paper presents a case study on binary classification of cognitive workload levels. The dataset was received from two versions of the digit symbol substitution test (DSST), conducted on 26 healthy volunteers. A screen-based eye tracker was applied during an examination gathering oculographic data. DSST test results such as total number of matches and error ratio were also applied. Classification was performed with several different machine learning models. The best accuracy (97%) was achieved with linear SVM classifier. The final dataset for classification was based on nine features selected with the Fisher score feature selection method.
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Kaczorowska, M., Wawrzyk, M., Plechawska-Wójcik, M. (2020). Binary Classification of Cognitive Workload Levels with Oculography Features. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2020. Lecture Notes in Computer Science(), vol 12133. Springer, Cham. https://doi.org/10.1007/978-3-030-47679-3_21
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DOI: https://doi.org/10.1007/978-3-030-47679-3_21
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