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A framework to estimate cognitive load using physiological data

Muneeb Ahmad Orcid Logo, Ingo Keller, David A. Robb, Katrin S. Lohan

Personal and Ubiquitous Computing

Swansea University Author: Muneeb Ahmad Orcid Logo

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DOI (Published version): 10.1007/s00779-020-01455-7

Abstract

Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can h...

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Published in: Personal and Ubiquitous Computing
ISSN: 1617-4909 1617-4917
Published: Springer Science and Business Media LLC 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa56610
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Abstract: Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.
Keywords: Cognitive load; Framework; Physiological data; Human-computer interaction
College: Faculty of Science and Engineering
Funders: ORCA Hub EPSRC (EP/R026173/1, 2017-2021)

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