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A framework to estimate cognitive load using physiological data

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

Personal and Ubiquitous Computing

Swansea University Author: Muneeb Ahmad

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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 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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first_indexed 2021-04-14T11:31:41Z
last_indexed 2022-01-14T04:25:26Z
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spelling 2022-01-13T15:35:19.0730167 v2 56610 2021-03-31 A framework to estimate cognitive load using physiological data 9c42fd947397b1ad2bfa9107457974d5 Muneeb Ahmad Muneeb Ahmad true false 2021-03-31 SCS 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. Journal Article Personal and Ubiquitous Computing 0 Springer Science and Business Media LLC 1617-4909 1617-4917 Cognitive load; Framework; Physiological data; Human-computer interaction 27 9 2020 2020-09-27 10.1007/s00779-020-01455-7 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Another institution paid the OA fee ORCA Hub EPSRC (EP/R026173/1, 2017-2021) 2022-01-13T15:35:19.0730167 2021-03-31T17:06:04.8784970 College of Science Computer Science Muneeb Ahmad 1 Ingo Keller 2 David A. Robb 3 Katrin S. Lohan 4 56610__19646__507ef5ac15e34867b5acf952fe4acf13.pdf 56610.pdf 2021-04-14T12:32:03.7289241 Output 1278638 application/pdf Version of Record true © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommonshorg/licenses/by/4.0/
title A framework to estimate cognitive load using physiological data
spellingShingle A framework to estimate cognitive load using physiological data
Muneeb, Ahmad
title_short A framework to estimate cognitive load using physiological data
title_full A framework to estimate cognitive load using physiological data
title_fullStr A framework to estimate cognitive load using physiological data
title_full_unstemmed A framework to estimate cognitive load using physiological data
title_sort A framework to estimate cognitive load using physiological data
author_id_str_mv 9c42fd947397b1ad2bfa9107457974d5
author_id_fullname_str_mv 9c42fd947397b1ad2bfa9107457974d5_***_Muneeb, Ahmad_***_
author Muneeb, Ahmad
author2 Muneeb Ahmad
Ingo Keller
David A. Robb
Katrin S. Lohan
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publishDate 2020
institution Swansea University
issn 1617-4909
1617-4917
doi_str_mv 10.1007/s00779-020-01455-7
publisher Springer Science and Business Media LLC
college_str College of Science
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hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
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description 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.
published_date 2020-09-27T04:25:01Z
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