No Cover Image

Journal article 923 views 140 downloads

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

  • Ahmad2020_Article_AFrameworkToEstimateCognitiveL.pdf

    PDF | Version of Record

    This article is licensed under a Creative Commons Attribution 4.0 International License

    Download (1.22MB)

Check full text

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...

Full description

Published in: Personal and Ubiquitous Computing
ISSN: 1617-4909 1617-4917
Published: Springer Science and Business Media LLC 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa56610
first_indexed 2021-04-14T11:31:41Z
last_indexed 2022-04-08T03:25:54Z
id cronfa56610
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-04-07T12:34:05.5017388</datestamp><bib-version>v2</bib-version><id>56610</id><entry>2021-03-31</entry><title>A framework to estimate cognitive load using physiological data</title><swanseaauthors><author><sid>9c42fd947397b1ad2bfa9107457974d5</sid><ORCID>0000-0001-8111-9967</ORCID><firstname>Muneeb</firstname><surname>Ahmad</surname><name>Muneeb Ahmad</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-03-31</date><deptcode>MACS</deptcode><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.</abstract><type>Journal Article</type><journal>Personal and Ubiquitous Computing</journal><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1617-4909</issnPrint><issnElectronic>1617-4917</issnElectronic><keywords>Cognitive load; Framework; Physiological data; Human-computer interaction</keywords><publishedDay>27</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-09-27</publishedDate><doi>10.1007/s00779-020-01455-7</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>ORCA Hub EPSRC (EP/R026173/1, 2017-2021)</funders><lastEdited>2022-04-07T12:34:05.5017388</lastEdited><Created>2021-03-31T17:06:04.8784970</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Muneeb</firstname><surname>Ahmad</surname><orcid>0000-0001-8111-9967</orcid><order>1</order></author><author><firstname>Ingo</firstname><surname>Keller</surname><order>2</order></author><author><firstname>David A.</firstname><surname>Robb</surname><order>3</order></author><author><firstname>Katrin S.</firstname><surname>Lohan</surname><order>4</order></author></authors><documents><document><filename>56610__22350__532145c3aaf04d8a995d0b57b5a34e96.pdf</filename><originalFilename>Ahmad2020_Article_AFrameworkToEstimateCognitiveL.pdf</originalFilename><uploaded>2022-02-10T17:59:21.2442815</uploaded><type>Output</type><contentLength>1278638</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This article is licensed under a Creative Commons Attribution 4.0 International License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommonshorg/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-04-07T12:34:05.5017388 v2 56610 2021-03-31 A framework to estimate cognitive load using physiological data 9c42fd947397b1ad2bfa9107457974d5 0000-0001-8111-9967 Muneeb Ahmad Muneeb Ahmad true false 2021-03-31 MACS 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 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 Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee ORCA Hub EPSRC (EP/R026173/1, 2017-2021) 2022-04-07T12:34:05.5017388 2021-03-31T17:06:04.8784970 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Muneeb Ahmad 0000-0001-8111-9967 1 Ingo Keller 2 David A. Robb 3 Katrin S. Lohan 4 56610__22350__532145c3aaf04d8a995d0b57b5a34e96.pdf Ahmad2020_Article_AFrameworkToEstimateCognitiveL.pdf 2022-02-10T17:59:21.2442815 Output 1278638 application/pdf Version of Record true 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
format Journal article
container_title Personal and Ubiquitous Computing
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 Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
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 2020-09-27T14:09:01Z
_version_ 1821414828157173760
score 11.048064

Similar Items