Detecting users' cognitive load by galvanic skin response with affective interference

Nourbakhsh, N; Chen, F; Wang, Y; Calvo, RA

Detecting users' cognitive load by galvanic skin response with affective interference

Nourbakhsh, N Chen, F

Wang, Y

Calvo, RA

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: The ACM Transactions on Interactive Intelligent Systems, 2017, 7, (3), pp. 1-20
Issue Date:: 2017-09-01

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Field	Value	Language
dc.contributor.author	Nourbakhsh, N
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Wang, Y https://orcid.org/0000-0002-6815-0879
dc.contributor.author	Calvo, RA
dc.date.accessioned	2022-08-03T05:29:17Z
dc.date.available	2022-08-03T05:29:17Z
dc.date.issued	2017-09-01
dc.identifier.citation	The ACM Transactions on Interactive Intelligent Systems, 2017, 7, (3), pp. 1-20
dc.identifier.issn	2160-6455
dc.identifier.issn	2160-6463
dc.identifier.uri	http://hdl.handle.net/10453/159563
dc.description.abstract	Experiencing high cognitive load during complex and demanding tasks results in performance reduction, stress, and errors. However, these could be prevented by a system capable of constantly monitoring users' cognitive load fluctuations and adjusting its interactions accordingly. Physiological data and behaviors have been found to be suitable measures of cognitive load and are now available in many consumer devices. An advantage of these measures over subjective and performance-based methods is that they are captured in real time and implicitly while the user interacts with the system, which makes them suitable for real-world applications. On the other hand, emotion interference can change physiological responses and make accurate cognitive load measurement more challenging. In this work, we have studied six galvanic skin response (GSR) features in detection of four cognitive load levels with the interference of emotions. The data was derived from two arithmetic experiments and emotions were induced by displaying pleasant and unpleasant pictures in the background. Two types of classifiers were applied to detect cognitive load levels. Results from both studies indicate that the features explored can detect four and two cognitive load levels with high accuracy even under emotional changes. More specifically, rise duration and accumulative GSR are the common best features in all situations, having the highest accuracy especially in the presence of emotions.
dc.language	English
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	The ACM Transactions on Interactive Intelligent Systems
dc.relation.isbasedon	10.1145/2960413
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1203 Design Practice and Management
dc.title	Detecting users' cognitive load by galvanic skin response with affective interference
dc.type	Journal Article
utslib.citation.volume	7
utslib.for	1203 Design Practice and Management
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-03T05:29:16Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	3

Abstract:

Experiencing high cognitive load during complex and demanding tasks results in performance reduction, stress, and errors. However, these could be prevented by a system capable of constantly monitoring users' cognitive load fluctuations and adjusting its interactions accordingly. Physiological data and behaviors have been found to be suitable measures of cognitive load and are now available in many consumer devices. An advantage of these measures over subjective and performance-based methods is that they are captured in real time and implicitly while the user interacts with the system, which makes them suitable for real-world applications. On the other hand, emotion interference can change physiological responses and make accurate cognitive load measurement more challenging. In this work, we have studied six galvanic skin response (GSR) features in detection of four cognitive load levels with the interference of emotions. The data was derived from two arithmetic experiments and emotions were induced by displaying pleasant and unpleasant pictures in the background. Two types of classifiers were applied to detect cognitive load levels. Results from both studies indicate that the features explored can detect four and two cognitive load levels with high accuracy even under emotional changes. More specifically, rise duration and accumulative GSR are the common best features in all situations, having the highest accuracy especially in the presence of emotions.

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http://hdl.handle.net/10453/159563