Detection and Estimation of Cognitive Conflict During Physical Human-Robot Collaboration

Aldini, S; Singh, AK; Leong, D; Wang, YK; Carmichael, MG; Liu, D; Lin, CT

Detection and Estimation of Cognitive Conflict During Physical Human-Robot Collaboration

Aldini, S

Singh, AK Leong, D Wang, YK Carmichael, MG Liu, D

Lin, CT

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive and Developmental Systems, 2022, PP, (99), pp. 1-1
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Aldini, S https://orcid.org/0000-0001-5411-3850
dc.contributor.author	Singh, AK
dc.contributor.author	Leong, D
dc.contributor.author	Wang, YK
dc.contributor.author	Carmichael, MG
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582
dc.contributor.author	Lin, CT
dc.date.accessioned	2023-03-09T04:59:42Z
dc.date.available	2023-03-09T04:59:42Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Cognitive and Developmental Systems, 2022, PP, (99), pp. 1-1
dc.identifier.issn	2379-8920
dc.identifier.issn	2379-8939
dc.identifier.uri	http://hdl.handle.net/10453/166798
dc.description.abstract	Robots for physical Human-Robot Collaboration (pHRC) often need to adapt their admittance and how they operate due to several factors. As the admittance of the system becomes variable throughout the workspace, it is not always straightforward for the operator to predict the robot’s behaviour. Previous work demonstrated that cognitive conflicts can be detected during one-dimensional tasks. This work assesses whether cognitive conflicts can also be detected during 2D tasks in pHRC and a classification problem is formulated. Different robot admittance profiles anticipating the stimulus translated into different levels of cognitive conflict. Several commonly used classification algorithms for EEG signals were evaluated to classify different levels of cognitive conflict. Results demonstrate that cognitive conflict level is lower when the admittance smoothly decreases before unexpected events when compared to conditions in which the admittance abruptly decreases before the stimulus. Among the classification algorithms, Convolutional Neural Network has shown the best results to classify different levels of cognitive conflict. Results suggest the feasibility of adaptive approaches for future pHRC control systems that close the loop on users through EEG signals. The detected human cognitive state can also be used to assess and improve the predictability of Human-Robot teams in various pHRC applications.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation	http://purl.org/au-research/grants/arc/LP140100950
dc.relation.ispartof	IEEE Transactions on Cognitive and Developmental Systems
dc.relation.isbasedon	10.1109/TCDS.2022.3205168
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Detection and Estimation of Cognitive Conflict During Physical Human-Robot Collaboration
dc.type	Journal Article
utslib.citation.volume	PP
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/Strength - RI - Robotics Institute
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-09-08T00:00:00+1000Z
dc.date.updated	2023-03-09T04:59:37Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Robots for physical Human-Robot Collaboration (pHRC) often need to adapt their admittance and how they operate due to several factors. As the admittance of the system becomes variable throughout the workspace, it is not always straightforward for the operator to predict the robot’s behaviour. Previous work demonstrated that cognitive conflicts can be detected during one-dimensional tasks. This work assesses whether cognitive conflicts can also be detected during 2D tasks in pHRC and a classification problem is formulated. Different robot admittance profiles anticipating the stimulus translated into different levels of cognitive conflict. Several commonly used classification algorithms for EEG signals were evaluated to classify different levels of cognitive conflict. Results demonstrate that cognitive conflict level is lower when the admittance smoothly decreases before unexpected events when compared to conditions in which the admittance abruptly decreases before the stimulus. Among the classification algorithms, Convolutional Neural Network has shown the best results to classify different levels of cognitive conflict. Results suggest the feasibility of adaptive approaches for future pHRC control systems that close the loop on users through EEG signals. The detected human cognitive state can also be used to assess and improve the predictability of Human-Robot teams in various pHRC applications.

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http://hdl.handle.net/10453/166798