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Multimodal Estimation of Frustrative Driving Situations Using a Latent Variable Model

Bosch, Esther Johanna and Klosterkamp, Marie and Guevara, Angelo and Käthner, David and Bendixen, Alexandra and Ihme, Klas (2022) Multimodal Estimation of Frustrative Driving Situations Using a Latent Variable Model. In: 13th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2022. 13th IEEE International Conference on Cognitive InfoCommunications, 22.-23. Sep. 2022, online. doi: 10.1109/CogInfoCom55841.2022.10081636. ISBN 978-1-6654-9238-6. ISSN 2375-1312.

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Within recent years, it has become popular to use physiological and expression data to determine appropriate moments for information communication in human-machine interaction. One emotion that is highly relevant to inform systems about these appropriate time points is frustration, which occurs when a user’s goal in using the system is failed to be met. In the context of transportation, frustration can lead to risky behavior when driving manually and diminished acceptance of automated cars. It is therefore crucial to mitigate this negative emotion, for example by implementing frustration-aware systems that can recognize frustration and react accordingly. Automated recognition of frustration has previously been achieved by various measures obtained from the user or driver of the vehicle via classical regression or automated classification. This paper presents a latent variable model that estimates frustration in two different contexts by continuous subjective frustration rating, facial expressions and frontal alpha asymmetry in the electroencephalogram. We then compare this full model to models with less measurement variables to evaluate which measurements can be left out. Our results show that expression frequency and subjective frustration make important contributions to the model of experienced frustration. This paper presents a proof of concept for using a latent variable model to evaluate collected measures to estimate an experienced emotion. This method can inform researchers which measurements are most informative in different circumstances. Additionally, the method can be used to evaluate how well purely objective measurements (that are the only feasible measurements in most applied settings) perform in comparison to a model including subjective ratings.

Item URL in elib:https://elib.dlr.de/188706/
Document Type:Conference or Workshop Item (Speech)
Title:Multimodal Estimation of Frustrative Driving Situations Using a Latent Variable Model
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bosch, Esther JohannaUNSPECIFIEDhttps://orcid.org/0000-0002-6525-2650UNSPECIFIED
Klosterkamp, MarieUNSPECIFIEDhttps://orcid.org/0000-0002-2792-6597UNSPECIFIED
Guevara, AngeloUNSPECIFIEDhttps://orcid.org/0000-0001-9307-804XUNSPECIFIED
Käthner, DavidUNSPECIFIEDhttps://orcid.org/0000-0003-4168-2266UNSPECIFIED
Bendixen, AlexandraUNSPECIFIEDhttps://orcid.org/0000-0001-8211-9619UNSPECIFIED
Ihme, KlasUNSPECIFIEDhttps://orcid.org/0000-0002-7911-3512UNSPECIFIED
Date:September 2022
Journal or Publication Title:13th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2022
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:latent variable models, psychophysiological data, frustration recognition
Event Title:13th IEEE International Conference on Cognitive InfoCommunications
Event Location:online
Event Type:international Conference
Event Dates:22.-23. Sep. 2022
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - DATAMOST - Daten & Modelle zur Mobilitätstransform
Location: Braunschweig
Institutes and Institutions:Institute of Transportation Systems > Design and Evaluation of Mobility Solutions, BS
Deposited By: Bosch, Esther Johanna
Deposited On:21 Oct 2022 16:36
Last Modified:20 Nov 2023 10:14

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