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Frustration Recognition Using Spatio Temporal Data: A Novel Dataset and GCN Model to Recognize In-Vehicle Frustration

Bosch, Esther Johanna and Le Houcq Corbi, Raquel and Ihme, Klas and Hörmann, Stefan and Jipp, Meike and Käthner, David (2022) Frustration Recognition Using Spatio Temporal Data: A Novel Dataset and GCN Model to Recognize In-Vehicle Frustration. IEEE Transactions on Affective Computing. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TAFFC.2022.3229263. ISSN 1949-3045.

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Abstract

Frustration is an unpleasant emotion prevalent in several target applications of affective computing, such as human-machine interaction, learning, (online) customer interaction, and gaming. One idea to redeem this issue is to recognize frustration to offer help or mitigation in real-time, e.g. by a personal assistant. However, the recognition of frustration is not limited to these applied contexts but can also inform emotion research in general. This paper presents a dataset of 43 participants who experienced frustration in driving-related situations in a simulator. The data set contains a continuous subjective label, hand-annotated face and body expressions, facial landmark coordinates of two cameras, and the participants’ age and sex information. In addition, a descriptive analysis and description of the data’s characteristics are provided together with a Graph Convolution Network based model to recognize frustration. Allowing for a tolerance of 10%, the model could correctly identify frustration with a similarity of 79.4 % and a variance of 7.7 %. This work is valuable for researchers of the affective computing community because it provides realistic data with an in-depth description of its characteristics and a benchmark model for automated frustration recognition. Our FRUST-dataset is publicly available under: https://ts.dlr.de/data-lake/frust-dataset/.

Item URL in elib:https://elib.dlr.de/194790/
Document Type:Article
Title:Frustration Recognition Using Spatio Temporal Data: A Novel Dataset and GCN Model to Recognize In-Vehicle Frustration
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bosch, Esther JohannaUNSPECIFIEDhttps://orcid.org/0000-0002-6525-2650UNSPECIFIED
Le Houcq Corbi, RaquelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ihme, KlasUNSPECIFIEDhttps://orcid.org/0000-0002-7911-3512UNSPECIFIED
Hörmann, StefanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Jipp, MeikeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Käthner, DavidUNSPECIFIEDhttps://orcid.org/0000-0003-4168-2266UNSPECIFIED
Date:December 2022
Journal or Publication Title:IEEE Transactions on Affective Computing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1109/TAFFC.2022.3229263
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1949-3045
Status:Published
Keywords:Frustration Recognition, Naturalistic Dataset, Graph Convolution Network, Affect-Aware Systems
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 > Information Flow Modelling in Mobility Systems, BS
Institute of Transport Research > Leitungsbereich VF
Deposited By: Bosch, Esther Johanna
Deposited On:28 Apr 2023 15:06
Last Modified:28 Apr 2023 15:06

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