Bosch, Esther Johanna und Le Houcq Corbi, Raquel und Ihme, Klas und Hörmann, Stefan und Jipp, Meike und 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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Kurzfassung
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/.
elib-URL des Eintrags: | https://elib.dlr.de/194790/ | ||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||
Titel: | Frustration Recognition Using Spatio Temporal Data: A Novel Dataset and GCN Model to Recognize In-Vehicle Frustration | ||||||||||||||
Autoren: |
*DLR corresponding author | ||||||||||||||
Datum: | Dezember 2022 | ||||||||||||||
Erschienen in: | IEEE Transactions on Affective Computing | ||||||||||||||
Open Access: | Nein | ||||||||||||||
In SCOPUS: | Ja | ||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||
DOI: | 10.1109/TAFFC.2022.3229263 | ||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||
ISSN: | 1949-3045 | ||||||||||||||
Stichwörter: | Frustration Recognition, Naturalistic Dataset, Graph Convolution Network, Affect-Aware Systems | ||||||||||||||
DLR - Schwerpunkt: | Verkehr | ||||||||||||||
DLR - Forschungsgebiet: | V VS - Verkehrssystem | ||||||||||||||
Standort: | Braunschweig | ||||||||||||||
Institute & Einrichtungen: | Institut für Verkehrssystemtechnik > Informationsflussmodellierung in Mobilitätssystemen, BS Institut für Verkehrsforschung > Leitungsbereich VF |
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