THERADIA WoZ: An Ecological Corpus for Appraisal-Based Affect Research in Healthcare

We present THERADIA WoZ, an ecological corpus designed for audiovisual research on affect in healthcare. Two groups of senior individuals, consisting of 52 healthy participants and 9 individuals with Mild Cognitive Impairment (MCI), performed Computerised Cognitive Training (CCT) exercises while receiving support from a virtual assistant, tele-operated by a human in the role of a Wizard-of-Oz (WoZ). The audiovisual expressions produced by the participants were fully transcribed, and partially annotated based on dimensions derived from recent appraisal theory models, including novelty, intrinsic pleasantness, goal conduciveness, and coping. Additionally, the annotations included 23 affective labels from the literature of achievement affects. We present the data collection, transcription, and annotation protocols, alongside a detailed analysis of the annotated dimensions and labels. Baseline methods and results for their automatic prediction are also presented. Results reveal that the dimensions of appraisal theory can be predicted, with the performance varying across different modalities. The corpus aims to serve as a valuable resource for researchers in affective computing, and is made available to both industry and academia.

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Publication Details

Journal
IEEE Transactions on Affective Computing
Published
2025-04-03
DOI
https://doi.org/10.1109/taffc.2025.3557465
Primary Topic
Food Security and Health in Diverse Populations
Type
article
Field-Weighted Citation Impact
0.00

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article

THERADIA WoZ: An Ecological Corpus for Appraisal-Based Affect Research in Healthcare

Béatrice Bouchot, Fabien Ringeval, Patrick Constant, Olivier Koenig et al.
IEEE Transactions on Affective Computing
Food Security and Health in Diverse Populations
article

THERADIA WoZ: An Ecological Corpus for Appraisal-Based Affect Research in Healthcare

Béatrice Bouchot, Fabien Ringeval, Patrick Constant, Olivier Koenig, Hanna Chainay, Sina Alisamir, Frédéric Elisei, Gérard Bailly, Solange Rossato, Hippolyte Fournier, François Portet, Isabella Zsoldos, Franck Tarpin-Bernard, Joan Fruitet, Safaa Azzakhnini, Brice Varini, Eléonore Trân
article en

Abstract

We present THERADIA WoZ, an ecological corpus designed for audiovisual research on affect in healthcare. Two groups of senior individuals, consisting of 52 healthy participants and 9 individuals with Mild Cognitive Impairment (MCI), performed Computerised Cognitive Training (CCT) exercises while receiving support from a virtual assistant, tele-operated by a human in the role of a Wizard-of-Oz (WoZ). The audiovisual expressions produced by the participants were fully transcribed, and partially annotated based on dimensions derived from recent appraisal theory models, including novelty, intrinsic pleasantness, goal conduciveness, and coping. Additionally, the annotations included 23 affective labels from the literature of achievement affects. We present the data collection, transcription, and annotation protocols, alongside a detailed analysis of the annotated dimensions and labels. Baseline methods and results for their automatic prediction are also presented. Results reveal that the dimensions of appraisal theory can be predicted, with the performance varying across different modalities. The corpus aims to serve as a valuable resource for researchers in affective computing, and is made available to both industry and academia.

IEEE Transactions on Affective ComputingVol. 16(3)
Université Claude Bernard Lyon 1 (FR), Institut polytechnique de Grenoble (FR), Centre National de la Recherche Scientifique (FR), Atos (France) (FR), Lyon College (US), Université de Lyon (FR), Stago (France) (FR), Centre Inria de l'Université Grenoble Alpes (FR), Laboratoire d'Informatique de Grenoble (FR), Matériaux Ingénierie et Science (FR), GIPSA-Lab (FR), Université Grenoble Alpes (FR)
Bpifrance, Agence Nationale de la Recherche, Association Nationale de la Recherche et de la Technologie
Openalex Percentile: Top 89%
Food Security and Health in Diverse Populations
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