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Appraisal-based affect recognition in healthcare: Insights from the THERADIA WoZ corpus

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  • معلومة اضافية
    • Contributors:
      Laboratoire d'Informatique de Grenoble (LIG); Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP); Université Grenoble Alpes (UGA); Laboratoire d'Etude des Mécanismes Cognitifs (EMC); Université Lumière - Lyon 2 (UL2); GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP); GIPSA Pôle Parole et Cognition (GIPSA-PPC); Grenoble Images Parole Signal Automatique (GIPSA-lab); Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP); Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab); GIPSA-Services (GIPSA-Services); Atos; Pertimm; SBT Human(s) Matter Lyon (SBT); Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole (GETALP); ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
    • بيانات النشر:
      CCSD
    • الموضوع:
      2024
    • Collection:
      Université Grenoble Alpes: HAL
    • الموضوع:
    • نبذة مختصرة :
      International audience ; 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), per- formed 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 models of the appraisal theories, including novelty, intrinsic pleasantness, goal conduciveness, and coping. Additionally, the annotations included 23 affective labels drew from the literature of achievement affects. We present the protocols used for the data collection, transcription, and annotation, along with a detailed analysis of the annotated dimensions and labels. Baseline methods and results for their automatic prediction are also presented. The corpus aims to serve as a valuable resource for researchers in affective computing, and is made available to both industry and academia.
    • الدخول الالكتروني :
      https://hal.science/hal-04752973
      https://hal.science/hal-04752973v1/document
      https://hal.science/hal-04752973v1/file/Poster_CORPUS_coll_cog_24.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.4DA326B2