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WEMAC: Women and Emotion Multi-modal Affective Computing dataset.

Jose A Miranda Calero1,2, Laura Gutiérrez-Martín3, Esther Rituerto-González4,5

  • 1Embedded Systems Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Vaud, Switzerland. jose.mirandacalero@epfl.ch.

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|October 31, 2024
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Summary
This summary is machine-generated.

The WEMAC dataset captures physiological and speech data from 100 women to detect gender-based violence. Technical validation confirms the dataset effectively elicits and records emotions, aiding in violence detection research.

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Area of Science:

  • Multimodal emotion recognition
  • Physiological signal processing
  • Speech analysis for emotion detection

Background:

  • Gender-based violence (GBV) detection requires nuanced emotional understanding.
  • Existing datasets may lack multi-modal emotional data, particularly from diverse populations.
  • Virtual reality (VR) offers a controlled environment for emotion elicitation.

Purpose of the Study:

  • Introduce the WEMAC dataset, an open, multi-modal resource for GBV detection.
  • Correlate elicited emotions with physiological and speech responses in 100 women.
  • Validate the dataset's effectiveness in capturing fear-related emotions crucial for GBV detection.

Main Methods:

  • Collected physiological data (blood volume pulse, galvanic skin response, skin temperature) and speech recordings from 100 women.
  • Elicited emotions using validated videos viewed via VR headsets.
  • Subjects self-reported emotions using categorical and dimensional scales (Self-Assessment Manikin).

Main Results:

  • Technical validation showed strong positive correlations between elicited and reported categorical emotions.
  • A significant negative correlation was observed between fear and non-fear emotion reporting.
  • The dataset successfully elicits target emotions, particularly fear, relevant to GBV detection.

Conclusions:

  • The WEMAC dataset is a validated resource for multi-modal emotion research, specifically for GBV detection.
  • The data effectively captures physiological and speech correlates of elicited emotions.
  • The dataset's focus on fear detection provides a valuable tool for advancing GBV research.