WEMAC: Women and Emotion Multi-modal Affective Computing dataset

TL;DR

This paper introduces WEMAC, a multi-modal dataset capturing physiological, speech, and self-report data from women exposed to emotional stimuli, aiming to advance affective computing for gender-related safety applications.

Contribution

The paper presents the first release of WEMAC, a novel multi-modal dataset designed for affective computing research involving women and emotional stimuli.

Findings

Dataset includes physiological, speech, and self-report data from 47 women.

Data collected during VR-based emotional stimuli exposure.

Supports research in multi-modal affective computing and safety systems.

Abstract

Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the Fifth SDG is a call for action to turn Gender Equality into a fundamental human right and an essential foundation for a better world. It includes the eradication of all types of violence against women. Within this context, the UC3M4Safety research team aims to develop Bindi. This is a cyber-physical system which includes embedded Artificial Intelligence algorithms, for user real-time monitoring towards the detection of affective states, with the ultimate goal of achieving the early detection of risk situations for women. On this basis, we make use of wearable affective computing including smart sensors, data encryption for secure and accurate collection of presumed crime evidence, as well as the remote connection to protecting agents. Towards the development of such system, the recordings of different laboratory and into-the-wild datasets are in process. These are contained within the UC3M4Safety Database. Thus, this paper presents and details the first release of WEMAC, a novel multi-modal dataset, which comprises a laboratory-based experiment for 47 women volunteers that were exposed to validated audio-visual stimuli to induce real emotions by using a virtual reality headset while physiological, speech signals and self-reports were acquired and collected. We believe this dataset will serve and assist research on multi-modal affective computing using physiological and speech information.