  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.
