Abstract
WEMAC is a unique open multi-modal dataset that comprises physiological, speech, and self-reported emotional data records of 100 women, targeting Gender-based Violence detection. Emotions were elicited through visualizing a validated video set using an immersive virtual reality headset. The physiological signals captured during the experiment include blood volume pulse, galvanic skin response, and skin temperature. The speech was acquired right after the stimuli visualization to capture the final traces of the perceived emotion. Subjects were asked to annotate among 12 categorical emotions, several dimensional emotions with a modified version of the Self-Assessment Manikin, and liking and familiarity labels. The technical validation proves that all the targeted categorical emotions show a strong statistically significant positive correlation with their corresponding reported ones. That means that the videos elicit the desired emotions in the users in most cases. Specifically, a negative correlation is found when comparing fear and not-fear emotions, indicating that this is a well-portrayed emotional dimension, a specific, though not exclusive, purpose of WEMAC towards detecting gender violence.