A Hybrid Data Fusion Architecture for BINDI - A Wearable Solution to Combat Gender-Based Violence.

Currently, most of the affective computing research is about modifying and adapting the machine behavior based on the human emotional state. Although, the use of the affective state inference can be extended to provide a tool for other fields more society related such as gender violence detection, which is a real global emergency. Based on the World Health Organization (WHO) statistics, one in three women worldwide experiences gender-based violence, often from an intimate partner. Due to this motivation, the authors developed BINDI, which is a wearable solution for detecting automatically those situations. It uses affective computing together with short-term physiological and physical observations. It represents a step toward an autonomous, embedded, non-intrusive, and wearable system for detecting those situations and connecting the victim with a trusted circle. In this work, and as a response for improving the detection capability of BINDI, a novel hybrid data fusion architecture is proposed. This new architecture is intended to improve the already implemented decision level fusion architecture. Further details of the uni-modal systems and the different approaches needed to be explored in the future are given.