EMPATÍA-CM: protEcción integral de las víctimas de violencia de género Mediante comPutación AfecTIva multimodal

The EMPATIACM project begins in January 2019, executed by a multidisciplinary team formed by the Institute of Gender Studies in Universidad Carlos III of Madrid (UC3M-IEG, with research staff from several branches of the Social Sciences and Humanities) and by the UC3M-TEC group (formed in turn by research personnel from several branches of Engineering), with the  fundamental objective  of   understanding the reactions of victims of Gender-based Violence (GBV) to dangerous situations, generate mechanisms for automatic detection of these situations and study how to react in a comprehensive, coordinated and effective way to protect them in the best possible way. This objective is divided into six sub-objectives, which demonstrate the need and added value of the multidisciplinary approach. EMPATIA combines cyberphysical systems and affective computing proposing a comprehensive protocol that improves the protection of victims of gender violence with a solution capable of automatically, immediately and remotely warning of risk situations. Close to the end of the project, it is time to assess the work done and the results achieved, presenting the contributions in each of the sub-objectives raised in the project proposal.El proyecto EMPATIACM comienza en enero de 2019, a cargo de un equipo multidisciplinar formado por el Instituto de Estudios de Género de la Universidad Carlos III de Madrid (UC3M-IEG, con personal investigador de varias ramas de las Ciencias Sociales y las Humanidades) y por el grupo UC3M-TEC (formado a su vez por personal investigador de varias ramas de la Ingeniería), con el objetivo fundamental de entender las reacciones de las víctimas de la Violencia de Género (VG) ante situaciones de peligro, generar mecanismos de detección automática de estas situaciones y estudiar cómo reaccionar de forma integral, coordinada y eficaz para protegerlas de la mejor forma posible. Este objetivo se divide en seis subobjetivos, que demuestran la necesidad y valor añadido del enfoque multidisciplinar. EMPATIA aúna sistemas ciberfísicos y computación afectiva proponiendo un protocolo integral que mejora la protección de las víctimas de violencia de género con una solución capaz de avisar de forma automática, inmediata y remota de situaciones de riesgo. Cerca del final del proyecto, es el momento de hacer balance del trabajo realizado y los resultados alcanzados, presentando las aportaciones en cada uno de los subobjetivos planteados en la propuesta del proyecto

Embedded Emotion Recognition within Cyber-Physical Systems using Physiological Signals

Cyber-Physical Systems (CPSs) are systems designed as a network of different interacting elements, which integrate computational and physical capabilities. The human-machine interaction plays a significant role in CPSs, especially in applications where people are an active element. In this context, emotion recognition is a relevant aspect to achieve a more efficient, collaborative, and resilient machine performance in collaboration with humans. On this basis, this paper proposes an embedded machine learning approach for emotion recognition fully implemented in an ultra low-power System-on-Chip (SoC) with limited resources. To this end, the intelligence system considers a reduced set of raw physiological signals within an approximate computing focus

Fear Classification using Affective Computing with Physiological Information and Smart-Wearables

Mención Internacional en el título de doctorAmong the 17 Sustainable Development Goals proposed within the 2030 Agenda and adopted by all of the United Nations member states, the fifth SDG is a call for action to effectively 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. Focusing on the technological perspective, the range of available solutions intended to prevent this social problem is very limited. Moreover, most of the solutions are based on a panic button approach, leaving aside the usage and integration of current state-of-the-art technologies, such as the Internet of Things (IoT), affective computing, cyber-physical systems, and smart-sensors. Thus, the main purpose of this research is to provide new insight into the design and development of tools to prevent and combat Gender-based Violence risky situations and, even, aggressions, from a technological perspective, but without leaving aside the different sociological considerations directly related to the problem. To achieve such an objective, we rely on the application of affective computing from a realist point of view, i.e. targeting the generation of systems and tools capable of being implemented and used nowadays or within an achievable time-frame. This pragmatic vision is channelled through: 1) an exhaustive study of the existing technological tools and mechanisms oriented to the fight Gender-based Violence, 2) the proposal of a new smart-wearable system intended to deal with some of the current technological encountered limitations, 3) a novel fear-related emotion classification approach to disentangle the relation between emotions and physiology, and 4) the definition and release of a new multi-modal dataset for emotion recognition in women. Firstly, different fear classification systems using a reduced set of physiological signals are explored and designed. This is done by employing open datasets together with the combination of time, frequency and non-linear domain techniques. This design process is encompassed by trade-offs between both physiological considerations and embedded capabilities. The latter is of paramount importance due to the edge-computing focus of this research. Two results are highlighted in this first task, the designed fear classification system that employed the DEAP dataset data and achieved an AUC of 81.60% and a Gmean of 81.55% on average for a subjectindependent approach, and only two physiological signals; and the designed fear classification system that employed the MAHNOB dataset data achieving an AUC of 86.00% and a Gmean of 73.78% on average for a subject-independent approach, only three physiological signals, and a Leave-One-Subject-Out configuration. A detailed comparison with other emotion recognition systems proposed in the literature is presented, which proves that the obtained metrics are in line with the state-ofthe- art. Secondly, Bindi is presented. This is an end-to-end autonomous multimodal system leveraging affective IoT throughout auditory and physiological commercial off-theshelf smart-sensors, hierarchical multisensorial fusion, and secured server architecture to combat Gender-based Violence by automatically detecting risky situations based on a multimodal intelligence engine and then triggering a protection protocol. Specifically, this research is focused onto the hardware and software design of one of the two edge-computing devices within Bindi. This is a bracelet integrating three physiological sensors, actuators, power monitoring integrated chips, and a System- On-Chip with wireless capabilities. Within this context, different embedded design space explorations are presented: embedded filtering evaluation, online physiological signal quality assessment, feature extraction, and power consumption analysis. The reported results in all these processes are successfully validated and, for some of them, even compared against physiological standard measurement equipment. Amongst the different obtained results regarding the embedded design and implementation within the bracelet of Bindi, it should be highlighted that its low power consumption provides a battery life to be approximately 40 hours when using a 500 mAh battery. Finally, the particularities of our use case and the scarcity of open multimodal datasets dealing with emotional immersive technology, labelling methodology considering the gender perspective, balanced stimuli distribution regarding the target emotions, and recovery processes based on the physiological signals of the volunteers to quantify and isolate the emotional activation between stimuli, led us to the definition and elaboration of Women and Emotion Multi-modal Affective Computing (WEMAC) dataset. This is a multimodal dataset in which 104 women who never experienced Gender-based Violence that performed different emotion-related stimuli visualisations in a laboratory environment. The previous fear binary classification systems were improved and applied to this novel multimodal dataset. For instance, the proposed multimodal fear recognition system using this dataset reports up to 60.20% and 67.59% for ACC and F1-score, respectively. These values represent a competitive result in comparison with the state-of-the-art that deal with similar multi-modal use cases. In general, this PhD thesis has opened a new research line within the research group under which it has been developed. Moreover, this work has established a solid base from which to expand knowledge and continue research targeting the generation of both mechanisms to help vulnerable groups and socially oriented technology.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: David Atienza Alonso.- Secretaria: Susana Patón Álvarez.- Vocal: Eduardo de la Torre Arnan