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

Fear recognition for women using a reduced set of physiological signals

This article belongs to the Section Biomedical Sensors.Emotion recognition is benefitting from the latest research into physiological monitoring and wireless communications, among other remarkable achievements. These technologies can indeed provide solutions to protect vulnerable people in scenarios such as personal assaults, the abuse of children or the elderly, gender violence or sexual aggression. Cyberphysical systems using smart sensors, artificial intelligence and wearable and inconspicuous devices can serve as bodyguards to detect these risky situations (through fear-related emotion detection) and automatically trigger a protection protocol. As expected, these systems should be trained and customized for each user to ensure the best possible performance, which undoubtedly requires a gender perspective. This paper presents a specialized fear recognition system for women based on a reduced set of physiological signals. The architecture proposed is characterized by the usage of three physiological sensors, lightweight binary classification and the conjunction of linear (temporal and frequency) and non-linear features. Moreover, a binary fear mapping strategy between dimensional and discrete emotional information based on emotional self-report data is implemented to avoid emotional bias. The architecture is evaluated using a public multi-modal physiological dataset with two approaches (subject-dependent and subject-independent models) focusing on the female participants. As a result, the proposal outperforms the state-of-the-art in fear recognition, achieving a recognition rate of up to 96.33% for the subject-dependent model.This activity is partially supported by Community of Madrid in the pluri-annual agreement with Universidad Carlos III de Madrid, in the line of action "Excelence with the University Faculty", V Regional Plan of Scientific Research and Technology Innovation 2016-2020, and by the Community of Madrid Region Government under the Synergic Program: EMPATIA-CM, Y2018/TCS-5046

Gender and Social Perspective in STEM Training: Artificial Intelligence Systems for Emotion Detection

En este trabajo se presenta cómo se combinan las disciplinas STEM y las Ciencias Sociales para generar una base de datos de estímulos audiovisuales con perspectiva de género que sea capaz de entrenar un sistema inteligente para la detección de situaciones de peligro en Víctimas de Violencia de Género. La metodología de selección de estímulos ha combinado dos procedimientos: 1) Según criterio investigador para aquellos relacionados con los miedos filogenéticos y de aprendizaje asociativo; 2) Según criterios basados en entrevistas en profundidad con expertos/as en violencia y validación de contenido a través de juezas expertas para aquellos relacionados con miedos de experiencias traumáticas de Violencia de Género. Los resultados señalan que la combinación metodológica mejora la selección de estímulos audiovisuales que forman parte de los experimentos, ajustándose mejor a las necesidades de las mujeres Víctimas de Violencia de Género.This paper presents how STEM disciplines and Social Sciences are combined to generate a database of audiovisual stimuli with a gender perspective capable of training an intelligent system for detecting dangerous situations in women who are Victims of Gender Violence. In the methodology of stimulus selection, it has been composed of two procedures: 1) According to research criteria for those related to phylogenetic and associative learning fears; 2) According to criteria based on indepth interviews with experts in violence and content validation through the technique of expert judges for those related to fears that have to do with traumatic experiences of Gender Violence. The results indicate that the methodological combination improves the selection of audiovisual stimuli that are part of the experiments, better adjusting to the needs of women Victims of Gender violence.EMPATÍA‐CM (Ref: Y2018/TCS‐5046) del programa de proyectos sinérgicos de I+D en nuevas y emergentes áreas científicas en la frontera de la ciencia y de naturaleza interdisciplinar, cofinanciada con los Programas Operativos del Fondo Social Europeo y del Fondo Europeo de Desarrollo Regional, 2014‐2020, de la Comunidad de Madrid; y el proyecto ARTEMISA‐CM‐UC3M (Ref: 2020/00048/001) de la convocatoria del programa de apoyo a la realización de Proyectos Interdisciplinares de I+D para jóvenes investigadores de la Universidad Carlos III de Madrid 2019‐2022

Gestión del conocimiento. Perspectiva multidisciplinaria. Volumen 10

El libro “Gestión del Conocimiento. Perspectiva Multidisciplinaria”, Volumen 10, de la Colección Unión Global, es resultado de investigaciones. Los capítulos del libro, son resultados de investigaciones desarrolladas por sus autores. El libro es una publicación internacional, seriada, continua, arbitrada de acceso abierto a todas las áreas del conocimiento, que cuenta con el esfuerzo de investigadores de varios países del mundo, orientada a contribuir con procesos de gestión del conocimiento científico, tecnológico y humanístico que consoliden la transformación del conocimiento en diferentes escenarios, tanto organizacionales como universitarios, para el desarrollo de habilidades cognitivas del quehacer diario. La gestión del conocimiento es un camino para consolidar una plataforma en las empresas públicas o privadas, entidades educativas, organizaciones no gubernamentales, ya sea generando políticas para todas las jerarquías o un modelo de gestión para la administración, donde es fundamental articular el conocimiento, los trabajadores, directivos, el espacio de trabajo, hacia la creación de ambientes propicios para el desarrollo integral de las instituciones

Síntesis de una década de diferentes acciones de restauración de cárcavas para olivares en el valle del Guadalquivir: descripción de situaciones, metodologías y costes

Este trabajo fue premiado en la III Edición Eduardo Pérez de Investigación en Olivicultura en el año 2020, http://www.premiodeinvestigacioneduardoperez.com/, organizado por la cooperativa olivarera San José de Lora de Estepa. Se trata de uno de los premios de investigación de mayor relevancia en el sector en España, siendo también el de mayor dotación económica.-- Los autores agradecen a la organización de dicho premio la autorización para reproducir de manera íntegra dicho trabajo en DIGITAL.CSIC, para facilitar su diseminación.Este trabajo sintetiza los resultados de siete actuaciones de control de cárcavas desarrolladas desde el año 2011 en el Valle medio del Guadalquivir por los autores, la mayoría en olivar y todas ellas aplicables a este cultivo. Aparte de una descripción de estas actuaciones y de los criterios principales de diseño, los autores desglosan los costes de actuación de las mismas, que se mueven en el rango de entre 39 y 52 € por metro lineal de cárcava restaurada cuando es necesario utilizar diques de retención, y entre 4 y 14 € por metro lineal cuando es posible utilizar la opción del canal vegetado (que debemos recordar no se recomienda para áreas aportadoras muy grandes). Este trabajo demuestra que el control de cárcavas en olivar es factible utilizando la información técnica hoy disponible y recursos técnicos limitados (excepto en cárcavas de gran tamaño superiores a 2.5-3 m de profundidad). Este trabajo pretende ser un punto de entrada a dichas. No obstante, los costes de ejecución de las diferentes alternativas de control recuerdan la conveniencia de actuar de manera preventiva para minimizarlos en las zonas de mayor riesgo de formación de cárcavas hoy fácilmente identificables.Igualmente se reconoce la colaboración de los proyectos y los proyectos AGL2009-1236-C03-01 (Ministerio de Ciencia e Innovación), AGL2015-65036-C3-1-R (Ministerio de Economía y Competitividad), SHUi (Comisión Europea, GA 773903) y la red RESEL (Ministerio de Medio Ambiente, Rural y Marino).Peer reviewe

Bindi: Affective internet of things to combat gender-based violence

The main research motivation of this article is the fight against gender-based violence and achieving gender equality from a technological perspective. The solution proposed in this work goes beyond currently existing panic buttons, needing to be manually operated by the victims under difficult circumstances. Instead, Bindi, our end-to-end autonomous multimodal system, relies on artificial intelligence methods to automatically identify violent situations, based on detecting fear-related emotions, and trigger a protection protocol, if necessary. To this end, Bindi integrates modern state-of-the-art technologies, such as the Internet of Bodies, affective computing, and cyber-physical systems, leveraging: 1) affective Internet of Things (IoT) with auditory and physiological commercial off-the-shelf smart sensors embedded in wearable devices; 2) hierarchical multisensorial information fusion; and 3) the edge-fog-cloud IoT architecture. This solution is evaluated using our own data set named WEMAC, a very recently collected and freely available collection of data comprising the auditory and physiological responses of 47 women to several emotions elicited by using a virtual reality environment. On this basis, this work provides an analysis of multimodal late fusion strategies to combine the physiological and speech data processing pipelines to identify the best intelligence engine strategy for Bindi. In particular, the best data fusion strategy reports an overall fear classification accuracy of 63.61% for a subject-independent approach. Both a power consumption study and an audio data processing pipeline to detect violent acoustic events complement this analysis. This research is intended as an initial multimodal baseline that facilitates further work with real-life elicited fear in women.This work was supported in part by the Department of Research and Innovation of Madrid Regional Authority, in the EMPATIA-CM Research Project (Reference Y2018/TCS-5046) funded by MCIN/AEI/10.13039/501100011033 under Grant PDC2021-121071-I00; in part by the European Union NextGenerationEU/PRTR in part by the Spanish Ministry of Universities with the FPU under Grant FPU19/00448; and in part by the Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M26), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

Perspectiva de género y social en las STEM: La construcción de sistemas inteligentes para detección de emociones

This paper presents how STEM disciplines and Social Sciences are combined to generate a database of audiovisual stimuli with a gender perspective capable of training an intelligent system for detecting dangerous situations in women who are Victims of Gender Violence. In the methodology of stimulus selection, it has been composed of two procedures: 1) According to research criteria for those related to phylogenetic and associative learning fears; 2) According to criteria based on in‐depth interviews with experts in violence and content validation through the technique of expert judges for those related to fears that have to do with traumatic experiences of Gender Violence. The results indicate that the methodological combination improves the selection of audiovisual stimuli that are part of the experiments, better adjusting to the needs of women Victims of Gender violence.En este trabajo se presenta cómo se combinan las disciplinas STEM y las Ciencias Sociales para generar una base de datos de estímulos audiovisuales con perspectiva de género que sea capaz de entrenar un sistema inteligente para la detección de situaciones de peligro en Víctimas de Violencia de Género. La metodología de selección de estímulos ha combinado dos procedimientos: 1) Según criterio investigador para aquellos relacionados con los miedos filogenéticos y de aprendizaje asociativo; 2) Según criterios basados en entrevistas en profundidad con expertos/as en violencia y validación de contenido a través de juezas expertas para aquellos relacionados con miedos de experiencias traumáticas de Violencia de Género. Los resultados señalan que la combinación metodológica mejora la selección de estímulos audiovisuales que forman parte de los experimentos, ajustándose mejor a las necesidades de las mujeres Víctimas de Violencia de Género

UC3M4Safety Database description

EMPATIA-CM (Comprehensive Protection of Gender-based Violence Victims through Multimodal Affective Computing) is a research project that aims to generally understand the reactions of Gender-based Violence Victims to situations of danger, 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 most optimal way possible. The project is divided into five objectives that demonstrate the need and added value of the multidisciplinary approach: * Understand the reaction mechanisms of the Gender-based Violence Victims to risky situations. * Investigate, design and verify algorithms to automatically detect Risk Situations in Gender-based Violence Victims. * Design and implement the Automatic Detection System for Risk Situations in Gender-based Violence Victims. * Investigate a new protocol to protect Gender-based Violence Victims with a holistic approach. * Use the data collected by the System for detecting hazardous situations in Gender-based Violence Victims.This project is funded by the Comunidad de Madrid, Consejería de Ciencia, Universidades e Innovación, Programa de proyectos sinérgicos de I+D en nuevas y emergentes áreas científicas en la frontera de la ciencia y de naturaleza interdisciplinar, cofinanciada con los Programas Operativos del Fondo Social Europeo y del Fondo Europeo de Desarrollo Regional, 2014-2020, de la Comunidad de Madrid (EMPATÍA-CM, Ref: Y2018/TCS-5046