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

Fear Detection in Multimodal affective computing: Physiological Signals versus Catecholamine Concentration

Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wearable devices are being developed for health or wellness tracking during daily life or sports activity. Likewise, other applications are being proposed for the early detection of risk situations involving sexual or violent aggressions, with the identification of panic or fear emotions. The use of other sources of information, such as video or audio signals will make multimodal affective computing a more powerful tool for emotion classification, improving the detection capability. There are other biological elements that have not been explored yet and that could provide additional information to better disentangle negative emotions, such as fear or panic. Catecholamines are hormones produced by the adrenal glands, two small glands located above the kidneys. These hormones are released in the body in response to physical or emotional stress. The main catecholamines, namely adrenaline, noradrenaline and dopamine have been analysed, as well as four physiological variables: skin temperature, electrodermal activity, blood volume pulse (to calculate heart rate activity. i.e., beats per minute) and respiration rate. This work presents a comparison of the results provided by the analysis of physiological signals in reference to catecholamine, from an experimental task with 21 female volunteers receiving audiovisual stimuli through an immersive environment in virtual reality. Artificial intelligence algorithms for fear classification with physiological variables and plasma catecholamine concentration levels have been proposed and tested. The best results have been obtained with the features extracted from the physiological variables. Adding catecholamine’s maximum variation during the five minutes after the video clip visualization, as well as adding the five measurements (1-min interval) of these levels, are not providing better performance in the classifiers.This research has been supported by the Madrid Governement (Comunidad de Madrid, Spain) under the ARTEMISA-UC3M-CM research project (reference 2020/00048/001), the EMPATIACM research project (reference Y2018/TCS-5046) and 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)

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

Neurotransmisores para mejorar la detección de situaciones de peligro en víctimas de violencia de género

Actas del V Congreso Internacional de Jóvenes Investigadorxs con perspectiva de género (Getafe, 3 - 5 de junio de 2020) organizado por el Instituto Universitario de Estudios de Género de la Universidad Carlos III de Madrid.La detección automática de emociones a través de información fisiológica, física y biológica del usuario y su entorno para ayudar a combatir la violencia contra la mujer plantea una serie de retos, entre ellos, la dificultad para clasificar las emociones debido a su carácter subjetivo. Por ello, con este proyecto se plantea la posibilidad de utilizar la medida de neurotransmisores en sangre para poder clasificarlas de una manera más precisa. En este trabajo se presenta los primeros resultados del experimento piloto realizado con 5 mujeres sanas a las que se les sometió a diferentes estímulos audiovisuales mientras se recogían muestras de sangre para ver la evolución temporal de la concentración de neurotransmisores después de experimentar emociones de miedo o de otro tipo. Los resultados obtenidos abren la puerta a un estudio de mayor envergadura con el que poder obtener un modelo que relacione las emociones con las concentraciones de neurotransmisores en sangre.Este trabajo forma parte de la investigación del proyecto 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 del 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

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

Investigación joven con perspectiva de género V

Actas del V Congreso Internacional de Jóvenes Investigadorxs con perspectiva de género (Getafe, 3 - 5 de junio de 2020) organizado por el Instituto Universitario de Estudios de Género de la Universidad Carlos III de Madrid