Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the Fifth SDG is a call for action to 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. Within this context, the UC3M4Safety research team aims to develop Bindi. This is a cyber-physical system which includes embedded Artificial Intelligence algorithms, for user real-time monitoring towards the detection of affective states, with the ultimate goal of achieving the early detection of risk situations for women. On this basis, we make use of wearable affective computing including smart sensors, data encryption for secure and accurate collection of presumed crime evidence, as well as the remote connection to protecting agents. Towards the development of such system, the recordings of different laboratory and into-the-wild datasets are in process. These are contained within the UC3M4Safety Database. Thus, this paper presents and details the first release of WEMAC, a novel multi-modal dataset, which comprises a laboratory-based experiment for 47 women volunteers that were exposed to validated audio-visual stimuli to induce real emotions by using a virtual reality headset while physiological, speech signals and self-reports were acquired and collected. We believe this dataset will serve and assist research on multi-modal affective computing using physiological and speech information.

WEMAC: Women and Emotion Multi-modal Affective Computing dataset

Audiovisual communication is greatly contributing to the emerging research field of affective computing. The use of audiovisual stimuli within immersive virtual reality environments is providing very intense emotional reactions, which provoke spontaneous physical and physiological changes that can be assimilated into real responses. In order to ensure high-quality recognition, the artificial intelligence (AI) system must be trained with adequate data sets, including not only those gathered by smart sensors but also the tags related to the elicited emotion. Currently, there are very few techniques available for the labeling of emotions. Among them, the Self-Assessment Manikin (SAM) devised by Lang is one of the most popular. This study shows experimentally that the graphic proposal for the original SAM labelling system, as devised by Lang, is not neutral to gender and contains gender biases in its design and representation. Therefore, a new graphic design has been proposed and tested according to the guidelines of expert judges. The results of the experiment show an overall improvement in the labeling of emotions in the pleasure–arousal–dominance (PAD) affective space, particularly, for women. This research proves the relevance of applying the gender perspective in the validation of tools used throughout the years.

/pdf/gender-biases-in-the-training-methods-of-affective-computing-2wex4pf8.pdf

Gender biases in the training methods of affective computing: Redesign and validation of the Self-Assessment Manikin in measuring emotions via audiovisual clips

Domestic violence is an issue of great importance that transcends socioeconomic backgrounds and cultural boundaries.It stems from factors such as power struggle, mental health issue, financial hardship, substance abuse, among others.Technology can play a vital role in not only detecting and mitigating instances of domestic violence, but also providing essential support and resources to its victims.This review paper examines the latest technological innovations in tackling domestic violence.It describes a range of technology platforms and tools, and discusses their capabilities and shortcomings.Firstly, the review methodology is given including aims and objectives of the review, search strategy, and selection of sources.Next, technological innovations in the context of domestic violence are defined.Then, the paper presents technological approaches developed to tackle domestic violence through: (i) analysis of data shared through digital platforms is presented, (ii) analysis of data captured by ambient sensors, (iii) analysis of data captured by smartphones, (iv) analysis of data captured by wearable sensors, (v) protecting online activities, (vi) preventing non-contact harassment, (vii) anti-stalking and monitoring measures, and (viii) virtual reality approaches.Next, discussions on the capabilities and applications of these technological innovations across different aspects of domestic violence are presented.The paper also addresses the challenges and limitations associated with these innovations, and gives future directions and recommendations for further research and development. 

Technological Innovations for Tackling Domestic Violence

The design of wearable devices has been approached from many perspectives over the years, mainly from a functionality, electronics, mechanics, usability, wearability, or product design perspective. However, there is a missing point in these approaches: the gender perspective. Gender intersects with every approach and, considering the interrelationships and dependencies, can achieve a better adherence, reach a wider audience, and even change the conception of the wearables design paradigm. The electronics design addressed from a gender perspective must consider both the morphological and anatomical impacts and those emanating from socialization. This paper presents an analysis of the different factors to consider when designing the electronics of a wearable device, including the functionality to implement, sensors, communications, or the location, together with their interdependencies, and proposes a user-centered methodology that contemplates a gender perspective at every stage. Finally, we present a use case that validates the proposed methodology in a real design of a wearable device for the prevention of gender-based violence cases. For the application of the methodology, 59 experts have been interviewed, 300 verbatims have been extracted and analyzed, a dataset from the data of 100 women has been created and the wearable devices have been tested for a week by 15 users. The electronics design needs to be addressed from a multidisciplinary approach, by rethinking the decisions taken for granted and analyzing the implications and interrelationships from a gender perspective. We need to enroll more diverse people at every design stage and include gender as one of the variables to study.

Electronic Design for Wearables Devices Addressed from a Gender Perspective: Cross-Influences and a Methodological Proposal

Suspended dust above the Martian surface is an important element in Martian climatology. In the frame of the Exomars’22 mission, we developed a dust sensor instrument, designed to provide size parameters of dust particles suspended in Mars surface from the light scattered by the particles. Thus, to interpret the data of the dust sensor, we need a method to calculate the theoretical optical power dispersed by the particles and, therefore, the theoretical signal obtained by the instrument. This signal depends on the suspended particles and on the instrument configuration. In this paper, we present a new method to calculate the angular weighting function (Wf) for scattering sensors. Wf encompasses the scattering angles measured by the sensor and depends only on the instrument and not on the suspended particles. To calculate this Wf, we use fundamental radiometry principles and an appropriate coordinate system, where one coordinate is the scattering angle. The method is applied to the dust sensor instrument and compared with other methods. The comparison highlights the advantages of the proposed method since it avoids using an ideal sampling volume, preserves the radiometric meaning, and avoids instrument calibration. The effectiveness of the method makes it a valuable tool for the design of scattering instruments and also for the interpretation of their data.

/pdf/new-method-to-calculate-the-angular-weighting-function-for-a-2j0tzfks.pdf

New Method to Calculate the Angular Weighting Function for a Scattering Instrument: Application to a Dust Sensor on Mars

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.

https://ieeexplore.ieee.org/ielx7/6488907/6702522/09780201.pdf

Bindi: Affective Internet of Things to Combat Gender-Based Violence

Exploring life conditions on the near-Earth planets and satellites before carrying out human missions is an important task for space agencies. For that purpose, scientific space missions usually include instruments to measure climatological variables. Within this space instrumentation and measurement context, dust sensors (DSs) aim to measure dust particles in suspension and provide valuable information for persons and equipment life conditions, while they must deal with low signal-to-noise ratios (SNRs). For example, the Exomars mission is focused to characterize the weather on Mars surface and include up to four DSs based on different technologies: infrared (IR), laser, interferometry, impact sensors, and electric field activity sensors. Due to the tight budget in terms of area, weight, power consumption, and data budget in aerospace instruments, as well as ionizing radiation and extreme temperatures, current solutions present low scalability and configurability. In this article, a novel system proposal that extracts valuable information from noisy signals obtained from IR sensors aimed to measure airborne dust is presented. The solution provides competitive capabilities in terms of power consumption, data budget, SNR, and reconfigurability. It has been implemented in a rad-hard Microsemi field programmable gate array (FPGA) (RT54SX32S). Therefore, robustness and scalability are guaranteed. The results reported a maximum power consumption of up to 141 mA (@12 Vdc), a sensitivity of 19.5 mV (input signal), and a data budget of 32 B/s. This research possesses great potential to further instruments not only in planetary exploration but also at Earth applications.

Optimized Design and Implementation of Digital Lock-In for Planetary Exploration Sensors

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Bindi: Affective Internet of Things to Combat Gender-Based Violence

Optimized Design and Implementation of Digital Lock-In for Planetary Exploration Sensors