Elena Romero

Emotion classification and assessment could be a potential tool to enhance women's protection in gender-based violence situations through fear-recognition systems. Despite advances in this field, female-focused studies of emotion recognition are lacking, even if there is evidence of socialisation-related gender differences in emotion recognition and expression. To create a women-oriented EEG database focused on fear, this research explores different preprocessing, feature extraction, and selection methods, along with machine learning classifiers, to establish an effective fear recognition pipeline. The findings suggest that using a Random Forest with a 4 s window size, features selected using the MRMR algorithm, self-reported labels, and incorporating the gamma frequency band yield the optimal pipeline. To validate said results, the pipeline is tested with strict data splits (LOSO, LASO, and LOVO) as well as with a public database (SEED IV). Overall, these insights contribute to the development of EEG-based fear recognition systems specifically tailored to women's protection.

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.

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.

Identification of emotions triggered by different sourced stimuli can be applied to automatic systems that help, relieve or protect vulnerable groups of population. The selection of the best stimuli allows to train these artificial intelligence-based systems in a more efficient and precise manner in order to discern different risky situations, characterized either by panic or fear emotions, in a clear and accurate way. The presented research study has produced a dataset of audiovisual stimuli (UC3M4Safety database) that triggers a complete range of emotions, with a high level of agreement and with a discrete emotional categorization, as well as quantitative categorization in the Pleasure-Arousal-Dominance Affective space. This database is adequate for the machine learning algorithms contained in these automatic systems. Furthermore, this work analyses the effects of gender in the emotion elicitation under audiovisual stimuli, which can help to better design the final solution. Particularly, the focus is set on emotional responses to audiovisual stimuli reproducing situations experienced by women, such as gender-based violence. A statistical study of gender differences in emotional response was carried out on 1332 participants (811 women and 521 men). The average responses per video is around 84 (SD = 22). Data analysis was carried out with RStudio^®.

Wireless Sensor Networks (WSNs) are a growing research area as a large of number portable devices are being developed. This fact makes operating systems (OS) useful to homogenize the development of these devices, to reduce design times, and to provide tools for developing complex applications. This work presents an operating system scheduler for resource‐constraint wireless devices, which adapts the tasks scheduling in changing environments. The proposed adaptive scheduler allows dynamically delaying the execution of low priority tasks while maintaining real‐time capabilities on high priority ones. Therefore, the scheduler is useful in nodes with rechargeable batteries, as it reduces its energy consumption when battery level is low, by delaying the least critical tasks. The adaptive scheduler has been implemented and tested in real nodes, and the results show that the nodes lifetime could be increased up to 70% in some scenarios at the expense of increasing latency of low priority tasks.

Security is critical for wireless sensor networks (WSN)deployed in hostile environments since many types of attacks could reduce the trust on the global functioning of any WSN. Many solutions have been proposed to secure communications for WSNs and most of them rely on a centralized component which behaves as a certificate authority. We propose in this paper a distributed solution able to ensure authentication of nodes at any time without having any on-line access to a certificate authority. Each node will be equipped with a Trusted Platform Module (TPM) which is able to store keys with security. Each node will have its own public key and private key pair in the TPM and a certificate of the public key. The certificate is issued off-line when setting-up the node. When a node communicates with another, it has to sign the message with its own private key (done securely by the TPM) and sends the message, the signature and the certificate of the public key. The evaluation of the solution has been done using simulation and the overhead added by integrating authentication does not exceed 15% of energy consumption.

The growth of Device-to-Device (D2D) communications and the extensive use of Wireless Sensor Networks (WSNs) bring new problems such as spectral coexistence and spectrum saturation. Cognitive Radio (CR) appears as a paradigm to solve these problems. The introduction of CR into WSNs as a solution to the spectrum utilization problem could be used not only to increase the reliability of communications, but also to optimize energy consumption. The contribution of this paper is a cognitive lightweight strategy based on game theory and collaboration proposed to save energy consumption in Cognitive Wireless Sensor Networks (CWSNs). The proposed strategy takes advantage of the two main capabilities of CWSNs, the ability to adapt communication parameters, specifically channel allocation, and collaboration among devices. The decision making is modeled through a light noncooperative game designed for low resources networks. Despite the fact that the game used is a noncooperative game, the decision process takes advantage of the collaboration among CWSN nodes in a distributed way. Simulations over Castalia Simulator have been carried out in order to validate the strategy in different scenarios with different interference schemes showing increases in energy savings around 50% depending on the scenario.

With the fast expansion of Wireless Sensor Networks (WSNs) and the increasing emergence of new scenarios and applications, extending their lifetime is crucial. Usually, WSN developers use generic algorithms and deployment arrangements without considering the specific needs of their network's application. Taking this application into account can result in a significant enhancement of performance, both in terms of increasing the lifetime and improving the quality of service (QoS). Furthermore, most WSN developers do not consider the final behavior of the network when nodes are nearly depleted and resources are scarce. In this paper we introduce the concept of the controlled degradation of the network, to refer to the strategies aimed at managing this deterioration process. The existing definitions of the network lifetime do not normally consider the specific purpose or application for which the WSN is intended. Thus, they are not suited to describe and test controlled degradation strategies. Consequently, we propose a new formal and comprehensive definition for the network lifetime. Finally, this work presents a proof of concept that confirms our statements and reinforces the potential of this research line.