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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 ® .