71 research outputs found
Tecnologías I+D+I para la igualdad. Soluciones, perspectivas y retos
Esta publicación está financiada por el Instituto de las Mujeres, por la resolución de fecha 17 de diciembre de 2021, de la Convocatoria de Posgrados y Actividades año 2021
Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG Quality Assessment for the Edge
Most of today's wearable technology provides seamless cardiac activity
monitoring. Specifically, the vast majority employ Photoplethysmography (PPG)
sensors to acquire blood volume pulse information, which is further analysed to
extract useful and physiologically related features. Nevertheless, PPG-based
signal reliability presents different challenges that strongly affect such data
processing. This is mainly related to the fact of PPG morphological wave
distortion due to motion artefacts, which can lead to erroneous interpretation
of the extracted cardiac-related features. On this basis, in this paper, we
propose a novel personalised and adjustable Interval Type-2 Fuzzy Logic System
(IT2FLS) for assessing the quality of PPG signals. The proposed system employs
a personalised approach to adapt the IT2FLS parameters to the unique
characteristics of each individual's PPG signals.Additionally, the system
provides adjustable levels of personalisation, allowing healthcare providers to
adjust the system to meet specific requirements for different applications. The
proposed system obtained up to 93.72\% for average accuracy during validation.
The presented system has the potential to enable ultra-low complexity and
real-time PPG quality assessment, improving the accuracy and reliability of
PPG-based health monitoring systems at the edge
Introduccción
Women's Studies, Feminists and Gender Studies have raised relevant theoretical contributions to scientific knowledge, that are essential to understand the surrounding reality. Among the most important contributions is the consolidation of the gender category and feminist epistemology as a way for a more inclusive and less segregationist science. An essential analysis tool to understand the processes of inequality between men and women, and the development of a critical point of view that questions the traditional and androcentric perspective of scientific knowledge.Los Estudios de Género, Feministas y de las Mujeres han introducido importantes aportaciones teóricas al conocimiento científico sin las cuales sería imposible comprender la realidad que nos rodea. Entre las aportaciones más importantes se encuentra la consolidación de la categoría género y la epistemología feminista como vía para una ciencia más inclusiva y menos segregacionista. Una herramienta de análisis indispensable para comprender los procesos de desigualdad entre hombres y mujeres, y el desarrollo de un punto de vista crítico que cuestione la tradicional y androcéntrica forma de hacer ciencia
EMPATÍA-CM: protEcción integral de las víctimas de violencia de género Mediante comPutación AfecTIva multimodal
The EMPATIACM project begins in January 2019, executed by a multidisciplinary team formed by the Institute of Gender Studies in Universidad Carlos III of Madrid (UC3M-IEG, with research staff from several branches of the Social Sciences and Humanities) and by the UC3M-TEC group (formed in turn by research personnel from several branches of Engineering), with the fundamental objective of understanding the reactions of victims of Gender-based Violence (GBV) to dangerous situations, 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 best possible way. This objective is divided into six sub-objectives, which demonstrate the need and added value of the multidisciplinary approach.
EMPATIA combines cyberphysical systems and affective computing proposing a comprehensive protocol that improves the protection of victims of gender violence with a solution capable of automatically, immediately and remotely warning of risk situations.
Close to the end of the project, it is time to assess the work done and the results achieved, presenting the contributions in each of the sub-objectives raised in the project proposal.El proyecto EMPATIACM comienza en enero de 2019, a cargo de un equipo multidisciplinar formado por el Instituto de Estudios de Género de la Universidad Carlos III de Madrid (UC3M-IEG, con personal investigador de varias ramas de las Ciencias Sociales y las Humanidades) y por el grupo UC3M-TEC (formado a su vez por personal investigador de varias ramas de la Ingeniería), con el objetivo fundamental de entender las reacciones de las víctimas de la Violencia de Género (VG) ante situaciones de peligro, generar mecanismos de detección automática de estas situaciones y estudiar cómo reaccionar de forma integral, coordinada y eficaz para protegerlas de la mejor forma posible. Este objetivo se divide en seis subobjetivos, que demuestran la necesidad y valor añadido del enfoque multidisciplinar.
EMPATIA aúna sistemas ciberfísicos y computación afectiva proponiendo un protocolo integral que mejora la protección de las víctimas de violencia de género con una solución capaz de avisar de forma automática, inmediata y remota de situaciones de riesgo.
Cerca del final del proyecto, es el momento de hacer balance del trabajo realizado y los resultados alcanzados, presentando las aportaciones en cada uno de los subobjetivos planteados en la propuesta del proyecto
Extensive SEU impact analysis of a PIC microprocessor for selective hardening
In order to increase the robustness of a circuit against SEUs, fault injection is commonly used to locate weak areas. autonomous emulation is a very powerful tool to locate these areas by executing huge fault injection campaigns. In this work, fault injection has been extensively applied to a PIC18 microprocessor, while executing three different workloads. A 80 million fault campaign has been performed, and results show that a failure rate lower than 1% can be obtained by hardening a 24% of the circuit flip-flops, for the given applications
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)
Autonomous fault emulation: a new FPGA-based acceleration system for hardness evaluation
The appearance of nanometer technologies has produced a significant increase of integrated circuit sensitivity to radiation, making the occurrence of soft errors much more frequent, not only in applications working in harsh environments, like aerospace circuits, but also for applications working at the earth surface. Therefore, hardened circuits are currently demanded in many applications where fault tolerance was not a concern in the very near past. To this purpose, efficient hardness evaluation solutions are required to deal with the increasing size and complexity of modern VLSI circuits. In this paper, a very fast and cost effective solution for SEU sensitivity evaluation is presented. The proposed approach uses FPGA emulation in an autonomous manner to fully exploit the FPGA emulation speed. Three different techniques to implement it are proposed and analyzed. Experimental results show that the proposed Autonomous Emulation approach can reach execution rates higher than one million faults per second, providing a performance improvement of two orders of magnitude with respect to previous approaches. These rates give way to consider very large fault injection campaigns that were not possible in the past.This work was supported by the Directorate of Research of Madrid Community Government, Spain (Code 07/0052/2003 2) and by the European Commission and Spanish Government under MEDEA+ Project (PARACHUTE-2A701) and PROFIT Project (CIRCE-FIT-330100-2005-60)
Solar Energy Harvesting to Improve Capabilities of Wearable Devices
The market of wearable devices has been growing over the past decades. Smart wearables
are usually part of IoT (Internet of things) systems and include many functionalities such as
physiological sensors, processing units and wireless communications, that are useful in fields like
healthcare, activity tracking and sports, among others. The number of functions that wearables
have are increasing all the time. This result in an increase in power consumption and more frequent
recharges of the battery. A good option to solve this problem is using energy harvesting so that the
energy available in the environment is used as a backup power source. In this paper, an energy
harvesting system for solar energy with a flexible battery, a semi-flexible solar harvester module and
a BLE (Bluetooth® Low Energy) microprocessor module is presented as a proof-of-concept for the
future integration of solar energy harvesting in a real wearable smart device. The designed device
was tested under different circumstances to estimate the increase in battery lifetime during common
daily routines. For this purpose, a procedure for testing energy harvesting solutions, based on solar
energy, in wearable devices has been proposed. The main result obtained is that the device could
permanently work if the solar cells received a significant amount of direct sunlight for 6 h every day.
Moreover, in real-life scenarios, the device was able to generate a minimum and a maximum power
of 27.8 mW and 159.1 mW, respectively. For the wearable system selected, Bindi, the dynamic tests
emulating daily routines has provided increases in the state of charge from 19% (winter cloudy days,
4 solar cells) to 53% (spring sunny days, 2 solar cells).
Keywords: energy harvesting; internet of things; physiologicalThis research was funded by the Department of Research and Innovation of Madrid
Regional Authority, in the EMPATIA-CM research project (reference Y2018/TCS-5046). This work has
been partially supported by the European Union—NextGenerationEU, with the SAPIENTIAE4BINDI
project “Proof of Concept” 2021. (Ref: PDC2021-121071-I00/AEI/10.13039/501100011033). This
work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under 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)
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