Cardiovascular information for improving biometric recognition

Mención Internacional en el título de doctorThe improvements of the last two decades in data modeling and computing have lead to new biometric modalities. The Electrocardiogram (ECG) modality is part of them, and has been mainly researched by using public databases related to medical training. Despite of being useful for initial approaches, they are not representative of a real biometric environment. In addition, publishing and creating a new database is none trivial due to human resources and data protection laws. The main goal of this thesis is to successfully use ECG as a biometric signal while getting closer to the real case scenario. Every experiment considers low computational calculations and transformations to help in potential portability. The core experiments in this work come from a private database with different positions, time and heart rate scenarios. An initial segmentation evaluation is achieved with the help of fiducial point detection which determines the QRS selection as the input data for all the experiments. The approach of training a model per user (open-set) is tested with different machine learning algorithms, only getting an acceptable result with Gaussian Mixture Models (GMM). However, the concept of training all users in one model (closed-set) shows more potential with Linear Discriminant Analysis (LDA), whose results were improved in 40%. The results with LDA are also tested as a multi-modality technique, decreasing the Equal Error Rate (EER) of fingerprint verification in up to 70.64% with score fusion, and reaching 0% in Protection Attack Detection (PAD). The Multilayer Perceptron (MLP) algorithm enhances these results in verification while applying the first differentiation to the signal. The network optimization is achieved with EER as an observation metric, and improves the results of LDA in 22% for the worst case scenario, and decreases the EER to 0% in the best case. Complexity is added creating a Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) based network, BioECG. The tuning process is achieved without extra feature transformation and is evaluated through accuracy, aiming for good identification. The inclusion of a second day of enrollment in improves results from MLP, reaching the overall lowest results of 0.009%–1.352% in EER. Throughout the use of good quality signals, position changes did not noticeably impact the verification. In addition, collecting data in a different day or in a different hour did not clearly affect the performance. Moreover, modifying the verification process based on attempts, improves the overall results, up to reach a 0% EER when applying BioECG. Finally, to get closer to a real scenario, a smartband prototype is used to collect new databases. A private database with limited scenarios but controlled data, and another local database with a wider range of scenarios and days, and with a more relaxed use of the device. Applying the concepts of first differentiation and MLP, these signals required the Stationary Wavelet Transform (SWT) and new fiducial point detection to improve their results. The first database gave subtle chances of being used in identification with up to 78.2% accuracy, but the latter was completely discarded for this purpose. These realistic experiments show the impact of a low fidelity sensor, even considering the same modifications in previous successful experiments with better quality data, reaching up to 13.530% EER. In the second database, results reach a range of 0.068%–31.669% EER. This type of sensor is affected by heart rate changes, but also by position variations, given its sensitivity to movement.Las mejoras en modelado de datos y computación de las últimas dos décadas, han llevado a la creación de nuevas modalidades biométricas. La modalidad de electrocardiograma (ECG) es una de ellas, la cual se ha investigado usando bases de datos públicas que fueron creadas para entrenamiento de profesional médico. Aunque estos datos han sido útiles para los estados iniciales de la modalidad, no son representativos de un entorno biométrico real. Además, publicar y crear bases de datos nuevas son problemas no triviales debido a los recursos humanos y las leyes de protección de datos. El principal objetivo de esta tesis es usar exitosamente datos de ECG como señales biométricas a la vez que nos acercamos a un escenario realista. Cada experimento considera cálculos y transformadas de bajo coste computacional para ayudar en su potencial uso en aparatos móviles. Los principales experimentos de este trabajo se producen con una base de datos privada con diferentes escenarios en términos de postura, tiempo y frecuencia cardíaca. Con ella se evalúan las diferentes seleccións del complejo QRS mediante detección de puntos fiduciales, lo cual servirá como datos de entrada para el resto de experimentos. El enfoque de entrenar un modelo por usuario (open-set) se prueba con diferentes algoritmos de aprendizaje máquina (machine learning), obteniendo resultados aceptables únicamente mediante el uso de modelos de mezcla de Gaussianas (Gaussian Mixture Models, GMM). Sin embargo, el concepto de entrenar un modelo con todos los usuarios (closed-set) demuestra mayor potencial con Linear Discriminant Analysis (Análisis de Discriminante Lineal, LDA), cuyos resultados mejoran en un 40%. Los resultados de LDA también se utilizan como técnica multi-modal, disminuyendo la Equal Error Rate (Tasa de Igual Error, EER) de la verificación mediante huella en hasta un 70.64% con fusión de puntuación, y llegando a un sistema con un 0% de EER en Detección de Ataques de Presentación (Presentation Attack Detection, PAD). El algoritmo de Perceptrón Multicapa (Multilayer Perceptron, MLP) mejora los resultados previos en verificación aplicando la primera derivada a la señal. La optimización de la red se consigue en base a su EER, mejora la de LDA en hasta un 22% en el peor caso, y la lleva hasta un 0% en el mejor caso. Se añade complejidad creando una red neural convolucional (Convolutional Neural Network, CNN) con una red de memoria a largo-corto plazo (Long-Short Term Memory, LSTM), llamada BioECG. El proceso de ajuste de hiperparámetros se lleva acabo sin transformaciones y se evalúa observando la accuracy (precisión), para mejorar la identificación. Sin embargo, incluir un segundo día de registro (enrollment) con BioECG, estos resultados mejoran hasta un 74% para el peor caso, llegando a los resultados más bajos hasta el momento con 0.009%–1.352% en la EER. Durante el uso de señales de buena calidad, los cambios de postura no afectaron notablemente a la verificación. Además, adquirir los datos en días u horas diferentes tampoco afectó claramente a los resultados. Asimismo, modificar el proceso de verificación en base a intentos también produce mejoría en todos los resultados, hasta el punto de llegar a un 0% de EER cuando se aplica BioECG. Finalmente, para acercarnos al caso más realista, se usa un prototipo de pulsera para capturar nuevas bases de datos. Una base de datos privada con escenarios limitados pero datos más controlados, y otra base de datos local con más espectro de escenarios y días y un uso del dispositivo más relajado. Para estos datos se aplican los conceptos de primera diferenciación en MLP, cuyas señales requieren la Transformada de Wavelet Estacionaria (Stationary Wavelet Transform, SWT) y un detector de puntos fiduciales para mejorar los resultados. La primera base de datos da opciones a ser usada para identificación con un máximo de precisión del 78.2%, pero la segunda se descartó completamente para este propósito. Estos experimentos más realistas demuestran el impact de tener un sensor de baja fidelidad, incluso considerando las mismas modificaciones que previamente tuvieron buenos resultados en datos mejores, llegando a un 13.530% de EER. En la segunda base de datos, los resultados llegan a un rango de 0.068%–31.669% en EER. Este tipo de sensor se ve afectado por las variaciones de frecuencia cardíaca, pero también por el cambio de posición, dado que es más sensible al movimiento.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Cristina Conde Vilda.- Secretario: Mariano López García.- Vocal: Young-Bin Know

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy

Sensores de fibra ótica para arquiteturas e-Health

In this work, optical fiber sensors were developed and optimized for biomedical applications in wearable and non-intrusive and/or invisible solutions. As it was intended that the developed devices would not interfere with the user's movements and their daily life, the fibre optic sensors presented several advantages when compared to conventional electronic sensors, among others, the following stand out: size and reduced weight, biocompatibility, safety, immunity to electromagnetic interference and high sensitivity. In a first step, wearable devices with fibre optic sensors based in Fiber Bragg gratings (FBG) were developed to be incorporated into insoles to monitor different walking parameters based on the analysis of the pressure exerted on several areas of the insole. Still within this theme, other sensors were developed using the same sensing technology, but capable of monitoring pressure and shear forces simultaneously. This work was pioneering and allowed monitoring one of the main causes of foot ulceration in people with diabetes: shear. At a later stage, the study focused on the issue related with the appearance of ulcers in people with reduced mobility and wheelchair users. In order to contribute to the mitigation of this scourge, a system was developed composed of a network of fibre optic sensors capable of monitoring the pressure at various points of the wheelchair. It not only measures the pressure at each point, but also monitors the posture of the wheelchair user and advises him/her to change posture regularly to reduce the probability of this pathology occurring. Still within this application, another work was developed where the sensor not only monitored the pressure but also the temperature in each of the analysis points, thus indirectly measuring shear. In another phase, plastic fibre optic sensors were studied and developed to monitor the body posture of an office chair user. Simultaneously, software was developed capable of monitoring and showing the user all the acquired data in real time and warning for incorrect postures, as well as advising for work breaks. In a fourth phase, the study focused on the development of highly sensitive sensors embedded in materials printed by a 3D printer. The sensor was composed of an optical fibre with a FBG and the sensor body of a flexible polymeric material called "Flexible". This material was printed on a 3D printer and during its printing the optical fibre was incorporated. The sensor proved to be highly sensitive and was able to monitor respiratory and cardiac rate, both in wearable solutions (chest and wrist) and in "invisible" solutions (office chair).Neste trabalho foram desenvolvidos e otimizados sensores em fibra ótica para aplicações biomédicas em soluções vestíveis e não intrusivas/ou invisíveis. Tendo em conta que se pretende que os dispositivos desenvolvidos não interfiram com os movimentos e o dia-a-dia do utilizador, os sensores de fibra ótica apresentam inúmeras vantagens quando comparados com os sensores eletrónicos convencionais, de entre várias, destacam-se: tamanho e peso reduzido, biocompatibilidade, segurança, imunidade a interferências eletromagnéticas e elevada sensibilidade. Numa primeira etapa, foram desenvolvidos dispositivos vestíveis com sensores de fibra ótica baseados em redes de Bragg (FBG) para incorporar em palmilhas de modo a monitorizar diferentes parâmetros da marcha com base na análise da pressão exercida em várias zonas da palmilha. Ainda no âmbito deste tema, adicionalmente, foram desenvolvidos sensores utilizando a mesma tecnologia de sensoriamento, mas capazes de monitorizar simultaneamente pressão e forças de cisalhamento. Este trabalho foi pioneiro e permitiu monitorizar um dos principais responsáveis pela ulceração dos pés em pessoas com diabetes: o cisalhamento. Numa fase posterior, o estudo centrou-se na temática relacionada com o aparecimento de úlceras em pessoas com mobilidade reduzida e utilizadores de cadeiras de rodas. De modo a contribuir para a mitigação deste flagelo, procurou-se desenvolver um sistema composto por uma rede de sensores de fibra ótica capaz de monitorizar a pressão em vários pontos de uma cadeira de rodas e não só aferir a pressão em cada ponto, mas monitorizar a postura do cadeirante e aconselhá-lo a mudar de postura com regularidade, de modo a diminuir a probabilidade de ocorrência desta patologia. Ainda dentro desta aplicação, foi publicado um outro trabalho onde o sensor não só monitoriza a pressão como também a temperatura em cada um dos pontos de análise, conseguindo aferir assim indiretamente o cisalhamento. Numa outra fase, foi realizado o estudo e desenvolvimento de sensores de fibra ótica de plástico para monitorizar a postura corporal de um utilizador de uma cadeira de escritório. Simultaneamente, foi desenvolvido um software capaz de monitorizar e mostrar ao utilizador todos os dados adquiridos em tempo real e advertir o utilizador de posturas incorretas, bem como aconselhar para pausas no trabalho. Numa quarta fase, o estudo centrou-se no desenvolvimento de sensores altamente sensíveis embebidos em materiais impressos 3D. O sensor é composto por uma fibra ótica com uma FBG e o corpo do sensor por um material polimérico flexível, denominado “Flexible”. O sensor foi impresso numa impressora 3D e durante a sua impressão foi incorporada a fibra ótica. O sensor demonstrou ser altamente sensível e foi capaz de monitorizar frequência respiratória e cardíaca, tanto em soluções vestíveis (peito e pulso) como em soluções “invisíveis” (cadeira de escritório).Programa Doutoral em Engenharia Físic