Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Geotechnical Engineering for the Preservation of Monuments and Historic Sites III

The conservation of monuments and historic sites is one of the most challenging problems facing modern civilization. It involves, in inextricable patterns, factors belonging to different fields (cultural, humanistic, social, technical, economical, administrative) and the requirements of safety and use appear to be (or often are) in conflict with the respect of the integrity of the monuments. The complexity of the topic is such that a shared framework of reference is still lacking among art historians, architects, structural and geotechnical engineers. The complexity of the subject is such that a shared frame of reference is still lacking among art historians, architects, architectural and geotechnical engineers. And while there are exemplary cases of an integral approach to each building element with its static and architectural function, as a material witness to the culture and construction techniques of the original historical period, there are still examples of uncritical reliance on modern technology leading to the substitution from earlier structures to new ones, preserving only the iconic look of the original monument. Geotechnical Engineering for the Preservation of Monuments and Historic Sites III collects the contributions to the eponymous 3rd International ISSMGE TC301 Symposium (Naples, Italy, 22-24 June 2022). The papers cover a wide range of topics, which include: 　 - Principles of conservation, maintenance strategies, case histories - The knowledge: investigations and monitoring - Seismic risk, site effects, soil structure interaction - Effects of urban development and tunnelling on built heritage - Preservation of diffuse heritage: soil instability, subsidence, environmental damages The present volume aims at geotechnical engineers and academics involved in the preservation of monuments and historic sites worldwide

inContexto: framework to obtain people context using wearable sensors and social network sites

Mención Internacional en el título de doctorAmbient Intelligent (AmI) technology is developing fast and will promote a new generation of applications with some characteristics in the area of context awareness, anticipatory behavior, home security, monitoring, Health Care and video surveillance. AmI Environments should be surrounded by multiples sensors in order to discover people needs. These kind of scenarios are characterized by intelligent environments, which are able to recognize inconspicuously the presence of individuals and react to their needs. In such systems, people are conceived as the main actor, always in control, playing multiple roles, and this is perhaps the new real facet of research related to AmI: it introduces a new dimension creating synergies between the user and the environment. The AmI paradigm sets the principles to design pervasive and transparent infrastructures being capable of observing people without prying into their lives, and also adapting to their needs. There are several basis concepts to consider for retrieving people context, however the most important for users is that sensors devices must be unobtrusive. Many technologies are conceived as hand-held or wearable, taking advantage of the intelligence embedded in the environment. Mobile technologies and Social Network Sites make it possible to collect people information anywhere at anytime, and provide users with up-to-date information ready for decision-making processes. Nevertheless, the management of these sensors for collecting user context poses several challenges. Besides the limited computational capabilities of mobile devices, mobile systems face specific problems that cannot be solved by traditional knowledge management methodologies and tools, and thus require creative new solutions. This dissertation proposes a set of techniques, interfaces and algorithms for the implementation of inferring context information from new kind of sensors (Smartphones and Social Networking). The huge potential of both new sensors have motivated us to design a framework that can intelligently capture different sensory data in real-time. Smartphones may obtain and process physical phenomena from embedded sensors (Accelerometer, gyroscope, compass, magnetometer, proximity sensor, light sensor, GPS, etc.) and SNS the affective ones. Subsequently this information could be transmitted to remote locations without any human intervention. The mechanisms proposed here are based on the implementation of a basic framework that modifies information from the raw data to the most descriptive action. To this end, the development of this thesis starts from a inContexto framework which exploits off-the-shelf sensor-enabled mobile phones and SNS people presence to automatically infer people’s context. The main goals of our architecture are: (i) Collection, storage, analyse, and sharing of the user context information, (ii) Plug-and-play support for a wide variety of sensing devices, (iii) Privacy preservation of individuals sharing their data, and (iv) Easy application development. Furthermore our inContexto has been implemented to allow third party application to participate and improve people context.La Inteligencia Ambiental (AmI) está sufriendo una evolución rápida y en un futuro cercano saldrán a la luz una nueva generación de aplicaciones en el área de los sistemas basados en contexto, seguridad en el hogar, monitorización, salud y video vigilancia. Los entornos AmI se caracterizan por estar plagados de sensores los cuales, están encargados de capturar información de la gente que hay en ellos para describir sus necesidades. Este tipo de escenarios se caracterizan por ser entornos inteligentes, capaces de reconocer autónomamente la presencia de personas y reaccionar a sus necesidades. En dichos sistemas, las personas o usuarios se conciben como el actor principal, siempre en control, jugando múltiples roles, y esto es una nueva característica dentro del marco de la investigación relacionada con AmI: introducir nuevas sinergias entre el usuario y el entorno que le rodea. El paradigma AmI establece los principios para el diseño de arquitecturas generales que son capaces de capturar información relevante de las personas sin entrometerse en su vida, y además adaptar dicha información a las necesidades del mismo. Existen diferentes conceptos a tener en cuenta para la captura del contexto de las personas, sin embargo, el factor más importante es que los dispositivos usados deben ser transparentes para el usuario, es decir que trabajen de manera autónoma y sin la ayuda del mismo. Los nuevos teléfonos móviles inteligentes o smarpthone y las redes sociales permiten extraer información de las personas en cualquier lugar en cualquier momento, y así poder proporcionar a los usuarios ayuda para la toma de decisiones en las actividades de su vida real. Sin embargo, la gestión de la información de estos sensores, los cuales nos permiten inferir el contexto, plantean varios desafíos a resolver En primer lugar la limitación de las capacidades tanto computacionales como de disponibilidad (consumo de energía) de los dispositivos móviles, los sistemas móviles se enfrentan a problemas específicos que no pueden ser resueltos por las metodologías y herramientas de gestión del conocimiento tradicional, y por lo tanto requieren de nuevas soluciones creativas. En esta tesis se propone un conjunto de técnicas, interfaces y algoritmos para inferir la información de contexto de las personas a través de nuevos sensores, los cuales han sido infrautilizados hasta el momento como son los smartphone y Redes Sociales. Gracias al enorme potencial de estos nuevos sensores nos ha motivado para diseñar un framework que de manera transparente al usuario puede capturar diferentes datos sensoriales en tiempo real. A través de los Smartphone se puede obtener y procesar los fenómenos físicos (Correr, Andar, etc.) de las personas, utilizando los sensores embebidos como el acelerómetro, giroscopio, brújula, magnetómetro, sensor de proximidad, sensor de luz, GPS, etc. Además a través de las redes sociales se podría obtener información de los fenómenos afectivos del usuario. Posteriormente, esta información se transmitirá para su procesamiento y búsqueda de nuevas inferencias sin la colaboración del usuario, de manera transparente. Los mecanismos propuestos en esta tesis se basan en la aplicación de un framework, inContexto, que recoge la información de los sensores (Señales, palabras, etc.) para posteriormente generar una acción más descriptiva y entendible por el usuario. Los principales objetivos que presenta inContexto son: (i) Recogida, almacenamiento, análisis e intercambio de la información de contexto de usuario, (ii) el apoyo Plug-and-play para una amplia variedad de dispositivos, (iii) la preservación de privacidad de los las personas, y (iv) el desarrollo de nuevas aplicaciones fácilmente, permitiendo a través de inContexto el acceso a los datos a aplicaciones de terceros para mejorar la información recogida.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Juan Pavón Mestras.- Secretario: Miguel Ángel Patricio Guisado.- Vocal: Nayat Sánchez P