A Hybrid-Powered Wireless System for Multiple Biopotential Monitoring

Chronic diseases are the top cause of human death in the United States and worldwide. A huge amount of healthcare costs is spent on chronic diseases every year. The high medical cost on these chronic diseases facilitates the transformation from in-hospital to out-of-hospital healthcare. The out-of-hospital scenarios require comfortability and mobility along with quality healthcare. Wearable electronics for well-being management provide good solutions for out-of-hospital healthcare. Long-term health monitoring is a practical and effective way in healthcare to prevent and diagnose chronic diseases. Wearable devices for long-term biopotential monitoring are impressive trends for out-of-hospital health monitoring. The biopotential signals in long-term monitoring provide essential information for various human physiological conditions and are usually used for chronic diseases diagnosis. This study aims to develop a hybrid-powered wireless wearable system for long-term monitoring of multiple biopotentials. For the biopotential monitoring, the non-contact electrodes are deployed in the wireless wearable system to provide high-level comfortability and flexibility for daily use. For providing the hybrid power, an alternative mechanism to harvest human motion energy, triboelectric energy harvesting, has been applied along with the battery to supply energy for long-term monitoring. For power management, an SSHI rectifying strategy associated with triboelectric energy harvester design has been proposed to provide a new perspective on designing TEHs by considering their capacitance concurrently. Multiple biopotentials, including ECG, EMG, and EEG, have been monitored to validate the performance of the wireless wearable system. With the investigations and studies in this project, the wearable system for biopotential monitoring will be more practical and can be applied in the real-life scenarios to increase the economic benefits for the health-related wearable devices

Networking Architecture and Key Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Digital twin (DT), refers to a promising technique to digitally and accurately represent actual physical entities. One typical advantage of DT is that it can be used to not only virtually replicate a system's detailed operations but also analyze the current condition, predict future behaviour, and refine the control optimization. Although DT has been widely implemented in various fields, such as smart manufacturing and transportation, its conventional paradigm is limited to embody non-living entities, e.g., robots and vehicles. When adopted in human-centric systems, a novel concept, called human digital twin (HDT) has thus been proposed. Particularly, HDT allows in silico representation of individual human body with the ability to dynamically reflect molecular status, physiological status, emotional and psychological status, as well as lifestyle evolutions. These prompt the expected application of HDT in personalized healthcare (PH), which can facilitate remote monitoring, diagnosis, prescription, surgery and rehabilitation. However, despite the large potential, HDT faces substantial research challenges in different aspects, and becomes an increasingly popular topic recently. In this survey, with a specific focus on the networking architecture and key technologies for HDT in PH applications, we first discuss the differences between HDT and conventional DTs, followed by the universal framework and essential functions of HDT. We then analyze its design requirements and challenges in PH applications. After that, we provide an overview of the networking architecture of HDT, including data acquisition layer, data communication layer, computation layer, data management layer and data analysis and decision making layer. Besides reviewing the key technologies for implementing such networking architecture in detail, we conclude this survey by presenting future research directions of HDT

Energy-Aware System-Level Design of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are heterogeneous systems in which one or several computational cores interact with the physical environment. This interaction is typically performed through electromechanical elements such as sensors and actuators. Many CPSs operate as part of a network and some of them present a constrained energy budget (for example, they are battery powered). Examples of energy constrained CPSs could be a mobile robot, the nodes that compose a Body Area Network or a pacemaker. The heterogeneity present in the composition of CPSs together with the constrained energy availability makes these systems challenging to design. A way to tackle both complexity and costs is the application of abstract modelling and simulation. This thesis proposed the application of modelling at the system level, taking energy consumption in the different kinds of subsystems into consideration. By adopting this cross disciplinary approach to energy consumption it is possible to decrease it effectively. The results of this thesis are a number of modelling guidelines and tool improvements to support this kind of holistic analysis, covering energy consumption in electromechanical, computation and communication subsystems. From a methodological point of view these have been framed within a V-lifecycle. Finally, this approach has been demonstrated on two case studies from the medical domain enabling the exploration of alternative systems architectures and producing energy consumption estimates to conduct trade-off analysis

Wearables for Industrial Work Safety: A Survey

Today, ensuring work safety is considered to be one of the top priorities for various industries. Workplace injuries, illnesses, and deaths often entail substantial production and financial losses, governmental checks, series of dismissals, and loss of reputation. Wearable devices are one of the technologies that flourished with the fourth industrial revolution or Industry 4.0, allowing employers to monitor and maintain safety at workplaces. The purpose of this article is to systematize knowledge in the field of industrial wearables’ safety to assess the relevance of their use in enterprises as the technology maintaining occupational safety, to correlate the benefits and costs of their implementation, and, by identifying research gaps, to outline promising directions for future work in this area. We categorize industrial wearable functions into four classes (monitoring, supporting, training, and tracking) and provide a classification of the metrics collected by wearables to better understand the potential role of wearable technology in preserving workplace safety. Furthermore, we discuss key communication technologies and localization techniques utilized in wearable-based work safety solutions. Finally, we analyze the main challenges that need to be addressed to further enable and support the use of wearable devices for industrial work safety

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system

An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease

Internet of Things Architectures for Enhanced Living Environments

Ambient Assisted Living (AAL) is an emerging multidisciplinary research area that aims to create an ecosystem of different types of sensors, computers, mobile devices, wireless networks, and software applications for enhanced living environments and occupational health. There are several challenges in the development and implementation of an effective AAL system, such as system architecture, human-computer interaction, ergonomics, usability, and accessibility. There are also social and ethical challenges, such as acceptance by seniors and the privacy and confidentiality that must be a requirement of AAL devices. It is also essential to ensure that technology does not replace human care and is used as a relevant complement. The Internet of Things (IoT) is a paradigm where objects are connected to the Internet and support sensing capabilities. IoT devices should be ubiquitous, recognize the context, and support intelligence capabilities closely related to AAL. Technological advances allow defining new advanced tools and platforms for real-time health monitoring and decision making in the treatment of various diseases. IoT is a suitable approach to building healthcare systems, and it provides a suitable platform for ubiquitous health services, using, for example, portable sensors to carry data to servers and smartphones for communication. Despite the potential of the IoT paradigm and technologies for healthcare systems, several challenges to be overcome still exist. The direction and impact of IoT in the economy are not clearly defined, and there are barriers to the immediate and ubiquitous adoption of IoT products, services, and solutions. Several sources of pollutants have a high impact on indoor living environments. Consequently, indoor air quality is recognized as a fundamental variable to be controlled for enhanced health and well-being. It is critical to note that typically most people occupy more than 90% of their time inside buildings, and poor indoor air quality negatively affects performance and productivity. Research initiatives are required to address air quality issues to adopt legislation and real-time inspection mechanisms to improve public health, not only to monitor public places, schools, and hospitals but also to increase the rigor of building rules. Therefore, it is necessary to use real-time monitoring systems for correct analysis of indoor air quality to ensure a healthy environment in at least public spaces. In most cases, simple interventions provided by homeowners can produce substantial positive impacts on indoor air quality, such as avoiding indoor smoking and the correct use of natural ventilation. An indoor air quality monitoring system helps the detection and improvement of air quality conditions. Local and distributed assessment of chemical concentrations is significant for safety (e.g., detection of gas leaks and monitoring of pollutants) as well as to control heating, ventilation, and HVAC systems to improve energy efficiency. Real-time indoor air quality monitoring provides reliable data for the correct control of building automation systems and should be assumed as a decision support platform on planning interventions for enhanced living environments. However, the monitoring systems currently available are expensive and only allow the collection of random samples that are not provided with time information. Most solutions on the market only allow data consulting limited to device memory and require procedures for downloading and manipulating data with specific software. In this way, the development of innovative environmental monitoring systems based on ubiquitous technologies that allow real-time analysis becomes essential. This thesis resulted in the design and development of IoT architectures using modular and scalable structures for air quality monitoring based on data collected from cost-effective sensors for enhanced living environments. The proposed architectures address several concepts, including acquisition, processing, storage, analysis, and visualization of data. These systems incorporate an alert management Framework that notifies the user in real-time in poor indoor air quality scenarios. The software Framework supports multiple alert methods, such as push notifications, SMS, and e-mail. The real-time notification system offers several advantages when the goal is to achieve effective changes for enhanced living environments. On the one hand, notification messages promote behavioral changes. These alerts allow the building manager to identify air quality problems and plan interventions to avoid unhealthy air quality scenarios. The proposed architectures incorporate mobile computing technologies such as mobile applications that provide ubiquitous air quality data consulting methods s. Also, the data is stored and can be shared with medical teams to support the diagnosis. The state-of-the-art analysis has resulted in a review article on technologies, applications, challenges, opportunities, open-source IoT platforms, and operating systems. This review was significant to define the IoT-based Framework for indoor air quality supervision. The research leads to the development and design of cost-effective solutions based on open-source technologies that support Wi-Fi communication and incorporate several advantages such as modularity, scalability, and easy installation. The results obtained are auspicious, representing a significant contribution to enhanced living environments and occupational health. Particulate matter (PM) is a complex mixture of solid and liquid particles of organic and inorganic substances suspended in the air. Moreover, it is considered the pollutant that affects more people. The most damaging particles to health are ≤PM10 (diameter 10 microns or less), which can penetrate and lodge deep within the lungs, contributing to the risk of developing cardiovascular and respiratory diseases as well as lung cancer. Taking into account the adverse health effects of PM exposure, an IoT architecture for automatic PM monitoring was proposed. The proposed architecture is a PM real-time monitoring system and a decision-making tool. The solution consists of a hardware prototype for data acquisition and a Web Framework developed in .NET for data consulting. This system is based on open-source and technologies, with several advantages compared to existing systems, such as modularity, scalability, low-cost and easy installation. The data is stored in a database developed in SQL SERVER using .NET Web services. The results show the ability of the system to analyze the indoor air quality in real-time and the potential of the Web Framework for the planning of interventions to ensure safe, healthy, and comfortable conditions. Associations of high concentrations of carbon dioxide (CO2) with low productivity at work and increased health problems are well documented. There is also a clear correlation between high levels of CO2 and high concentrations of pollutants in indoor air. There are sufficient reasons to monitor CO2 and provide real-time notifications to improve occupational health and provide a safe and healthy indoor living environment. Taking into account the significant influence of CO2 for enhanced living environments, a real-time IoT architecture for CO2 monitoring was proposed. CO2 was selected because it is easy to measure and is produced in quantity (by people and combustion equipment). It can be used as an indicator of other pollutants and, therefore, of air quality in general. The solution consists of a hardware prototype for data acquisition environment, a Web software, and a smartphone application for data consulting. The proposed architecture is based on open-source technologies, and the data is stored in a SQL SERVER database. The mobile Framework allows the user not only to consult the latest data collected but also to receive real-time notifications in poor indoor air quality scenarios, and to configure the alerts threshold levels. The results show that the mobile application not only provides easy access to real-time air quality data, but also allows the user to maintain parameter history and provide a history of changes. Consequently, this system allows the user to analyze in a precise and detailed manner the behavior of air quality. Finally, an air quality monitoring solution was implemented, consisting of a hardware prototype that incorporates only the MICS-6814 sensor as the detection unit. This system monitors various air quality parameters such as NH3 (ammonia), CO (carbon monoxide), NO2 (nitrogen dioxide), C3H8 (propane), C4H10 (butane), CH4 (methane), H2 (hydrogen) and C2H5OH (ethanol). The monitoring of the concentrations of these pollutants is essential to provide enhanced living environments. This solution is based on Cloud, and the collected data is sent to the ThingSpeak platform. The proposed Framework combines sensitivity, flexibility, and measurement accuracy in real-time, allowing a significant evolution of current air quality controls. The results show that this system provides easy, intuitive, and fast access to air quality data as well as relevant notifications in poor air quality situations to provide real-time intervention and improve occupational health. These data can be accessed by physicians to support diagnoses and correlate the symptoms and health problems of patients with the environment in which they live. As future work, the results reported in this thesis can be considered as a starting point for the development of a secure system sharing data with health professionals in order to serve as decision support in diagnosis.Ambient Assisted Living (AAL) é uma área de investigação multidisciplinar emergente que visa a construção de um ecossistema de diferentes tipos de sensores, microcontroladores, dispositivos móveis, redes sem fios e aplicações de software para melhorar os ambientes de vida e a saúde ocupacional. Existem muitos desafios no desenvolvimento e na implementação de um sistema AAL, como a arquitetura do sistema, interação humano-computador, ergonomia, usabilidade e acessibilidade. Existem também problemas sociais e éticos, como a aceitação por parte dos utilizadores mais vulneráveis e a privacidade e confidencialidade, que devem ser uma exigência de todos os dispositivos AAL. De facto, também é essencial assegurar que a tecnologia não substitua o cuidado humano e seja usada como um complemento essencial. A Internet das Coisas (IoT) é um paradigma em que os objetos estão conectados à Internet e suportam recursos sensoriais. Tendencialmente, os dispositivos IoT devem ser omnipresentes, reconhecer o contexto e ativar os recursos de inteligência ambiente intimamente relacionados ao AAL. Os avanços tecnológicos permitem definir novas ferramentas avançadas e plataformas para monitorização de saúde em tempo real e tomada de decisão no tratamento de várias doenças. A IoT é uma abordagem adequada para construir sistemas de saúde sendo que oferece uma plataforma para serviços de saúde ubíquos, usando, por exemplo, sensores portáteis para recolha e transmissão de dados e smartphones para comunicação. Apesar do potencial do paradigma e tecnologias IoT para o desenvolvimento de sistemas de saúde, muitos desafios continuam ainda por ser resolvidos. A direção e o impacto das soluções IoT na economia não está claramente definido existindo, portanto, barreiras à adoção imediata de produtos, serviços e soluções de IoT. Os ambientes de vida são caracterizados por diversas fontes de poluentes. Consequentemente, a qualidade do ar interior é reconhecida como uma variável fundamental a ser controlada de forma a melhorar a saúde e o bem-estar. É importante referir que tipicamente a maioria das pessoas ocupam mais de 90% do seu tempo no interior de edifícios e que a má qualidade do ar interior afeta negativamente o desempenho e produtividade. É necessário que as equipas de investigação continuem a abordar os problemas de qualidade do ar visando a adoção de legislação e mecanismos de inspeção que atuem em tempo real para a melhoraria da saúde e qualidade de vida, tanto em locais públicos como escolas e hospitais e residências particulares de forma a aumentar o rigor das regras de construção de edifícios. Para tal, é necessário utilizar mecanismos de monitorização em tempo real de forma a possibilitar a análise correta da qualidade do ambiente interior para garantir ambientes de vida saudáveis. Na maioria dos casos, intervenções simples que podem ser executadas pelos proprietários ou ocupantes da residência podem produzir impactos positivos substanciais na qualidade do ar interior, como evitar fumar em ambientes fechados e o uso correto de ventilação natural. Um sistema de monitorização e avaliação da qualidade do ar interior ajuda na deteção e na melhoria das condições ambiente. A avaliação local e distribuída das concentrações químicas é significativa para a segurança (por exemplo, deteção de fugas de gás e supervisão dos poluentes) bem como para controlar o aquecimento, ventilação, e sistemas de ar condicionado (HVAC) visando a melhoria da eficiência energética. A monitorização em tempo real da qualidade do ar interior fornece dados fiáveis para o correto controlo de sistemas de automação de edifícios e deve ser assumida com uma plataforma de apoio à decisão no que se refere ao planeamento de intervenções para ambientes de vida melhorados. No entanto, os sistemas de monitorização atualmente disponíveis são de alto custo e apenas permitem a recolha de amostras aleatórias que não são providas de informação temporal. A maioria das soluções disponíveis no mercado permite apenas a acesso ao histórico de dados que é limitado à memória do dispositivo e exige procedimentos de download e manipulação de dados com software proprietário. Desta forma, o desenvolvimento de sistemas inovadores de monitorização ambiente baseados em tecnologias ubíquas e computação móvel que permitam a análise em tempo real torna-se essencial. A Tese resultou na definição e no desenvolvimento de arquiteturas para monitorização da qualidade do ar baseadas em IoT. Os métodos propostos são de baixo custo e recorrem a estruturas modulares e escaláveis para proporcionar ambientes de vida melhorados. As arquiteturas propostas abordam vários conceitos, incluindo aquisição, processamento, armazenamento, análise e visualização de dados. Os métodos propostos incorporam Frameworks de gestão de alertas que notificam o utilizador em tempo real e de forma ubíqua quando a qualidade do ar interior é deficiente. A estrutura de software suporta vários métodos de notificação, como notificações remotas para smartphone, SMS (Short Message Service) e email. O método usado para o envio de notificações em tempo real oferece várias vantagens quando o objetivo é alcançar mudanças efetivas para ambientes de vida melhorados. Por um lado, as mensagens de notificação promovem mudanças de comportamento. De facto, estes alertas permitem que o gestor do edifício e os ocupantes reconheçam padrões da qualidade do ar e permitem também um correto planeamento de intervenções de forma evitar situações em que a qualidade do ar é deficiente. Por outro lado, o sistema proposto incorpora tecnologias de computação móvel, como aplicações móveis, que fornecem acesso omnipresente aos dados de qualidade do ar e, consequentemente, fornecem soluções completas para análise de dados. Além disso, os dados são armazenados e podem ser partilhados com equipas médicas para ajudar no diagnóstico. A análise do estado da arte resultou na elaboração de um artigo de revisão sobre as tecnologias, aplicações, desafios, plataformas e sistemas operativos que envolvem a criação de arquiteturas IoT. Esta revisão foi um trabalho fundamental na definição das arquiteturas propostas baseado em IoT para a supervisão da qualidade do ar interior. Esta pesquisa conduz a um desenvolvimento de arquiteturas IoT de baixo custo com base em tecnologias de código aberto que operam como um sistema Wi-Fi e suportam várias vantagens, como modularidade, escalabilidade e facilidade de instalação. Os resultados obtidos são muito promissores, representando uma contribuição significativa para ambientes de vida melhorados e saúde ocupacional. O material particulado (PM) é uma mistura complexa de partículas sólidas e líquidas de substâncias orgânicas e inorgânicas suspensas no ar e é considerado o poluente que afeta mais pessoas. As partículas mais prejudiciais à saúde são as ≤PM10 (diâmetro de 10 micrómetros ou menos), que podem penetrar e fixarem-se dentro dos pulmões, contribuindo para o risco de desenvolver doenças cardiovasculares e respiratórias, bem como de cancro do pulmão. Tendo em consideração os efeitos negativos para a saúde da exposição ao PM foi desenvolvido numa primeira fase uma arquitetura IoT para monitorização automática dos níveis de PM. Esta arquitetura é um sistema que permite monitorização de PM em tempo real e uma ferramenta de apoio à tomada de decisão. A solução é composta por um protótipo de hardware para aquisição de dados e um portal Web desenvolvido em .NET para consulta de dados. Este sistema é baseado em tecnologias de código aberto com várias vantagens em comparação aos sistemas existentes, como modularidade, escalabilidade, baixo custo e fácil instalação. Os dados são armazenados numa base de dados desenvolvida em SQL SERVER e são enviados com recurso a serviços Web. Os resultados mostram a capacidade do sistema de analisar em tempo real a qualidade do ar interior e o potencial da Framework Web para o planeamento de intervenções com o objetivo de garantir condições seguras, saudáveis e confortáveis. Associações de altas concentrações de dióxido de carbono (CO2) com défice de produtividade no trabalho e aumento de problemas de saúde encontram-se bem documentadas. Existe também uma correlação evidente entre altos níveis de CO2 e altas concentrações de poluentes no ar interior. Tendo em conta a influência significativa do CO2 para a construção de ambientes de vida melhorados desenvolveu-se uma solução de monitorização em tempo real de CO2 com base na arquitetura de IoT. A arquitetura proposta permite também o envio de notificações em tempo real para melhorar a saúde ocupacional e proporcionar um ambiente de vida interior seguro e saudável. O CO2 foi selecionado, pois é fácil de medir e é produzido em quantidade (por pessoas e equipamentos de combustão). Assim, pode ser usado como um indicador de outros poluentes e, portanto, da qualidade do ar em geral. O método proposto é composto por um protótipo de hardware para aquisição de dados, um software Web e uma aplicação smartphone para consulta de dados. Esta arquitetura é baseada em tecnologias de código aberto e os dados recolhidos são armazenados numa base de dados SQL SERVER. A Framework móvel permite não só consultar em tempo real os últimos dados recolhidos, receber notificações com o objetivo de avisar o utilizador quando a qualidade do ar está deficiente, mas também para configurar alertas. Os resultados mostram que a Framework móvel fornece não apenas acesso fácil aos dados da qualidade do ar em tempo real, mas também permite ao utilizador manter o histórico de parâmetros. Assim este sistema permite ao utilizador analisar de maneira precisa e detalhada o comportamento da qualidade do ar interior. Por último, é proposta uma arquitetura para monitorização de vários parâmetros da qualidade do ar, como NH3 (amoníaco), CO (monóxido de carbono), NO2 (dióxido de azoto), C3H8 (propano), C4H10 (butano), CH4 (metano), H2 (hidrogénio) e C2H5OH (etanol). Esta arquitetura é composta por um protótipo de hardware que incorpora unicamente o sensor MICS-6814 como unidade de deteção. O controlo das concentrações destes poluentes é extremamente relevante para proporcionar ambientes de vida melhorados. Esta solução tem base na Cloud sendo que os dados recolhidos são enviados para a plataforma ThingSpeak. Esta Framework combina sensibilidade, flexibilidade e precisão de medição em tempo real, permitindo uma evolução significativa dos atuais sistemas de monitorização da qualidade do ar. Os resultados mostram que este sistema fornece acesso fácil, intuitivo e rápido aos dados de qualidade do ar bem como notificações essenciais em situações de qualidade do ar deficiente de forma a planear intervenções em tempo útil e melhorar a saúde ocupacional. Esses dados podem ser acedidos pelos médicos para apoiar diagnósticos e correlacionar os sintomas e problemas de saúde dos pacientes com o ambiente em que estes vivem. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto de partida para o desenvolvimento de um sistema seguro para partilha de dados com profissionais de saúde de forma a servir de suporte à decisão no diagnóstico

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

Wearable Wireless Devices

No abstract available

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing