706 research outputs found
Authentication and authorisation in entrusted unions
This paper reports on the status of a project whose aim is to implement and demonstrate in a real-life environment an integrated eAuthentication and eAuthorisation framework to enable trusted collaborations and delivery of services across different organisational/governmental jurisdictions. This aim will be achieved by designing a framework with assurance of claims, trust indicators, policy enforcement mechanisms and processing under encryption to address the security and confidentiality requirements of large distributed infrastructures. The framework supports collaborative secure distributed storage, secure data processing and management in both the cloud and offline scenarios and is intended to be deployed and tested in two pilot studies in two different domains, viz, Bio-security incident management and Ambient Assisted Living (eHealth). Interim results in terms of security requirements, privacy preserving authentication, and authorisation are reported
Enhanced Living Environments
This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area
NILM techniques for intelligent home energy management and ambient assisted living: a review
The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora:
Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve
01/SAICT/2018/39578
Fundação para a Ciência e Tecnologia through IDMEC, under LAETA:
SFRH/BSAB/142998/2018
SFRH/BSAB/142997/2018
UID/EMS/50022/2019
Junta de Comunidades de Castilla-La-Mancha, Spain:
SBPLY/17/180501/000392
Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project):
TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio
Progress in ambient assisted systems for independent living by the elderly
One of the challenges of the ageing population in many countries is the efficient delivery of health and care services, which is further complicated by the increase in neurological conditions among the elderly due to rising life expectancy. Personal care of the elderly is of concern to their relatives, in case they are alone in their homes and unforeseen circumstances occur, affecting their wellbeing. The alternative; i.e. care in nursing homes or hospitals is costly and increases further if specialized care is mobilized to patients’ place of residence. Enabling technologies for independent living by the elderly such as the ambient assisted living systems (AALS) are seen as essential to enhancing care in a cost-effective manner. In light of significant advances in telecommunication, computing and sensor miniaturization, as well as the ubiquity of mobile and connected devices embodying the concept of the Internet of Things (IoT), end-to-end solutions for ambient assisted living have become a reality. The premise of such applications is the continuous and most often real-time monitoring of the environment and occupant behavior using an event-driven intelligent system, thereby providing a facility for monitoring and assessment, and triggering assistance as and when needed. As a growing area of research, it is essential to investigate the approaches for developing AALS in literature to identify current practices and directions for future research. This paper is, therefore, aimed at a comprehensive and critical review of the frameworks and sensor systems used in various ambient assisted living systems, as well as their objectives and relationships with care and clinical systems. Findings from our work suggest that most frameworks focused on activity monitoring for assessing immediate risks while the opportunities for integrating environmental factors for analytics and decision-making, in particular for the long-term care were often overlooked. The potential for wearable devices and sensors, as well as distributed storage and access (e.g. cloud) are yet to be fully appreciated. There is a distinct lack of strong supporting clinical evidence from the implemented technologies. Socio-cultural aspects such as divergence among groups, acceptability and usability of AALS were also overlooked. Future systems need to look into the issues of privacy and cyber security
Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns
Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590This work was part of and supported by GoodBrother, COST Action 19121—Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living
Events of daily living classification on an ambient assisted living environment
Dissertação de mestrado em Engenharia Eletrónica Industrial e ComputadoresPopulation ageing is a global demographic challenge and countries all around the world are facing
significant pressure on their health and social care systems in order to mitigate the effects of it.
The emerging social aspect introduced some crucial challenges to society and greater demands
on the actual health care sector, which led to the emergence and increased integration of agefriendly
innovative welfare technological-based care services for safe and independent ageing, including
the assisted living technologies based on Ambient Intelligence (AmI) paradigm and Pervasive
HealthCare. The Ambient Assisted Living (AAL) systems intend to provide caregivers with a detailed
overview of their Events of Daily Living (EDL), which constitutes a clinical criteria to evaluate activity
limitations.
This dissertation addresses these challenges and contributes to the Ambient Assisted Living
research, by means of a holistic solution composed of a beyond the state-of-the-art AAL technologies,
representing a novel approach to assist in the investigation and on the modeling of a subset of Events
of Daily Living (EDL), for sustaining independent living and a continual naturalistic assessment of
health.
The investigation was focused on 1) developing a multisensorial pervasive Research Data Acquistion
(RDA) Platform with embedded Ambient Intelligence (AmI), 2) COTS to verify their validity
and reliability for healthcare applications.
The proposed solution has been thoroughly evaluated in the Ambient Assisted Living Laboratory
that showed its effectiveness classifying EDL through the application of the AAL paradigm in the real
world.O envelhecimento populacional é um desafio demográfico global e os países em todo o mundo estão
sob com enorme pressão nos seus sistemas de saúde a fim de mitigar os efeitos que poderão advir.
O aspecto social emergente introduziu alguns desafios cruciais para a sociedade e uma maior
sobrecarga no setor de saúde, o que levou ao surgimento e aumento da integração de serviços
inovadores de assistência social, de modo a que haja um envelhecimento seguro e independente,
incluindo as tecnologias de assistência à vida com base no paradigma de Ambient Intelligence (AmI)
e no Pervasive HealthCare, os sistemas de Ambient Assisted Living (AAL). Eles pretendem fornecer
aos profissionais de saúde uma visão detalhada de seu Events of Daily Living (EDL), que constitui
um critério clínico para avaliar as limitações da atividade.
Para enfrentar estes desafios, esta dissertação contribui para a pesquisa na área de Ambient
Assisted Living, por meio de uma solução holística composta por uma tecnologia além das tecnologias
state-of-the-art, representando uma nova abordagem para auxiliar na investigação e na
modelação de um subconjunto de Events of Daily Living (EDL), para sustentar uma vida independente
e uma avaliação naturalística contínua da saúde. A investigação foi focada em 1) desenvolver
uma plataforma multisensorial pervasiva Research Data Acquistion (RDA) com Ambient Intelligence
(AmI), 2) COTS para verificar a sua validade e fiabilidade para aplicações de assistência médica.
A solução proposta foi avaliada no Ambient Assisted Living Laboratory, que mostrou bastante
eficácia ao classificar EDL através da aplicação do paradigma AAL no mundo real
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
Designing a gamified social platform for people living with dementia and their live-in family caregivers
In the current paper, a social gamified platform for people living with dementia and their live-in family caregivers, integrating a broader diagnostic approach and interactive interventions is presented. The CAREGIVERSPRO-MMD (C-MMD) platform constitutes a support tool for the patient and the informal caregiver - also referred to as the dyad - that strengthens self-care, and builds community capacity and engagement at the point of care. The platform is implemented to improve social collaboration, adherence to treatment guidelines through gamification, recognition of progress indicators and measures to guide management of patients with dementia, and strategies and tools to improve treatment interventions and medication adherence. Moreover, particular attention was provided on guidelines, considerations and user requirements for the design of a User-Centered Design (UCD) platform. The design of the platform has been based on a deep understanding of users, tasks and contexts in order to improve platform usability, and provide adaptive and intuitive User Interfaces with high accessibility. In this paper, the architecture and services of the C-MMD platform are presented, and specifically the gamification aspects. © 2018 Association for Computing Machinery.Peer ReviewedPostprint (author's final draft
The European Union's Ambient and Assisted Living Joint Programme: An evaluation of its impact on population health and well-being
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