Energy consumption in buildings depends on the local climate, building characteristics, and user behavior. Focusing on user interaction, this research work developed a novel approach to monitoring and interaction with local users by providing in situ context information through graphic descriptions of energy consumption and indoor/outdoor environment parameters: temperature, luminosity, and humidity, which are routinely measured in real-time and stored to identify consumption patterns and other savings actions. To involve local users, collected data are represented in 3D color representation using building 3d models. A simplified color scale depicts environmental comfort (low/comfortable/high temperature/relative humidity) and energy consumption (above/below usual patterns). We found that these indices induced user commitment and increased their engagement and participation in saving actions like turning off lights and better management of air conditioning systems.
O projeto SECClasS -Sustainability Enhanced Construction Classification Systemfinanciado pelo EEA Grants -pretende facilitar a Economia Circular na Construção introduzindo um Sistema de Classificação de Informações sobre Construção otimizado para a Sustentabilidade. Este sistema será orientado para a metodologia BIM e servirá não só a componente de sustentabilidade, mas também os restantes usos BIM, como a gestão do processo BIM, extração de quantidades, compatibilização de especialidades ou planeamento de obra, e todas as fases do ciclo de vida.
First and foremost, I would like to thank my parents, my sister, and the rest of my family for always believing in me and telling me that I would be able to finish the thesis, even when I thought I couldn't.I'd also like to thank all of my college friends, my "Octajericos," who always tried to help me and believed in me and my abilities, as well as "Casa Carlos," who represent my home friends and colleagues who always supported me and put up with all of my stresses every day at home, making me laugh when I was down.Finally, I'd like to thank my supervisors, Carlos Coutinho and Daniel Calé, for making their time available to me, offering thoughts and suggestions, assisting me in organizing ideas, and being able to turn and describe all of my practical work into writing, allowing me to complete this thesis.Thank you to everyone I mentioned for their encouragement and trust; without them, it would have been very difficult for me to complete this chapter of my life. i Resumo É possível observar como as tecnologias de comunicação e informação avançaram a um ritmo bastante acelerado nos dias de hoje. A introdução e aparecimento da tecnologia "wearable" representa um aspeto que contribui para este progresso e tem o potencial de ser uma solução inovadora para os desafios dos cuidados de saúde, uma vez que pode ser utilizada para a prevenção e manutenção de doenças, tais como a monitorização física, bem como para a gestão de pacientes.Para abordar alguns dos desafios dos cuidados de saúde, esta tese de investigação propõe uma metodologia de investigação, questões de investigação, e hipóteses para o desenvolvimento de um sistema inteligente de monitorização da saúde com alertas e monitorização contínua utilizando wearable devices capazes de recolher dados biométricos de seres humanos.O conceito foi então provado pelo desenvolvimento de um protótipo utilizando wearable devices conectados a um microcontrolador, que transmite os seus dados através do Protocolo MQTT a um painel de instrumentos alimentado por o Node-RED que lida com a monitorização de métricas de saúde e onde toda a monitorização executada, e os alarmes gerados, podem ser visualizados em tempo real e depois entregues numa base de dados MongoDB para posterior análise e visualização.Para demonstrar a eficácia deste protótipo, este foi implementado no mundo real onde foram adquiridos vários resultados através da utilização de dois utilizadores distintos. Os resultados foram bastante favoráveis e conclusivos, demonstrando que o protótipo criado foi satisfatório no fornecimento de dados para apoiar as hipóteses e questões de investigação desenvolvidas.
Health Remote Monitoring Systems (HRMS) offer the ability to address health-care human resource concerns. In developing nations, where pervasive mobile networks and device access are linking people like never before, HRMS are of special relevance. A fundamental aim of this research work is the realization of technological-based solution to triage and follow-up people living with dementias so as to reduce pressure on busy staff while doing this from home so as to avoid all unnecessary visits to hospital facilities, increasingly perceived as dangerous due to COVID-19 but also raising nosocomial infections, raising alerts for abnormal values. Sensing approaches are complemented by advanced predictive models based on Machine Learning (ML) and Artificial Intelligence (AI), thus being able to explore novel ways of demonstrating patient-centered predictive measures. Low-cost IoT devices composing a network of sensors and actuators aggregated to create a digital experience that will be used and exposure to people to simultaneously conduct several tests and obtain health data that can allow screening of early onset dementia and to aid in the follow-up of selected cases. The best ML for predicting AD was logistic regression with an accuracy of 86.9%. This application as demonstrated to be essential for caregivers once they can monitor multiple patients in real-time and actuate when abnormal values occur.
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