Stress detection using machine learning and deep learning

Stress is a normal phenomenon in today's world, and it causes people to respond to a variety of factors, resulting in physiological and behavioural changes. If we keep stress in our minds for too long, it will have an effect on our bodies. Many health conditions associated with stress can be avoided if stress is detected sooner. When a person is stressed, a pattern can be detected using various bio-signals such as thermal, electrical, impedance, acoustic, optical, and so on, and stress levels can be identified using these bio-signals. This paper uses a dataset that was obtained using an Internet of Things (IOT) sensor, which led to the collection of information about a real-life situation involving a person's mental health. To obtain a pattern for stress detection, data from sensors such as the Galvanic Skin Response Sensor (GSR) and the Electrocardiogram (ECG) were collected. The dataset will then be categorised using Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Deep Learning algorithms (DL). Accuracy, precision, recall, and F1-Score are used to assess the data's performance. Finally, Decision Tree (DT) had the best performance where DT have accuracy 95%, precision 96%, recall 96% and F1-score 96% among all machine learning classifiers

Overview of Biosignal Analysis Methods for the Assessment of Stress

Objectives: Stress is a normal reaction of the human organism induced in situations that demand a level of activation. This reaction has both positive and negative impact on the life of each individual. Thus, the problem of stress management is vital for the maintenance of a person’s psychological balance. This paper aims at the brief presentation   of stress definition and various factors that can lead to augmented stress levels. Moreover, a brief synopsis of biosignals that are used for the detection and categorization of stress and their analysis is presented. Methods: Several studies, articles and reviews were included after literature research. The main questions of the research were: the most important and widely used physiological signals for stress detection/assessment, the analysis methods for their manipulation and the implementation of signal analysis for stress detection/assessment in various developed systems.  Findings: The main conclusion is that current researching approaches lead to more sophisticated methods of analysis and more accurate systems of stress detection and assessment. However, the lack of a concrete framework towards stress detection and assessment remains a great challenge for the research community. Doi: 10.28991/esj-2021-01267 Full Text: PD

Blockchain based End-to-end Tracking System for Distributed IoT Intelligence Application Security Enhancement

IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model in turn provides useful analysis results that can improve the operation of IoT systems. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide an end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-toend integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services

IoT in medical diagnosis: detecting excretory functional disorders for Older adults via bathroom activity change using unobtrusive IoT technology

The Internet of Things (IoT) and Artificial Intelligence (AI) are promising technologies that can help make the health system more efficient, which concurrently can be particularly useful to help maintain a high quality of life for older adults, especially in light of healthcare staff shortage. Many health issues are challenging to manage both by healthcare staff and policymakers. They have a negative impact on older adults and their families and are an economic burden to societies around the world. This situation is particularly critical for older adults, a population highly vulnerable to diseases that needs more consideration and care. It is, therefore, crucial to improve diagnostic and management as well as proposed prevention strategies to enhance the health and quality of life of older adults. In this study, we focus on detecting symptoms in early stages of diseases to prevent the deterioration of older adults' health and avoid complications. We focus on digestive and urinary system disorders [mainly the Urinary Tract Infection (UTI) and the Irritable Bowel Syndrome (IBS)] that are known to affect older adult populations and that are detrimental to their health and quality of life. Our proposed approach relies on unobtrusive IoT and change point detections algorithms to help follow older adults' health status daily. The approach monitors long-term behavior changes and detects possible changes in older adults' behavior suggesting early onsets or symptoms of IBS and UTI. We validated our approach with medical staff reports and IoT data collected in the residence of 16 different older adults during periods ranging from several months to a few years. Results are showing that our proposed approach can detect changes associated to symptoms of UTI and IBS, which were confirmed with observations and testimonies from the medical staff

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

Machine Learning Models for Mental Stress Classification based on Multimodal Biosignal Input

Mental stress is a largely prevalent condition directly or indirectly responsible for almost half of all work-related diseases. Work-Related Stress is the second most impactful occupational health problem in Europe, behind musculoskeletal diseases. When mental health is adequately handled, a worker’s well-being, performance, and productivity can be considerably improved. This thesis presents machine learning models to classify mental stress experienced by computer users using physiological signals including heart rate, acquired using a smart- watch; respiration, derived from a smartphone’s acc placed on the chest; and trapezius electromyography, using proprietary electromyography sensors. Two interactive proto- cols were implemented to collect data from 12 individuals. Time and frequency domain features were extracted from the heart rate and electromyography signals, and statistical and temporal features were extracted from the derived respiration signal. Three algorithms: Support Vector Machine, Random Forest, and K-Nearest-Neighbor were employed for mental stress classification. Different input modalities were tested for the machine learning models: one for each physiological signal and a multimodal one, combining all of them. Random Forest obtained the best mean accuracy (98.5%) for the respiration model whereas K-Nearest-Neighbor attained higher mean accuracies for the heart rate (89.0%) left, right and total electromyography (98.9%, 99.2%, and 99.3%) models. KNN algorithm was also able to achieve 100% mean accuracy for the multimodal model. A possible future approach would be to validate these models in real-time.O stress mental é uma condição amplamente prevalente direta ou indiretamente responsável por quase metade de todas doenças relacionadas com trabalho. O stress expe- rienciado no trabalho é o segundo problema de saúde ocupacional com maior impacto na Europa, depois das doenças músculo-esqueléticas. Quando a saúde mental é adequada- mente cuidada, o bem-estar, o desempenho e a produtividade de um trabalhador podem ser consideravelmente melhorados. Esta tese apresenta modelos de aprendizagem automática que classificam o stress mental experienciado por utilizadores de computadores recorrendo a sinais fisiológi- cos, incluindo a frequência cardíaca, adquirida pelo sensor de fotopletismografia de um smartwatch; a respiração, derivada de um acelerómetro incorporado no smartphone po- sicionado no peito; e electromiografia de cada um dos músculos trapézios, utilizando sensores electromiográficos proprietários. Foram implementados dois protocolos inte- ractivos para recolha de dados de 12 indivíduos. Características do domínio temporal e de frequência foram extraídas dos sinais de frequência cardíaca e electromiografia, e características estatísticas e temporais foram extraídas do sinal respiratório. Três algoritmos entitulados K-Nearest-Neighbor, Random Forest, e Support Vector Machine foram utilizados para a classificação do stress mental. Foram testadas diferentes modalidades de dados para os modelos de aprendizagem automática: uma para cada sinal fisiológico e uma multimodal, combinando os três. O Random Forest obteve a melhor precisão média (98,5%) para o modelo de respiração enquanto que o K-Nearest-Neighbor atingiu uma maior precisão média nos modelos de frequência cardíaca (89,0%) e electro- miografia esquerda, direita e total (98,9%, 99,2%, e 99,3%). O algoritmo KNN conseguiu ainda atingir uma precisão média de 100% para o modelo multimodal. Uma possível abordagem futura seria efetuar uma validação destes modelos em tempo real

Analysis and design of individual information systems to support health behavior change

As a wide-ranging socio-technical transformation, the digitalization has significantly influenced the world, bringing opportunities and challenges to our lives. Despite numerous benefits like the possibility to stay connected with people around the world, the increasing dispersion and use of digital technologies and media (DTM) pose risks to individuals’ well-being and health. Rising demands emerging from the digital world have been linked to digital stress, that is, stress directly or indirectly resulting from DTM (Ayyagari et al. 2011; Ragu-Nathan et al. 2008; Tarafdar et al. 2019; Weil and Rosen 1997), potentially intensifying individuals’ overall exposure to stress. Individuals experiencing this adverse consequence of digitalization are at elevated risk of developing severe mental health impairments (Alhassan et al. 2018; Haidt and Allen 2020; Scott et al. 2017), which is why various scholars emphasize that research should place a stronger focus on analyzing and shaping the role of the individual in a digital world, pursuing instrumental as well as humanistic objectives (Ameen et al. 2021; Baskerville 2011b). Information Systems (IS) research has long placed emphasis on the use of information and communication technology (ICT) in organizations, viewing an information system as the socio-technical system that emerges from individuals’ interaction with DTM in organizations. However, socio-technical information systems, as the essence of the IS discipline (Lee 2004; Sarker et al. 2019), are also present in different social contexts from private life. Acknowledging the increasing private use of DTM, such as smartphones and social networks, IS scholars have recently intensified their efforts to understand the human factor of IS (Avison and Fitzgerald 1991; Turel et al. 2021). A framework recently proposed by Matt et al. (2019) suggests three research angles: analyzing individuals’ behavior associated with their DTM use, analyzing what consequences arise from their DTM use behavior, and designing new technologies that promote positive or mitigate negative effects of individuals’ DTM use. Various recent studies suggest that individuals’ behavior seems to be an important lever influencing the outcomes of their DTM use (Salo et al. 2017; Salo et al. 2020; Weinstein et al. 2016). Therefore, this dissertation aims to contribute to IS research targeting the facilitation of a healthy DTM use behavior. It explores the use behavior, consequences, and design of DTM for individuals' use with the objective to deliver humanistic value by increasing individuals' health through supporting a behavior change related to their DTM use. The dissertation combines behavioral science and design science perspectives and applies pluralistic methodological approaches from qualitative (e.g., interviews, prototyping) and quantitative research (e.g., survey research, field studies), including mixed-methods approaches mixing both. Following the framework from Matt et al. (2019), the dissertation takes three perspectives therein: analyzing individuals’ behavior, analyzing individuals’ responses to consequences of DTM use, and designing information systems assisting DTM users. First, the dissertation presents new descriptive knowledge on individuals’ behavior related to their use of DTM. Specifically, it investigates how individuals behave when interacting with DTM, why they behave the way they do, and how their behavior can be influenced. Today, a variety of digital workplace technologies offer employees different ways of pursuing their goals or performing their tasks (Köffer 2015). As a result, individuals exhibit different behaviors when interacting with these technologies. The dissertation analyzes what interactional roles DTM users can take at the digital workplace and what may influence their behavior. It uses a mixed-methods approach and combines a quantitative study building on trace data from a popular digital workplace suite and qualitative interviews with users of this digital workplace suite. The empirical analysis yields eight user roles that advance the understanding of users’ behavior at the digital workplace and first insights into what factors may influence this behavior. A second study adds another perspective and investigates how habitual behavior can be changed by means of DTM design elements. Real-time feedback has been discussed as a promising way to do so (Schibuola et al. 2016; Weinmann et al. 2016). In a field experiment, employees working at the digital workplace are provided with an external display that presents real-time feedback on their office’s indoor environmental quality. The experiment examines if and to what extent the feedback influences their ventilation behavior to understand the effect of feedback as a means of influencing individuals’ behavior. The results suggest that real-time feedback can effectively alter individuals’ behavior, yet the feedback’s effectiveness reduces over time, possibly as a result of habituation to the feedback. Second, the dissertation presents new descriptive and prescriptive knowledge on individuals’ ways to mitigate adverse consequences arising from the digitalization of individuals. A frequently discussed consequence that digitalization has on individuals is digital stress. Although research efforts strive to determine what measures individuals can take to effectively cope with digital stress (Salo et al. 2017; Salo et al. 2020; Weinert 2018), further understanding of individuals’ coping behavior is needed (Weinert 2018). A group at high risk of suffering from the adverse effects of digital stress is adolescents because they grow up using DTM daily and are still developing their identity, acquiring mental strength, and adopting essential social skills. To facilitate a healthy DTM use, the dissertation explores what strategies adolescents use to cope with the demands of their DTM use. Combining a qualitative and a quantitative study, it presents 30 coping responses used by adolescents, develops five factors underlying adolescents’ activation of coping responses, and identifies gender- and age-related differences in their coping behavior. Third, the dissertation presents new prescriptive knowledge on the design of individual information systems supporting individuals in understanding and mitigating their perceived stress. Facilitated by the sensing capabilities of modern mobile devices, it explores the design and development of mobile systems that assess stress and support individuals in coping with stress by initiating a change of stress-related behavior. Since there is currently limited understanding of how to develop such systems, this dissertation explores various facets of their design and development. As a first step, it presents the development of a prototype aiming for life-integrated stress assessment, that is, the mobile sensor-based assessment of an individual’s stress without interfering with their daily routines. Data collected with the prototype yields a stress model relating sensor data to individuals’ perception of stress. To deliver a more generalized perspective on mobile stress assessment, the dissertation further presents a literature- and experience-based design theory comprising a design blueprint, design requirements, design principles, design features, and a discussion of potentially required trade-offs. Mobile stress assessment may be used for the development of mobile coping assistants. Aiming to assist individuals in effectively coping with stress and preventing future stress, a mobile coping assistant should recommend adequate coping strategies to the stressed individual in real-time or execute targeted actions within a defined scope of action automatically. While the implementation of a mobile coping assistant is yet up to future research, the dissertation presents an abstract design and algorithm for selecting appropriate coping strategies. To sum up, this dissertation contributes new knowledge on the digitalization of individuals to the IS knowledge bases, expanding both descriptive and prescriptive knowledge. Through the combination of diverse methodological approaches, it delivers knowledge on individuals’ behavior when using DTM, on the mitigation of consequences that may arise from individuals’ use of DTM, and on the design of individual information systems with the goal of facilitating a behavior change, specifically, regarding individuals’ coping with stress. Overall, the research contained in this dissertation may promote the development of digital assistants that support individuals’ in adopting a healthy DTM use behavior and thereby contribute to shaping a socio-technical environment that creates more benefit than harm for all individuals

Socio-technical analysis and design of digital workplaces to foster employee health

Recent socio-technical developments caused by ongoing digitalization (e.g., robotic process automation, artificial intelligence, anthropomorphic systems) or the COVID-19 pandemic (e.g., an increasing number of remote working employees and hence, increasing number of virtual collaboration) change the work environment and culture. Digital and smart workplace technol-ogies facilitate business processes and provide tools for efficient communication and (virtual) collaboration, “increasing the productivity of the workforce in the information age” (Attaran et al. 2019, p. 1). Especially in times of the COVID-19 pandemic, digital technologies play a crucial role in keeping us socially close, connected, and collaborative while increasing the phys-ical distance between humans. However, this development affects the health of employees (Tarafdar et al. 2013). In research, for example, it has long been known that the increased usage of digital technologies and media (DTM) may cause stress, leading to potentially harmful reac-tions in individuals. Research has noted this specific form of stress as technostress (Ayyagari et al. 2011; Tarafdar et al. 2007; Tarafdar et al. 2011; Tarafdar et al. 2019), which is an umbrella term for causes, negative organizational outcomes, and negative humanistic outcomes resulting from the use of DTM at work. The simultaneous consideration of humanistic (e.g., well-being, equality) and organizational outcomes (e.g., efficiency, productivity) is an integral part of a socio-technical system (Beath et al. 2013; Mumford 2006), which is at the core of the IS discipline (Bostrom et al. 2009; Chiasson and Davidson 2005). However, a review from Sarker et al. (2019) regarding published research articles in one of the top journals within the IS community revealed that most reviewed studies (91%) had focused exclusively on instrumental goals. They conclude that “many IS researchers have forgotten or ignored the premise that technologies need to benefit humankind overall (Majchrzak et al. 2016), not just their economic condition” (Sarker et al. 2019, p. 705). Especially as humanistic outcomes can lead to even more positive instrumental outcomes. Hence, Sarker et al. (2019) call for focusing on the connection between humanistic and instru-mental outcomes, enabling a positive synergy resulting from this interplay. For this reason, this dissertation adopts a socio-technical perspective. It aims to conduct re-search that links instrumental outcomes with humanistic objectives to ultimately achieve a healthier use of DTMs at the digital workplace. It is important to note that the socio-technical perspective considers both the technical component and the social component privileging nei-ther one of them and sees outcomes resulting from the reciprocal interaction between those two.Therefore, the dissertation focuses on the interaction while applying pluralistic methodological approaches from qualitative (e.g., semi-structured interviews, focus group discussions) and quantitative research (e.g., collection from a field study or survey research). It provides a theo-retical contribution applying both behavioral research (i.e., analysis of cause-and-effect rela-tionships) and design-oriented research (i.e., instructions for designing socio-technical information systems). Overall, this work addresses four different areas within the reciprocal interaction between the social and technical components: the role of the technical component, the role of the social component, DTMs fostering a fit between the technical and social compo-nents, and the imminent misfit between these two due to ongoing digitalization. First, to contribute to an understanding of the technical component’s role, this thesis presents new knowledge on the characteristics and features of DTM and their influence on employee health and productivity. Research on the design of digital workplaces examined different design approaches, in which information exchange and sharing documents or project support were regarded (Williams and Schubert 2018). However, the characteristics of DTM also play an es-sential role in the emergence of technostress (Dardas and Ahmad 2015). This thesis presents ten characteristics of DTM that affect technostress at an individual’s workplace, including a measurement scale and analysis on how these characteristics affect technostress. Besides, also, the provision of functional features by DTMs can affect instrumental outcomes or humanistic objectives. For example, affording users with certain kinds of autonomy regarding the config-uration of DTM while they work towards their goals could have a tremendous effect on pursu-ing goals and well-being (Patall et al. 2008; Ryan and Deci 2000). Therefore, this thesis presents knowledge regarding the design of DTM on the benefits of affording users with autonomy. Furthermore, it shows that merely affording more autonomy can have positive effects above and beyond the positive effects of the actualization of affordance. Second, to contribute to an understanding of the social component’s role, this thesis presents new knowledge on contextual and individual factors of social circumstances and their influence on employee health and productivity. In this context, the influence of the COVID-19 pandemic on the intensity of technostress among employees is considered, as work became more digital almost overnight. Therefore, this thesis provides empirical insights into digital work and its context in times of the COVID-19 pandemic and its effect on employees’ well-being, health, and productivity. Furthermore, measures to steer the identified effects if the situation in the course of the COVID-19 pandemic persists or comparable disruptive situations should re-occur are discussed. On the other hand, this research takes a closer look at the effect of an individual preference regarding coping styles in dealing with upcoming technostress. A distinction is made between the effects of two different coping styles, namely active-functional and dysfunctional, on strain as a humanistic outcome and productivity as an instrumental outcome. In the course of this, evidence is provided that coping moderates the relationship between the misfit within the socio-technical system and strain as proposed by the psychological theory of job demands-resources model (Demerouti et al. 2001). Third, to contribute to a successful fit between the technical and social components, this thesis presents frameworks and guidelines on the design of DTM, which understand the social com-ponent (here the user and her/his environment) and adjust accordingly to the needs of their users. Therefore, the thesis provides knowledge on the design of DTMs that support users in applying stress management techniques and build the foundation for stress-sensitive systems (i.e., systems that aim to mitigate stress by applying intervention measures on the social and technical component (Adam et al. 2017)). As a matter of fact, a framework for collecting and storing data (e.g., on the user and her/his environment) is developed and experiences with im-plementing a prototype for life-integrated stress assessment are reported. The experiences from this and the existing knowledge in the literature will finally be aggregated to a mid-range design theory for mobile stress assessment. To contribute to the fourth and last aspect, the imminent misfit within the socio-technical sys-tem due to ongoing digitalization, this thesis presents new knowledge regarding digital work demands that potentially affect both employees’ health and instrumental outcomes. The current version of technostress’s theoretical foundation was introduced more than ten years ago by Tarafdar et al. (2007). However, the interaction with and use of DTM has considerably changed along with the societal and individual expectations. Therefore, this thesis puts the current con-cept of technostress to test. As a result, a new theory of digital stress, as an extension of the concept of technostress, is proposed with twelve dimensions – instead of five dimensions within the concept of Tarafdar et al. (2007) – that could be hierarchically structured in four higher-order factors. This theory holistically addresses the current challenges that employees have to deal with digitalization. To sum up, this dissertation contributes to the IS community’s knowledge base by providing knowledge regarding the interaction between employees and their digital workplace to foster the achievement of humanistic and instrumental outcomes. It provides both behavioral research and design-oriented research while using pluralistic methodological approaches. For this pur-pose, this thesis presents knowledge about the different components within the socio-technical system, design knowledge on DTMs fostering the fit between these components, and an under-standing of an upcoming misfit due to the ongoing digitalization. Overall, this research aims to support the successful change towards a healthy digital workplace in the face of digitalization

Solução ciber-física para a gestão de edifícios suportada por dispositivos inteligentes e modelos de ambientes inteligentes

A utilização de dispositivos ligados à internet e modelos de ambientes inteligentes em sistemas de gestão de edifícios tem vindo a ganhar notoriedade nos últimos anos, sendo cada vez mais comum a sua aplicação em edifícios. Estes conceitos, de internet das coisas e ambientes inteligentes, fornecem um meio para automatizar e otimizar as operações de gestão de edifícios, levando a uma maior eficiência no uso dos recursos, diminuição de custos e aumento do conforto dos utilizadores. Contudo, muitas das soluções existentes carecem de interoperabilidade e modelos inteligentes que considerem as necessidades e requisitos únicos de edifícios individuais e as preferências e necessidades dinâmicas dos utilizadores. Como principal objetivo, esta dissertação propõe a conceção, implementação, teste e validação de uma solução robusta que integra modelos de ambientes inteligentes e mecanismos de acesso controlado a dados. A solução proposta inclui a utilização de sensores e dispositivos ligados à internet para a recolha e analise de dados em tempo real, que serão posteriormente usados para a criação de modelos de previsão de comportamento do edifício e dos seus utilizadores. Para a identificação de padrões e contextos, foram concebidos algoritmos de aprendizagem automática e técnicas de análise de dados. O acesso aos dados, da solução proposta, contempla um mecanismo de acesso seguro e eficiente, seguindo as diretrizes do Regulamento Geral sobre a Proteção de Dados (RGPD), nacional e europeu. Para suportar o uso da solução proposta, foi concebida e implementada uma interface gráfica que permite aos gestores e utilizadores do edifício monitorizarem e controlarem as operações em tempo real, proporcionando-lhes a capacidade de responder rapidamente às condições atuais, tomando decisões informadas. Esta interface gráfica, baseada em web, permite ainda consultar os dados históricos e interagir com os modelos de suporte que foram desenvolvidos. A solução proposta foi avaliada através de casos de estudo executados em ambiente realista. Os resultados destes estudos foram utilizados para avaliar a eficácia da solução proposta na melhoria do desempenho dos edifícios. Os estudos concluem que a utilização de dispositivos inteligentes e modelos de ambientes inteligentes na gestão de edifícios é uma abordagem promissora que pode culminar em melhorias significativas no desempenho e operação dos edifícios inteligentes. Esta dissertação contribui para o domínio dos edifícios inteligentes, fornecendo uma solução abrangente que integra dispositivos ligados à internet e modelos de ambientes inteligentes para melhorar o desempenho dos edifícios e o conforto dos utilizadores.The use of internet connected devices and ambient intelligence models in building management systems has been gaining notoriety in recent years, and its application in buildings is becoming more and more common. These concepts, of the internet of things and ambient intelligence, provide a means to automate and optimise building management operations, leading to greater efficiency in the use of resources, reduced costs and increased user comfort. However, many existing solutions lack interoperability and intelligent models that consider the unique needs and requirements of individual buildings and the dynamic preferences and needs of users. As the main objective, this dissertation proposes the design, implementation, testing and validation of a robust solution that integrates ambient intelligence models and controlled data access mechanisms. The proposed solution includes the use of sensors and devices connected to the internet for real-time data collection and analysis, which will be later used for the creation of forecasting models for the behaviour of the building and its users. For the identification of patterns and contexts, machine learning algorithms and data analysis techniques were designed. The data access, of the proposed solution, contemplates a safe and efficient access mechanism, following the guidelines of the national and European General Data Protection Regulation (GDPR). To support the use of the proposed solution, a graphic interface was designed and implemented to allow building managers and users to monitor and control operations in real time, providing them with the ability to quickly respond to current conditions, making informed decisions. This web-based graphical interface also allows consulting historical data and interacting with the support models that were developed. The proposed solution was evaluated through case studies executed in a realistic environment. The results of these studies were used to evaluate the effectiveness of the proposed solution in improving building performance. The studies conclude that the use of smart devices and ambient intelligence models in building management is a promising approach that can culminate in significant improvements in the performance and operation of smart buildings. This dissertation contributes to the domain of intelligent buildings by providing a comprehensive solution that integrates internet-connected devices and ambient intelligence models to improve building performance and user comfort