Adaptive system to manage everyday user comfort preferences

Urban mobility brings many challenges and opportunities, particularly regarding sustainability. It is natural that we want better living conditions, we are naturally given to consuming, even when there is no need, we increasingly want to travel, socialize, enjoy and it is not easy to accept that we will most likely have to change. It is no longer a distant future, but the present that we are living. Even in the face of successful solutions, receptivity is far from being massified and in most cases it imposes compromises in terms of comfort and quality of life, sometimes even imposing new habits and ways of being. In addition, not all of us have the same perception of the situation seriousness, or the same willingness to compromise. And this can happen for numerous reasons, namely physical or health limitations, financial limitations, different beliefs/motivations, or different ways of facing problems. It is even common that the staunchest defender of certain solutions, when faced with other equally plausible solutions, is completely insensitive or even opposed. In fact, the same individual may have different needs/preferences relatively to the place where he is or the activity he is performing, that is, preferences that vary with time and place. In a broader context of mobility, in which individuals in their daily lives move and visit different places, often with the presence of more people, the situation is even more complex, the variability of preferences increases, and it is necessary to combine preferences/needs of different individuals. Emerging technologies, within the Internet of Things (IoT) scope and smart spaces [1], allow us to aspire to capable solutions in line with the urban mobility and sustainability demands and, at the same time, to promote better conditions of comfort and well-being, without imposing sacrifices or changes in habits and considering the specificities of each individual, at different time and place. These solutions whose success depends in part on the autonomy of operation, not requiring any direct and conscious participation of people, for the ability to make the best decisions given the current context and future expectations, the context being defined by the characteristics of the environment. Including the dynamics, namely those resulting from the presence/involvement of people, but also for the transparency of action, not being evasive and, if possible, fulfilling its function without people realizing the existence of the technology/solution simply the most convenient happens. There are other factors that should not be neglected, such as those related to security and privacy. In this paper, the authors propose an architecture that considers these requirements so that, in a non-evasive way, it adapts the different spaces that the user frequents (house, work, leisure, others) to their personal preferences, such as temperature, humidity, sound, environment, etc. The architecture includes the different devices needed, to identify users, as well as the communication technologies to be used to transfer the preferences of each user to the system. The architecture includes a multi-agent system that allows managing conflicts of preferences through a user’s hierarchy and that considers safety values for each preference, to safeguard the different actuators (air conditioning, fan coils, multimedia, etc.) present in space. It was developed, focusing on the definition of each user's preferences in a smartphone application, which allows the user's preferences to be transferred to the space, without the need to perform any interaction, they can also be passed through smartwatches, fitness bracelets and similar devices, which currently have different communication technologies such as Bluetooth Low Energy (BLE), Near Field Communication (NFC) or Wifi-Direct. It also contains a local processing solution, currently supported by a Raspberry Pi, and will be present in each space where we want to adapt to different preferences. Each of these systems constantly receives each present user preferences. Based on the multi-agent system, it calculates the optimal preferences to be applied to each space at a given time. It is also responsible for sending these to the different actuators present in the space. The multi-agent system has different layers (simulation, data acquisition, user information, actuation). Briefly, there is an agent for each user present, containing their preferences, and there is an agent that represents the pace, containing eventual constraints, such as security values and others that may exist, namely in public spaces. Each of these agents aims to represent the interests of the involved parties. For example, the agent representing the space should be focused on an efficient use of equipment, minimizing energy costs, enhancing the durability of the equipment, minimizing maintenance costs. Taking advantage of the different hierarchies, an equation was devised that meets the different preferences to define the optimal solution, which will be sent to the different actuators.info:eu-repo/semantics/publishedVersio

Espaços inteligentes: conhecedores de utilizadores, preferências, comportamentos e hábitos numa abordagem não invasiva

Este projeto de doutoramento está integrado nas atividades de investigação aplicada e desenvolvimento tecnológico da Techwelf, Lda, empresa dedicada à conceção de soluções de Intelligent Environment. Como salientado em [6] existem novas perspectivas de pesquisa na área de ambientes inteligentes que devem ser exploradas. Nomeadamente os conceitos de casas inteligentes e domótica [7], atualmente em crescente expansão tanto do ponto de vista de investigação científica, como a nível de procura do mercado de melhores soluções neste campo. Pretende-se tirar partido das tecnologias emergentes que suportam os denominados dispositivos wearables [12, 13], e da particularidade não invasiva destes, para de forma autónoma adaptar o ambiente aos parâmetros de conforto de cada utilizador (térmico, acústico, qualidade do ar, luminosidade, exposição solar e outros). Isto promoverá as condições de conforto à medida de cada individuo, potenciando soluções inovadoras e novos paradigmas no âmbito dos Intelligent Environments [8, 11]. Para consolidar e sustentar o projeto proposto, foram analisados diversos artigos científicos para validar a originalidade e inovação do projeto. Pelo que se constata, atualmente a recolha de dados para análise de comportamentos em ambientes inteligentes é efetuada sobretudo recorrendo à instalação de diversos sensores dispersos pelo ambiente [1, 3]. Como se concluiu em [2], o sistema perfeito de aprendizagem para ambientes inteligentes ainda não foi encontrado, e qualquer contributo nesta área coloca-nos um passo mais próximo da verdadeira realidade de ambientes inteligentes. É ainda referida a necessidade e o desafio de estabelecer um novo paradigma eficaz para ambient intelligence, onde o foco passe a ser o utilizador e a capacidade de gerir a complexidade e riqueza da vida humana diária [2, 11]. Um problema recorrente neste campo é a gestão de conflitos de interesses [5, 9], entre diversos utilizadores para um mesmo espaço, que nesta proposta de solução pretende ser ultrapassado recorrendo a sistemas de multiagentes, assim como à recolha em tempo real de informação do utilizador (temperatura corporal, pulsação) [4]. Após a análise do estado da arte, podemos salientar o carácter de inovação científica e contributo que este projeto poderá trazer a esta área. Pois propõe-se neste projeto a conceção e desenvolvimento de soluções com vista a estabelecer um novo paradigma. Poder-se-á, recorrendo às tecnologias e dispositivos wearables emergentes no mercado (smartwatches, smartphones, fitness trackers) [5], focalizar-se o processo de recolha de dados no utilizador sempre tendo em conta que será um processo não invasivo. Isto alavancará/enriquecerá de forma substancial o processo de tomada de decisão e ultrapassará os limites físicos até aqui impostos pela necessidade dos sensores serem colocados estaticamente no espaço. Pode referir-se esta proposta como tendo um carácter de relevante inovação, tanto a nível científico, como industrial. Anteriormente a esta proposta, a empresa efetuou diversos estudos de mercado, tendose concluído que a nível nacional não existe qualquer produto nesta área que possua qualquer tipo de automatismo. A nível internacional, existem alguns produtos com algumas funcionalidades implementadas, mas que necessitam sempre da programação e configuração destas por parte do utilizador, e não possuem qualquer grau de inteligência artificial, que possibilite capacidades preditivas e melhorias na eficácia do produto na tomada de decisão. Pretende-se criar uma solução que permita tirar partido das tecnologias emergentes no mercado que suportam os denominados dispositivos wearables (smartwatches, smartphones, fitness trackers) e não invasividade destes, para proceder à recolha de dados de uma forma autónoma, transparente e sem qualquer necessidade de intervenção por parte do utilizador, para com esta informação auxiliar o processo de tomada de decisão dos sistemas de conforto na sua tarefa de adaptar o ambiente aos parâmetros de conforto de cada utilizador (térmico, acústico, qualidade do ar, luminosidade, exposição solar e outros). Esta solução passará ainda por recorrer a sistemas de multiagentes inteligentes [17, 18], efetuar uma gestão completa a nível de possíveis conflitos de interesses que possam existir entre utilizadores para um mesmo espaço. Especificamente com este projeto pretende-se atingir os seguintes objetivos: - Caracterizar os diferentes tipos de ambiente (Ambient Intelligence). - Caracterizar o conforto nas suas diferentes vertentes e dimensões. - Definir uma arquitetura base para um sistema não invasivo que tire partido das tecnologias e dispositivos emergentes de recolha de dados wearables (smartwatches, smartphones, fitness trackers) para a finalidade prevista. - Utilizar agentes inteligentes [19] para representar os vários intervenientes, contextos e dimensões do problema, que cooperem para alcançar a solução ótima. - Desenvolver soluções que permitam a ubiquidade na identificação dos utilizadores e suas preferências de conforto, de forma automática e transparente, potenciando a integração entre o espaço e utilizador. - Definir uma solução de agentes [20] que facilite a interação do utilizador com os sistemas atuais. - Aplicar o protótipo proposto numa unidade de saúde e numa instituição de ensino superior, tirando partido das parcerias já existentes por parte da empresa albergue. - Avaliar o protótipo utilizando problemas reais/simulados de gestão de conflitos entre diferentes preferências de conforto de utilizadores para um mesmo espaço.info:eu-repo/semantics/publishedVersio

Is Context-aware Reasoning = Case-based Reasoning?

The purpose of this paper is to explore the similarities and differences and then argue for the potential synergies between two methodologies, namely Context-aware Reasoning and Case-based Reasoning, that are amongst the tools which can be used for intelligent environment (IE) system development. Through a case study supported by a review of the literature, we argue that context awareness and case based reasoning are not equal and are complementary methodologies to solve a domain specific problem, rather, the IE development paradigm must build a cooperation between these two approaches to overcome the individual drawbacks and to maximise the success of the IE systems

Is Context-aware Reasoning = Case-based Reasoning?

The purpose of this paper is to explore the similarities and differences and then argue for the potential synergies between two methodologies, namely Context-aware Reasoning and Case-based Reasoning, that are amongst the tools which can be used for intelligent environment (IE) system development. Through a case study supported by a review of the literature, we argue that context awareness and case based reasoning are not equal and are complementary methodologies to solve a domain specific problem, rather, the IE development paradigm must build a cooperation between these two approaches to overcome the individual drawbacks and to maximise the success of the IE systems

Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results

Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results

In2CoP 2020 - International Conference on Co-Creation Processes in Higher Education 2020: book of abstracts

A Cocriação e a Inovação no Ensino Superior representam um dos principais compromissos do Instituto Politécnico de Bragança (IPB), cuja atividade formativa e de investigação se orienta, de forma sinérgica, para a cooperação com as empresas e instituições da região. O IPB pretende contribuir, de forma ativa, para uma economia regional baseada no conhecimento e com uma forte articulação internacional. Nesta esteira, teve lugar, na cidade de Bragança, nos dias 29 a 30 de janeiro de 2020, a Conferência Internacional em Processos de Cocriação no Ensino Superior (In2CoP). Privilegiamos um espaço de reflexão de processos e de partilha de resultados de ecossistemas de cocriação, visando o desenvolvimento de uma comunidade de aprendizagem integradora, interdisciplinar e multicultural. Ao longo dos três dias da conferência, mais de 150 conferencistas, nacionais e estrangeiros, participaram, ativamente, nas diversas atividades propostas: uma sessão plenária com seis intervenções, quatro workshops e uma sessão pitch com a apresentação de trinta e seis projetos de inovação e cocriação. Com um espírito inovador, as atividades decorreram no campus do IPB e em espaços emblemáticos da cidade de Bragança, designadamente: Centro de Arte Contemporânea Graças Morais, Centro de Fotografia Georges Dussaud e Centro de Ciência Viva. Foi ainda realizada uma visita social ao Museu do Côa em Vila Nova de Foz Côa.Co-creation and Innovation in Higher Education represents one of the main commitments of the Polytechnic Institute of Bragança (IPB), whose training and research activity is synergistically oriented towards cooperation with companies and institutions in the region. The IPB intends to contribute actively to a regional economy based on knowledge and with a strong international articulation. In this context, the International Conference on Co-Creation Processes in Higher Education (In2CoP) took place in the city of Bragança, on January 29-30, 2020. We privilege a space for reflection of processes and sharing of results from co-creation ecosystems, aiming at the development of an integrative, interdisciplinary, and multicultural learning community. Over the three days of the conference, more than 150 national and foreign conferencists actively participated in the various activities proposed: a plenary session with six interventions, four workshops, and a pitch session with the presentation of thirty-six innovation and co-creation projects. With an innovative spirit, the activities took place in IPB's campus and in emblematic spaces in the city of Bragança, namely: Centro de Arte Contemporânea Graças Morais, Centro de Fotografia Georges Dussaud and Centro de Ciência Viva. A social visit was also made to the Côa Museum in Vila Nova de Foz Côa.info:eu-repo/semantics/publishedVersio

Learning frequent behaviours of the users in intelligent environments

Intelligent environments (IEs) are expected to support people in their daily lives. One of the hidden assumptions in IEs is that they propose a change of perspective in the relationships between humans and technology, shifting from a techno-centered perspective to a human-centered one. Unlike current computing systems where the user has to learn how to use the technology, an IE adapts its behavior to the users, even anticipating their needs, preferences, or habits. For this reason, the environment should learn how to react to the actions and needs of the users, and this should be achieved in an unobtrusive and transparent way. In order to provide personalized and adapted services, it is necessary to know the preferences and habits of users. Thus, the ability to learn patterns of behavior becomes an essential aspect for the successful implementation of IEs. This paper presents a system, learning frequent patterns of user behavior system (LFPUBS), that discovers users' frequent behaviors taking into consideration the specific features of IEs. The core of LFPUBS is the learning layer, which, unlike some other components, is independent of the particular environment in which the system is being applied. On one hand, it includes a language that allows the representation of discovered behaviors in a clear and unambiguous way. On the other hand, coupled with the language, an algorithm that discovers frequent behaviors has been designed and implemented. For this reason, it uses association, workflow mining, clustering, and classification techniques. LFPUBS was validated using data collected from two real environments. In MavPad environment, LFPUBS was tested with different confidence levels using data collected in three different trials, whereas in a WSU Smart Apartment environment LFPUBS was able to discover a predefined behavior

Learning frequent behaviours of the users in intelligent environments

Intelligent environments (IEs) are expected to support people in their daily lives. One of the hidden assumptions in IEs is that they propose a change of perspective in the relationships between humans and technology, shifting from a techno-centered perspective to a human-centered one. Unlike current computing systems where the user has to learn how to use the technology, an IE adapts its behavior to the users, even anticipating their needs, preferences, or habits. For this reason, the environment should learn how to react to the actions and needs of the users, and this should be achieved in an unobtrusive and transparent way. In order to provide personalized and adapted services, it is necessary to know the preferences and habits of users. Thus, the ability to learn patterns of behavior becomes an essential aspect for the successful implementation of IEs. This paper presents a system, learning frequent patterns of user behavior system (LFPUBS), that discovers users' frequent behaviors taking into consideration the specific features of IEs. The core of LFPUBS is the learning layer, which, unlike some other components, is independent of the particular environment in which the system is being applied. On one hand, it includes a language that allows the representation of discovered behaviors in a clear and unambiguous way. On the other hand, coupled with the language, an algorithm that discovers frequent behaviors has been designed and implemented. For this reason, it uses association, workflow mining, clustering, and classification techniques. LFPUBS was validated using data collected from two real environments. In MavPad environment, LFPUBS was tested with different confidence levels using data collected in three different trials, whereas in a WSU Smart Apartment environment LFPUBS was able to discover a predefined behavior