Smart Built Environment Including Smart Home, Smart Building and Smart City: Definitions and Applied Technologies

Technology, particularly over the past decades, has affected the cities and their components, such as building sectors. Consequently, smart building that has currently utilized various technologies which is incorporated into buildings is the core of the present chapter. It provides a comprehensive overview on smart cities, smart buildings and smart home to address what systems and technologies have been incorporated so far. The aim is to review the smart concepts in built environment with the main focus on smart cities, smart buildings, and smart homes. State-of-the-art and current practices in smart buildings were also reviewed to enlighten a set of directions for future studies. The Chapter is primarily focuses on 51 articles in smart buildings/homes, as per collected from various datasets. It represents a summary of systems utilized and incorporared into smart buildings and homes over the past decade (2010–2020). Additional to different features of smart buildings and homes, is the discussion around various fields and system performances currently utilized in smart buildings/homes. Limitations and future trends and directions is also discussed. In total, such building/home systems were categorized into 6 groups, including: security systems, healthcare systems, energy management systems, building/home management systems, automation systems, and activity/movement recognition systems. Furthermore, there are a number of surveys which investigated the user’s acceptance and adoption of the new smart systems in homes and buildings, as presented and summarized thereafter in Tables. The present Chapter is a contribution to a better understanding of the functions and performances of such buildings/homes for further implementation and enhancement so that varying demands of smart citizens are fulfilled and eventually contribute to the development of smart cities

Designing a goal-oriented smart-home environment

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-016-9670-x[EN] Nowadays, systems are growing in power and in access to more resources and services. This situation makes it necessary to provide user-centered systems that act as intelligent assistants. These systems should be able to interact in a natural way with human users and the environment and also be able to take into account user goals and environment information and changes. In this paper, we present an architecture for the design and development of a goal-oriented, self-adaptive, smart-home environment. With this architecture, users are able to interact with the system by expressing their goals which are translated into a set of agent actions in a way that is transparent to the user. This is especially appropriate for environments where ambient intelligence and automatic control are integrated for the user’s welfare. In order to validate this proposal, we designed a prototype based on the proposed architecture for smart-home scenarios. We also performed a set of experiments that shows how the proposed architecture for human-agent interaction increases the number and quality of user goals achieved.This work is partially supported by the Spanish Government through the MINECO/FEDER project TIN2015-65515-C4-1-R.Palanca Cámara, J.; Del Val Noguera, E.; García-Fornes, A.; Billhard, H.; Corchado, JM.; Julian Inglada, VJ. (2016). Designing a goal-oriented smart-home environment. Information Systems Frontiers. 1-18. https://doi.org/10.1007/s10796-016-9670-xS118Alam, M. R., Reaz, M. B. I., & Ali, M. A. M. (2012). A review of smart homes: Past, present, and future. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1190–1203.Andrushevich, A., Staub, M., Kistler, R., & Klapproth, A. (2010). Towards semantic buildings: Goal-driven approach for building automation service allocation and control. In 2010 IEEE conference on emerging technologies and factory automation (ETFA) (pp. 1–6) IEEE.Ayala, I., Amor, M., & Fuentes, L. (2013). Self-configuring agents for ambient assisted living applications. Personal and Ubiquitous Computing, 17(6), 1159–1169.Cetina, C., Giner, P., Fons, J., & Pelechano, V. (2009). Autonomic computing through reuse of variability models at runtime: The case of smart homes. Computer, 42(10), 37–43.Cook, D. J. (2009). Multi-agent smart environments. Journal of Ambient Intelligence and Smart Environments, 1(1), 51–55.Dalpiaz, F., Giorgini, P., & Mylopoulos, J. (2009). An architecture for requirements-driven self-reconfiguration. In Advanced information systems engineering (pp. pp 246–260). Springer.De Silva, L. C., Morikawa, C., & Petra, I. M. (2012). State of the art of smart homes. 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Smart homes and their users:a systematic analysis and key challenges

Published research on smart homes and their users is growing exponentially, yet a clear understanding of who these users are and how they might use smart home technologies is missing from a field being overwhelmingly pushed by technology developers. Through a systematic analysis of peer-reviewed literature on smart homes and their users, this paper takes stock of the dominant research themes and the linkages and disconnects between them. Key findings within each of nine themes are analysed, grouped into three: (1) views of the smart home-functional, instrumental, socio-technical; (2) users and the use of the smart home-prospective users, interactions and decisions, using technologies in the home; and (3) challenges for realising the smart home-hardware and software, design, domestication. These themes are integrated into an organising framework for future research that identifies the presence or absence of cross-cutting relationships between different understandings of smart homes and their users. The usefulness of the organising framework is illustrated in relation to two major concerns-privacy and control-that have been narrowly interpreted to date, precluding deeper insights and potential solutions. Future research on smart homes and their users can benefit by exploring and developing cross-cutting relationships between the research themes identified

Smart Home and Artificial Intelligence as Environment for the Implementation of New Technologies

The technologies of a smart home and artificial intelligence (AI) are now inextricably linked. The perception and consideration of these technologies as a single system will make it possible to significantly simplify the approach to their study, design and implementation. The introduction of AI in managing the infrastructure of a smart home is a process of irreversible close future at the level with personal assistants and autopilots. It is extremely important to standardize, create and follow the typical models of information gathering and device management in a smart home, which should lead in the future to create a data analysis model and decision making through the software implementation of a specialized AI. AI techniques such as multi-agent systems, neural networks, fuzzy logic will form the basis for the functioning of a smart home in the future. The problems of diversity of data and models and the absence of centralized popular team decisions in this area significantly slow down further development. A big problem is a low percentage of open source data and code in the smart home and the AI when the research results are mostly unpublished and difficult to reproduce and implement independently. The proposed ways of finding solutions to models and standards can significantly accelerate the development of specialized AIs to manage a smart home and create an environment for the emergence of native innovative solutions based on analysis of data from sensors collected by monitoring systems of smart home. Particular attention should be paid to the search for resource savings and the profit from surpluses that will push for the development of these technologies and the transition from a level of prospect to technology exchange and the acquisition of benefits.The technologies of a smart home and artificial intelligence (AI) are now inextricably linked. The perception and consideration of these technologies as a single system will make it possible to significantly simplify the approach to their study, design and implementation. The introduction of AI in managing the infrastructure of a smart home is a process of irreversible close future at the level with personal assistants and autopilots. It is extremely important to standardize, create and follow the typical models of information gathering and device management in a smart home, which should lead in the future to create a data analysis model and decision making through the software implementation of a specialized AI. AI techniques such as multi-agent systems, neural networks, fuzzy logic will form the basis for the functioning of a smart home in the future. The problems of diversity of data and models and the absence of centralized popular team decisions in this area significantly slow down further development. A big problem is a low percentage of open source data and code in the smart home and the AI when the research results are mostly unpublished and difficult to reproduce and implement independently. The proposed ways of finding solutions to models and standards can significantly accelerate the development of specialized AIs to manage a smart home and create an environment for the emergence of native innovative solutions based on analysis of data from sensors collected by monitoring systems of smart home. Particular attention should be paid to the search for resource savings and the profit from surpluses that will push for the development of these technologies and the transition from a level of prospect to technology exchange and the acquisition of benefits

Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201

Dissertation: Examining and investigating home modifications and smart home technologies to reduce fall injury among older adults.

Nearly one in six U.S. residents are over the age of 65. The proportion of older adults in the U.S. is anticipated to grow to 22.1% of the total population by 2050. The cost of treating age related conditions and injuries is expensive, government programs including Medicaid paid over $550 Billion in 2017, and makes up between 14-16% of the federal budget each year. With the high cost of treating age related conditions and injuries, and the proportion of older adults continuing to increase every year, it is imperative that researchers and government entities find and invest in preventative measures in order to reduce injury and related healthcare costs. Among the many age-related injuries older adults suffer, falls are arguably the most important to address. It is estimated that one in three older adults has a fall every year. In 2016, falls were the seventh leading cause of death among older adults. Approximately one third of all fallers require medical attention after experiencing a fall. Over 800,000 older adults are hospitalized each year due to fall related injuries. Injuries sustained as a result of a serious fall include various fractures, traumatic brain injuries, and other cuts and bruises.Home modifications, and more recently smart home technologies, can help increase the safety of older adults living in the community. With older adults wanting to “age in place”, installing these modifications and technologies before an accident happens may lower rates of injury. Today, dozens of companies sell various smart home devices for the consumer market. Bud despite the high demand for these technologies by the American consumer, the ability of these devices to keep older adults safe, and how older adults value these technologies, remains uncertain. These home technologies may be particularly beneficial to older adults living in rural areas due to the increased isolation and limited access to healthcare resources. Previous research indicates rural populations have a greater proportion of older adults compared to urban areas, yet lack the infrastructure to provide specialty care to this population. It is estimated that more than 60 million family members provide some sort of informal care to an older adult relative. Of all of these family members, nearly 40% report spending 20 or more hours a week providing this unpaid care. Previous research has failed to examine how these family members feel about home modifications and technologies for their older adult relative. Finding ways to ease the burden of caring for older family members will significantly better the situations of many family relatives.This dissertation aims to cover three areas. 1. Identify people at risk of suffering subsequent fall injuries. Find the average time between an initial fall injury and a subsequent fall injury, and find average time between an initial fall injury and death.2. Examine the preferences of older adults living in a rural area towards various smart home technologies and home modifications.3. Examine the preferences of family members of older adults regarding smart home technologies and home modifications

Experiences of in-home evaluation of independent living technologies for older adults

Evaluating home-based independent living technologies for older adults is essential. Whilst older adults are a diverse group with a range of computing experiences, it is likely that many of this user group may have little experience with technology and may be challenged with age-related impairments that can further impact upon their interaction with technology. However, the evaluation life cycle of independent living technologies does not only involve usability testing of such technologies in the home. It must also consider the evaluation of the older adult’s living space to ensure technologies can be easily integrated into their homes and daily routines. Assessing the impact of these technologies on older adults is equally critical as they can only be successful if older adults are willing to accept and adopt them. In this paper we present three case studies that illustrate the evaluation life cycle of independent living technologies within TRIL, which include ethnographic assessment of participant attitudes and expectations, evaluation of the living space prior to the deployment of any technology, to the final evaluation of usability and participant perspectives