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Conceptual framework of smart homes.
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Smart homes are homes with technologically advanced systems to enable domestic task automation, easier communication, and higher security. As an enabler of health and well-being enhancement, smart homes have been geared to accommodate people with special needs, especially older people. This paper examines the concept of “smart home” in a technologi...
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... a digitally enriched living environment, the concept "smart home" is subject to various definitions and interpretations because being "smart" can imply various characteristics of a highly advanced modern home, such as being automatic, compact, innovative, convenient, self-adjusting, responsive, or functional. With a view to generating a sound background for further discussion, a conceptual framework of smart homes has been constructed based on a review of relevant literature (Figure 1). This proposed framework proposes that smart homes can be characterized or identified as having five basic features: ...
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Citations
... In [20], the authors identified four primary features of smart buildings: climate response, grid response, user response, monitoring, and supervision. At the same time, in [77], it is ascertained the five fundamental features of smart buildings: automation, multi-functionality, adaptability, interactivity, and efficiency [78]. The authors established the principal key indicators for assessment tools, categorizing them into key factors such as climate (including temperature, humidity, and solar radiation), building-related characteristics (encompassing type, area, orientation, and materials), building service systems and operation (involving space cooling/heating and hot water supply), user-related characteristics (involving user presence), and the building occupants' behavior and activities (including turning lights and TVs on/off). ...
The impact of global climate change on the built environment emphasizes the need for sustainable development goals (SDGs) using technological solutions, such as the Internet of Things (IoT). The significance of innovative building assessment (BA) tools plays a pivotal role in bridging the existing gap between the theoretical and actual operational performance of buildings. The main research question is how can a new generation of BA tools leverage the IoT to optimize occupant well-being and achieve SDGs’ targets. This article delves into the pivotal role played by the IoT and occupant-centric concepts in advancing sustainability initiatives and facilitating the achievement of SDGs. The novelty of this paper lies in its exploration of the current state of IoT integration as a strategic imperative for SDGs’ achievement and climate change mitigation. Consequently, a paradigm shift is evident in this work, showcasing a comprehensive comparison between conventional and IoT occupant-centric BA tools and introducing a correlation study between IoT occupant-centric systems and future SDGs’ targets. Lastly, current gaps and valuable insights into future research possibilities are offered.
... In addition, due to a lack of digital competence, older people often struggle to use smart health products effectively without the help of family, friends, or other groups, resulting in product functionality not being truly realized. A large number of literature points out that due to the lack of market feedback and product data, existing products targeted at the elderly are difficult to be really used [11]. This reduces the practical feasibility of smart health services and further exacerbates the resistance of the elderly to using them. ...
Currently, an increasing number of elderly people are choosing to live in elderly care communities, where they have access to smart health services. Despite the availability of these services, the utilization rate among the elderly remains low, resulting in the elderly not fully experiencing the benefits of this advanced technology. To ensure more elderly people to receive these benefits, this study constructed a model of the mechanisms affecting the use of smart health services by elderly people in retirement communities based on the TAM theory and collected data through questionnaires. A total of 1,292 valid questionnaires were collected from 8 retirement communities in several cities in China, and the data were analyzed by PLS-SEM. This study finds that different types of trust differ in how they influence the elderly’s perception of smart health services; perceived ease of use cannot directly affect the elderly’s willingness to adopt smart health services but should be achieved through the influence of health expectations; various types of social support play an important role in the elderly’s perceived usability of smart health services; and perceived usefulness plays an important role in the adoption of smart health services. In addition, through a multi-cohort comparative analysis, this study found differences in gender, age, income, and education in the adoption of health services by the elderly. The findings support the advancement and implementation of smart healthcare services among the elderly.
... Through the literature review and user interviews, the item pool of the perceived risk of smart homes for the future elderly was formed. Low accuracy (Wrong command, False alarm, Technical failure, Accuracy and performance, Inaccurate measurement, Authentication), Fear of malfunction (Malfunction concerns), Inflexibility (Stubbornness, Modularity), Feasibility (Architecture issues, Pattern recognition issues, Perceived hassle factor, Restriction in distance or time away from home, Complexity assessment,), Low compatibility (Incompatibility of devices, Integration issues, Compatible devices, Lack of interoperability, Lack of interoperability among heterogeneous systems), Insufficient system reliability (Reliability, Lack of reliability in the sensor system, Loss connection, Sensor uncertainty management, Long-term reliability, Lack of continuous monitoring), Expansion capability, Stability (Robustness, General system stability, Risk of old-fashioned system), Lack of information to organize programs for the elderly (Continuous learning, Prediction, Recommendation and decision (AI-driven)), Data management (Volume of data collection, Recording and storage of data, Data handling capability and compression techniques, Real-time data analysis, Salient summary generation from large amounts of sensory data) [8,24,[26][27][28]30,33,34,[36][37][38][39][40][43][44][45][46]51,52] Performance Risk (PER) ...
The 21st century has marked the dawn of an aging population. China’s aging process ranks first worldwide. The country has recognized the gravity of this demographic shift and implemented strategies to address it at the national level. A vast majority of elderly Chinese individuals (approximately 90%) aspire to age in their own homes. Smart homes, endowed with cutting-edge digital technologies, such as AI, the Internet of Things, and big data, hold vast potential for enabling this vision. However, acceptance of smart home products and services among elderly individuals in China remains low. The main reason is that the related products and services fail to effectively alleviate the perceived risk of this population in the R&D process of related products and services, and there is a lack of effective measurement methods. To holistically assess the potential obstacles faced by elderly individuals using smart home products and services, this study targeted individuals aged 45–60 years in China. This study aimed to develop a comprehensive perceived risk scale specific to smart homes for this demographic. Initially, this study identified key risk dimensions and corresponding measurement items through a rigorous literature review, user interviews, and expert consultations. Subsequently, it ensured the reliability and validity of each dimension and its corresponding observation variables through preliminary research, exploratory factor analysis, and confirmatory factor analysis. This approach allowed for a comprehensive understanding of the challenges faced by future elderly individuals when utilizing smart home products and services, thus enabling the development of more effective solutions. The scale encompassed ten factors and seventy measurement items, including Privacy and Security Risk (seven items), Physical Risk (seven items), Technological Risk (nine items), Performance Risk (seven items), Service Risk (nine items), Financial Risk (five items), Psychological Risk (seven items), Industry and Market Risk (six items), Social Support Risk (six items), and Policy and Legal risk (seven items). The measurement scale developed in this study represents a groundbreaking first attempt to create a systematic scale for assessing the perceived risks associated with smart homes for the elderly in China. It not only enables professionals, businesses, and manufacturers to avoid or reduce barriers in the R&D process of related products and services, facilitating smart home industry growth and enhancing user adoption, but also serves as a universal reference for the potential obstacles that digital technology may encounter in addressing aging-related issues, which has significant theoretical value and practical importance.
... More recent definitions of smart buildings consist of heterogeneous artificial intelligence (AI) models to solve problems related not only to buildings directly but also to personalization, electric grid, and data Fig. 9 Overview of blockchain technology in energy peer-to-peer trading. Source: [155] security according to [134,170]. Utilizing these models requires that the building has advanced control mechanisms that can ensure the reliability and operating points for the models. This control system is often referred to as (building) energy management systems, but some other system, such as average voltage control, can be deployed as well [133,133]. ...
The United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20–30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades. Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications. Therefore, this paper presents a systematic review of the literature, and then creates a novel taxonomy for applications of smart building energy utilization. The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization, (b) a novel taxonomy for the applications, (c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally, (d) a discussion on open issues and developments in the field.
... Domotics focuses on transforming conventional homes into smart homes. This means that smart technologies are used to control indoor environments by installing intelligent lighting systems, entertainment systems, temperature controllers, and other applications to improve the comfort and safety for any user [19,20]. All mechanical and digital devices are interconnected to a network allowing the communication with each other and with the final user to create an interactive space [21]. ...
... This can be achieved by providing a proactive environment that is aware of their inhabitants' personal and emotional needs based on their location in the smart home, and it can have several solutions for those specific needs [22]. Every smart home system must have the following features [20]: ...
Domotics (Home Automation) aims to improve the quality of life of people by integrating intelligent systems within inhabitable spaces. While traditionally associated with smart home systems, these technologies have potential for User Experience (UX) research. By emulating environments to test products and services, and integrating non-invasive user monitoring tools for emotion recognition, an objective UX evaluation can be performed. To achieve this objective, a testing booth was built and instrumented with devices based on KNX, an international standard for home automation, to conduct experiments and ensure replicability. A framework was designed based on Python to synchronize KNX systems with emotion recognition tools; the synchronization of these data allows finding patterns during the interaction process. To evaluate this framework, an experiment was conducted in a simulated laundry room within the testing booth to analyze the emotional responses of participants while interacting with prototypes of new detergent bottles. Emotional responses were contrasted with traditional questionnaires to determine the viability of using non-invasive methods. Using emulated environments alongside non-invasive monitoring tools allowed an immersive experience for participants. These results indicated that the testing booth can be implemented for a robust UX evaluation methodology.
... The sensors (or input devices) and actuators of smart things like smartphones, computers, and robots make up this subsystem, which executes human interaction. Possible components of this subsystem include applications for managing devices, managing multimedia devices, searching for items, communicating with others, alleviating feelings of isolation, and answering questions [25]. ...
A new era of architectural ideas is likely to be ushered in by the natural progression of "smart buildings," which necessitates the integration of sensors, rich data, and artificial intelligence (AI) simulation models. Better control, enhanced reliability, and automation are just a few ways in which AI simulation models can make homes more convenient, more comfortable, and more energy efficient. This article discusses the ways in which AI models can be used to enhance the development of smart homes, particularly in the realm of interior design. This case study demonstrates how AI may be integrated into smart homes to enhance the user experience and reduce energy consumption. After that, the essay will delve into the study of current research on the application of AI technology in smart houses, utilizing a wide range of novel concepts such as smart interior design and a Smart Building System Framework based on digital twins (DT). The benefits of employing AI models in smart homes, with an emphasis on living areas, are discussed at length before the paper concludes. The theme's case study is meant to inspire new ways of thinking about how artificial intelligence (AI) might be practically implemented in smart homes to enhance their utility, comfort, and environmental friendliness. The ultimate objective is to maximize the benefits of AI in order to revolutionize domestic life and enhance the quality of human existence. Unanswered questions and promising directions for future AI research in the realm of smart homes are addressed in the article's last section. Smart houses that incorporate AI technology are beneficial to homeowners because they improve security, convenience, and energy economy.
... Deviations from usually pursued activities could be detected, and emergency action can be initiated based on the extent and type of deviation. Experts can also record and examine these deviations if needed [25,50]. ...
Human activity recognition (HAR) and abnormal / anomaly detection have significant applications for health monitoring in a smart environment. Abnormal / anomaly prediction during daily activities helps to indicate whether the person is healthy or having behavior issues that need assistance. HAR has been accomplished using deep learning approaches. The activation functions employed in deep learning models have a significant impact on the training model and the reliability of the model. Several activation functions are developed for deep learning models. Nevertheless, most existing activation functions suffer from the dying gradient problem and lack of utilization of large negative input values. This work proposes a novel activation function called Optimized Parametric Hyperbolic Tangent Swish (OP-Tanish), which is non-monotonicity, unbounded in both negative and positive directions, smooth variations, and introduces a higher degree of non-linearity than Relu and other state-of-art activation functions. We test this activation function by training on customized shallow 1D- Convolutional Neural Networks (CS1DCNN) for adequate recognition of human activities and anomaly detection. The contributions are compared to the state-of-the-art activation functions on benchmark datasets (UCI-HAR, PAMPA2, Opportunity, and Daphnet Gait HAR datasets; UP-FALL and Simulated Activities of Daily Living (SIMADL) anomaly datasets), namely: ReLu and its variants, SWISH, MISH, and LiSHT. The proposed OP-Tanish activation function outperforms other state-of-art activation functions with an accuracy of 99.58%, 99.58%, 95.14%, and 97.79%over UCI, Opportunity, PAMPA2, and Daphnet Gait datasets, respectively, and achieved an accuracy of 97.28% and 98% on UP-Fall and SIMADL dataset.
... Considering that there is no standard form of smart home, as designs vary and are tailor-made according to the user's characteristics and needs [28], the need to identify specific challenges and concerns that would hinder the effective use of SHTs in elderly residences is essential. For example, Lê et al. [110] identified two issues: accessibility and ethical considerations. Accessibility includes (i) financial accessibility (e.g., affordability), (ii) technical accessibility (e.g., user-friendliness) and (iii) psychological accessibility (e.g., acceptability and trust). ...
... Second, the study also revealed the importance of the government in providing financial assistance for the SHT installation to enhance the safety of the elderly. Therefore, it is recommended that the government provide schemes to assist various categories of citizens that cannot afford SHT devices for their elderly [110]. In fact, the elderly should be given top priority, as the installation of SHTs plays a crucial role in achieving safety performance, timely health monitoring, quick medical attention and social interaction. ...
... Perceptions of using SHTs for safety and well-being and their sources.P12Awareness should be created about the multiple benefits of smart home technology devices for elderly care[98,111] P13 Smart homes technologies demand will likely increase due to the rising ageing population[83,84] P14The government should provide a financial incentive for the installation of smart home technologies for elderly safety and well-being[110] ...
An ageing population is a global phenomenon. Like other developed economies, Hong Kong Special Administrative Region (HKSAR), China, also faces a severe ageing problem. One initiative to enhance the safe living and well-being of the growing elderly population is to assist them by building ageing-friendly living environments with the application of smart home technologies (SHTs). Therefore, this study focused on investigating the perception of professionals on the use of SHTs to improve and enhance the “ageing-in-place” (AIP) of elderly residents in HKSAR, China. A questionnaire survey was employed to obtain the perception of professionals with requisite knowledge of the older people facility needs regarding SHTs in achieving AIP for the elderly. The data retrieved were analysed with different statistical analyses. Based on the results of the analyses, all the professionals had similar perceptions of the use of SHTs for the safety and well-being of the elderly, except for the incongruence observed between the government employees, contractors and academic regarding how SHTs may not help to better monitor elderly daily activities. The possible reasons for the inconsistent opinions of the academics with other groups were linked to the knowledge of human behaviours and early dementia symptoms in gerontology. The findings will help care receivers, healthcare professionals, social workers, policymakers, smart home designers and developers to improve and enhance AIP in elderly residences in HKSAR, China.
... Throughout much of the literature, suggestions are given toward environmental adjustments in making for a better aging in place experience (e.g., adjusting activities so as to limit the possibility of falls and injuries, and use of personal alert systems) (Lê et al., 2012). Other research promoting change looks at functional capacity, movement, and changes in activities as we rethink the aging body (Pereira et al., 2008). ...
Aging research tends to define rural as any area that is not urban without considering the diversity that exists within rural environments. Using government guidelines defining frontier and rural counties, the purpose was to identify similarities and differences in the aging experience reported by community-dwelling rural and frontier older adults. Individual interviews were completed in Wyoming with 142 older adults from frontier (n = 72) and rural counties (n=70). Summative content analysis was used to evaluate responses within the framework of social influences and nested environmental interactions of a socio-ecological model. Rural older adults reported needing more medical services and care, while frontier adults indicated an absence for many of these services. Similar response patterns were indicated with regard to grocery stores and general shopping. Current interview statements provide foundational information for future policies that recognize frontier aging in place that does not equate to aging in rural specific areas.
... While numerical formulations of problems are helpful, inconsistent assessment criteria make it hard to establish design goals [17,18]. Lê et al. [19,20] outline the foundations of smart buildings as follows: adaptability (the ability to learn, predict, and satisfy the needs of users and the stress from the external environment); multi-functionality (the ability to allow the performance of more than one function in a building); interactivity (the ability to enable the interaction among users); and efficiency. ...
Artificial Intelligence (AI) simulation models and digital twins (DT) are used in designing and treating the activities, layout, and functions for the new generation of buildings to enhance user experience and optimize building performance. These models use data about a building’s use, configuration, functions, and environment to simulate different design options and predict their effects on house function efficiency, comfort, and safety. On the one hand, AI algorithms are used to analyze this data and find patterns and trends that can guide the design process. On the other hand, DTs are digital recreations of actual structures that can replicate building performance in real time. These models would evaluate alternative design options, the performance of the building, and ways to improve user comfort and building efficiency. This study examined the important role of intelligent building design aspects, such as activities using multi-layout and the creation of particular functions based on AI simulation models, in developing DT-based smart building systems. The empirical data came from a study of architecture and engineering firms throughout the globe using a CSAQ (computer-administered, self-completed survey). For this purpose, the study employed structural equation modeling (SEM) to examine the hypotheses and build the relationship model. The research verifies the relevance of AI-based simulation models supporting the creation of intelligent building design features (activities, layout, functionalities), enabling the construction of DT-based smart building systems. Furthermore, this study highlights the need for further exploration of AI-based simulation models’ role and integration with DT in smart building design.
