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The development of the COVID-19 epidemic has increased the home learning time of children. More researchers began to pay attention to children's learning in home. This survey reviewed the frontier and classic cases in the field of interactive design of children's home learning in the past five years, analyzed tangible user interface, augmented reality, and multimodal interaction in human-computer interaction of children's home learning. This paper reviewed the application of interactive system in children's learning and points out its positive side in development of ability, process of learning, habits of learning, and environment of learning of children. Through analysis, we advise that it is necessary to create home learning applications, link smart home systems, and build an interactive learning environment for smart home learning environment design. Finally, we point out the technical and ethical problems existing in the current research, proposes that intelligent perception, emotion recognition, and expression technologies should be introduced in the future, and looks forward to the development of this field. © 2023 Institute of Computing Technology. All rights reserved.
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With the goal of designing smart environments that can support users' physical/mental well-being, we studied users' experiences and different factors that can influence success of smart home devices through an online study conducted during and after the COVID-19 restrictions in June 2021 (109 participants) and March 2022 (81 participants). We investigated what motivates users to buy smart home devices, and if smart home devices may have the potential to improve different aspects of users' well-being. As COVID-19 emphasized a situation where people spent a significant amount of time at home in Canada, we also asked if/how COVID-19 motivated purchase of smart-home devices and how these devices affected participants during the pandemic. Our results provide insights into different aspects that may motivate the purchase of smart home devices and users' concerns. The results also suggest that there may be correlations between the use of specific types of devices and psychological well-being.
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A smart home is a component of the Internet of Things (IoT) technology implementations that help people with their daily activities. To link devices to the Internet of Things, a variety of communication methods can be used. Impairments restrict the activities that disabled people can participate in. This paper proposes an automation system that enables disabled people to control televisions (TVs), lights, and fans, any other electrical devices at home, using just voice commands without moving. The Google Assistant feature for mobile phones is used to achieve voice recognition on electronic components. This system also contains the concept of human temperature measurement where the temperature sensor, fixed to the door, checks the temperature of the person and opens when it is normal. This prevents the user from getting infected by the illness, keeping in mind the present situation of covid19. © 2023 IEEE.
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Over the past two years, the world has witnessed one of the worst pandemics due to the outbreak of coronavirus (covid19), which has infected hundreds of millions and claimed the lives of millions across the globe. If we have learned anything from this pandemic, it is that the actual healthcare systems are unreliable under situations of enormous pressure. Accordingly, the present investigation tackles smart healthcare paradigm as a solution to transform the classical healthcare model into a sustainable one. Therefore, this paper reviews the most advances on remote healthcare monitoring technologies and introduces a novel smart home architecture combined with cloud computing and machine learning to create a sustainable solution for healthcare. Furthermore, a case study of a patient with heart disease is suggested to highlight the importance of using machine learning to automate medical monitoring at home. Additionally, an investigation of human behavior using neural network transformers is suggested as a perspective of the research in hand to examine patients' activities at home using surveillance camera thus constructing a resilient remote healthcare model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Many researchers have studied non-expert users' perspectives of cyber security and privacy aspects of computing devices at home, but their studies are mostly small-scale empirical studies based on online surveys and interviews and limited to one or a few specific types of devices, such as smart speakers. This paper reports our work on an online social media analysis of a large-scale Twitter dataset, covering cyber security and privacy aspects of many different types of computing devices discussed by non-expert users in the real world. We developed two new machine learning based classifiers to automatically create the Twitter dataset with 435,207 tweets posted by 337,604 non-expert users in January and February of 2019, 2020 and 2021. We analyzed the dataset using both quantitative (topic modeling and sentiment analysis) and qualitative analysis methods, leading to various previously unknown findings. For instance, we observed a sharp (more than doubled) increase of non-expert users' tweets on cyber security and privacy during the pandemic in 2021, compare to in the pre-COVID years (2019 and 2020). Our analysis revealed a diverse range of topics discussed by non-expert users, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, help-seeking, and roles of different stakeholders. Overall negative sentiment was observed across almost all topics in all the three years. Our results indicate the multi-faceted nature of non-expert users' perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on their perspectives. © 2022
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Purpose In the past, older adults had ambivalent attitudes regarding the use of technology (Pirzada et al., 2022). However, the pandemic has been a catalyst for the use of technology. The COVID-19 pandemic has forced individuals to change habits and increase their trust in technology (Umair et al., 2022). This is an opportunity for older adults in general, specifically long-term care communities can take advantage of this and adapt their living spaces with assistive technologies. Smart home technology includes automation, social connectivity, activity, and health monitoring, among others. The use of smart home technology will benefit not only older adults' quality of life but also may reduce costs and improve the quality of care within long-term facilities (Borelli et al., 2019). Therefore, this qualitative study explored the perception of residents living in a retirement community regarding the use of a portal that integrated smart-home technology. Method Qualitative data were collected through semi-structured interviews with nine residents. The interviewer asked about demographics, use of devices, and the internet. Also, residents expressed their experiences living in smart-cottages that had a responsive platform (portal). The portal allowed older adults to control domestic devices and appliances, and communicate with the retirement community using a tablet. The interviews were transcribed, coded, and organized in themes using NVivo software. Results and Discussion The findings are presented in Table 1. Residents indicated that the portal was a protected and supportive tool. Older adults felt a strong sense of being cared for because if something went wrong with home services (such as the furnace, air conditioner, or lights), maintenance was notified automatically through the portal. Also, the motion sensors notified the nursing staff if residents fell or if they stayed in bed too long. A resident said, "They keep track. To make sure we are alive”. Residents enjoyed scheduling services directly in the portal without contacting staff. Older adults chose what services they wanted to use. Some of them used the portal for communication with family members, but not with other residents because they preferred face-to-face communication. Residents associated the portal with safety, care, and support from the facility. The use of smart home technologies in retirement communities is perceived as a high-quality service by the residents. It increases technology literacy and improves residents' quality of life. Furthermore, the use of technology can save energy and reduce costs to the retirement community, increase customer satisfaction, and it can be a potential solution to the shortage of professionals in the aging field. Finally, the use of smart-home technologies can help residents to age in their cottages (aging in place) without moving to sections of the community that require a higher level of care such as assisted living or nursing homes. © 2022, Gerontechnology. All Rights Reserved.
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Social isolation is likely to be one of the most serious health outcomes for the elderly due to the COVID-19 pandemic, especially for seniors living alone at home. In fact, two approaches have been used to assess social isolation. The first is a self-reported survey designed for research purposes. The second approach is the use of monitoring technology. The objective of this paper is to provide some illustrative publications, works and examples of the current status and future prospects in the field of monitoring systems that focused on two main activities of daily living: meal-taking activity (shopping, cooking, eating and washing dishes) and mobility (inside the home and the act of going out). These two activities combined seem relevant to a potential risk of social isolation in the elderly. Although current research focuses on identifying only ADLs, we propose to use them as a first step to extract daily habits and risk level of social isolation. Moreover, since activity recognition is a recent field, we raise specific problems as well as needed contributions and we propose directions and research opportunities to accelerate advances in this field.
Subject(s)
Activities of Daily Living , COVID-19 , Humans , Aged , Pandemics/prevention & control , Social Isolation , TechnologyABSTRACT
Many researchers have studied non-expert users’ perspectives of cyber security and privacy aspects of computing devices at home, but their studies are mostly small-scale empirical studies based on online surveys and interviews and limited to one or a few specific types of devices, such as smart speakers. This paper reports our work on an online social media analysis of a large-scale Twitter dataset, covering cyber security and privacy aspects of many different types of computing devices discussed by non-expert users in the real world. We developed two new machine learning based classifiers to automatically create the Twitter dataset with 435,207 tweets posted by 337,604 non-expert users in January and February of 2019, 2020 and 2021. We analyzed the dataset using both quantitative (topic modeling and sentiment analysis) and qualitative analysis methods, leading to various previously unknown findings. For instance, we observed a sharp (more than doubled) increase of non-expert users’ tweets on cyber security and privacy during the pandemic in 2021, compare to in the pre-COVID years (2019 and 2020). Our analysis revealed a diverse range of topics discussed by non-expert users, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, help-seeking, and roles of different stakeholders. Overall negative sentiment was observed across almost all topics in all the three years. Our results confirm the multi-faceted nature of non-expert users’ perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on their perspectives.
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Modern households are increasingly becoming digitized as they contain numerous Internet-connected networked devices throughout the home. However, this growth in the adoption of smart devices in households comes with the risk of Internet cyber-attacks that seem to be increasing every year. With many individuals working from home due to the COVID-19 pandemic, smart home networks are becoming small extensions of the organizational IT infrastructure. Amidst this backdrop, this behavioral study aims to understand the factors that drive an individual's intention toward securing their home network from cyber-attacks. We draw upon rational choice theory (RCT) and theory of planned behavior (TPB) to derive a model consisting of cognitive and psychological components to explain an individual's intention to secure their smart home network. From a survey of 503 working professionals, our data analysis shows strong support for our research model, and, thus, the hypothesized relationships between the cognitive and psychological factors and individual security intentions. Practical implications of these results for home users, organizations, and researchers are discussed, which will be helpful for organizational IT security managers in planning for organizational security as the line between home and workplace is becoming increasingly blurred.
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The reduction in face-to-face contact and the increase in time spent at home during the ongoing coronavirus disease pandemic have resulted in increasing interest and demand for smart homes. Further, the rapid increase in the number of one-person and two-person households in Korea recently has led to these becoming representative household types. This study identifies the wellness characteristics of such households and proposes a direction for smart home development to help them lead healthy, happy lives. It focuses on mapping residents' perceptions and experiences to scenarios and on identifying the functions required in smart homes and the technologies needed to provide these functions. It uses data from a survey to investigate and analyze the wellness characteristics of one- and two-person households in five dimensions and develops five scenarios of representative household types. By analyzing the developed scenarios, this study proposes smart homes that support the wellness of such households in six categories: exercise/sports, hobby/entertainment, social communications, occupation/work, self-development/education, and energy conservation. These households are exposed to digital environments from an early age and are familiar with the internet and technologies. Therefore, they are likely to adopt innovative technologies in housing. Thus, the smart home development proposed in this study is a promising strategic approach to housing planning.
Subject(s)
Health Status , Housing , Humans , Technology , Longitudinal Studies , Republic of KoreaABSTRACT
With the COVID-19 pandemic, the importance of home health care to manage and monitor one's health status in a home environment became more crucial than ever. This change raised the need for smart home health care services (SHHSs) and their extension to everyday life. However, the factors influencing the acceptance behavior of SHHSs have been inadequately investigated and failed to address why users have the intention to use and adopt the services. This study aimed to analyze the influential factors and measure the behavioral acceptance of SHHSs in South Korea. This study adopted the integrated model of the unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) to understand the behavioral acceptance of SHHSs from users' perceptions and task-technology fit. Multiple-item scales were established based on validated previous measurement scales and adjusted in accordance with SHHS context. Data from 487 valid samples were analyzed statistically, applying partial least square structural equation modeling. The results indicated that the integrated acceptance model explained 55.2% of the variance in behavioral intention, 44.9% of adoption, and 62.5% of the continuous intention to use SHHSs, supporting 11 of the 13 proposed hypotheses. Behavioral intention was positively influenced by users' perceptions on performance expectancy, effort expectancy, social influence, and functional conditions. Task-technology fit significantly influenced performance expectancy and behavioral intention, validating the linkage between the two models. Meanwhile, task characteristics were insignificant to determine task-technology fit, which might stem from complex home health care needs due to the COVID-19 pandemic, but were not sufficiently resolved by current service technologies. The findings implied that the acceptance of SHHSs needs to be evaluated according to both the user perceptions of technologies and the matching fit of task and technology. Theoretically, this study supports the applicability of the integrated model of UTAUT and TTF to the domain of SHHS, and newly proposed the measurement items of TTF reflecting the domain specificity of SHHS, providing empirical evidence during the pandemic era in South Korea. Practically, the results could suggest to the planners and strategists of home health care services how to promote SHHS in one's health management.
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COVID-19 , Home Care Services , Humans , COVID-19/epidemiology , Pandemics , Republic of Korea , TechnologyABSTRACT
Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems;It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies.
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Sensor networks are deployed in people's homes to make life easier and more comfortable and secure. They might represent an interesting approach for elderly care as well. This work highlights the benefits of a sensor network implemented in the homes of a group of users between 55 and 75 years old, which encompasses a simple home energy optimization algorithm based on user behavior. We analyze variables related to vital signs to establish users' comfort and tranquility thresholds. We statistically study the perception of security that users exhibit, differentiating between men and women, examining how it affects the person's development at home, as well as the reactivity of the sensor algorithm, to optimize its performance. The proposed algorithm is analyzed under certain performance metrics, showing an improvement of 15% over a sensor network under the same conditions. We look at and quantify the usefulness of accurate alerts on each sensor and how it reflects in the users' perceptions (for men and women separately). This study analyzes a simple, low-cost, and easy-to-implement home-based sensor network optimized with an adaptive energy optimization algorithm to improve the lives of older adults, which is capable of sending alerts of possible accidents or intruders with the highest efficiency.
Subject(s)
Algorithms , Perception , Aged , Female , Humans , Male , Middle AgedABSTRACT
Medical surveillance has been constantly linked to hospitals and infirmaries. However, the recent increase in demand for health assistance, especially with the current covid-19 pandemic, has made it clear that relying on placing patients on hospitals for surveillance is deprecated. In the same context, this paper strives to illuminate the significance of using trending paradigms such as home automation and artificial intelligence to better advance and modernize healthcare systems. Accordingly, the main contribution of this study is a demonstration of a novel smart home architecture and an evaluation of machine-learning algorithms aimed at predicting a health condition severity based on the patient data gathered from several sensors and wearables. In respect to the need of providing a real time alerting system, several classification algorithms are highlighted with their advantages in mitigating the remote health monitoring problematic. The results assessment of the machine learning algorithms emphasizes the convenience of using artificial intelligence for health monitoring regardless of time and place constraints. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This paper presents a cost-effective and flexible electronic textile sensor with high sensitivity and fast response and demonstrates its versatile applications, including real-time measurements of finger kinematics, phonation, cough patterns, as well as subtle muscle movements (i.e., eye reflex). The sensor can discriminate between speech and cough patterns, thereby expanding its applications to COVID-19 detection, speech rehabilitation training, and human/machine interactions. A combination of different sensor data is essential to acquire clinically significant information. Therefore, a sensor array is interfaced with the LoRa communication protocol to establish an Internet of Things (IoT)-based electronic textile framework. The IoT integration allows remote monitoring of body kinematics and physiological parameters. Therefore, the proposed IoT-based framework holds the potential to provide real-time and continuous health monitoring to allow immediate intervention during this pandemic. © 2022 IEEE.
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The training of Human Activity Recognition (HAR) models requires a substantial amount of labeled data. Unfortunately, despite being trained on enormous datasets, most current models have poor performance rates when evaluated against anonymous data from new users. Furthermore, due to the limits and problems of working with human users, capturing adequate data for each new user is not feasible. This paper presents semi-supervised adversarial learning using the LSTM (Long-short term memory) approach for human activity recognition. This proposed method trains annotated and unannotated data (anonymous data) by adapting the semi-supervised learning paradigms on which adversarial learning capitalizes to improve the learning capabilities in dealing with errors that appear in the process. Moreover, it adapts to the change in human activity routine and new activities, i.e., it does not require prior understanding and historical information. Simultaneously, this method is designed as a temporal interactive model instantiation and shows the capacity to estimate heteroscedastic uncertainty owing to inherent data ambiguity. Our methodology also benefits from multiple parallel input sequential data predicting an output exploiting the synchronized LSTM. The proposed method proved to be the best state-of-the-art method with more than 98% accuracy in implementation utilizing the publicly available datasets collected from the smart home environment facilitated with heterogeneous sensors. This technique is a novel approach for high-level human activity recognition and is likely to be a broad application prospect for HAR.
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Human Activities , Supervised Machine Learning , HumansABSTRACT
Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). "Smart-Home" was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories-Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies-were developed and discussed. There is an imperative need to monitor CVD patients' vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients' needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency's role to be followed when designing, developing, and deploying Smart Home Technology for CVD.
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Following the outbreak of the Coronavirus (COVID-19) pandemic, many organisations have shifted to remote working overnight. The new reality has created conditions to use smart home technologies for work purposes, for which they were not originally intended. The lack of insights into the new application of smart home technologies has led to two research objectives. First, the paper aimed to investigate the factors correlating with productivity and perceived wellbeing. Second, the study tried to explore individuals' intentions to use smart home offices for remote work in the future. 528 responses were gathered from individuals who had smart homes and had worked from home during the pandemic. The results showed that productivity positively relates to service relevance, perceived usefulness, perceived ease of use, hedonic beliefs, control over environmental conditions, innovativeness and attitude. Task-technology fit, service relevance, attitude to smart homes, innovativeness, hedonic beliefs, perceived usefulness, perceived ease of use and control over environmental conditions correlate with perceived wellbeing. The intention to work from smart home-offices in the future is determined by perceived wellbeing. Findings contribute to the research on smart homes and remote work practices, by providing the first empirical evidence about the new applications and outcomes of smart home use in the work context.
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Background: Telehealth and home-based care options significantly expanded during the SARS-CoV2 pandemic. Sophisticated, remote monitoring technologies now exist that support at-home care. Advances in the research of smart homes for health monitoring have shown these technologies are capable of recognizing and predicting health changes in near-real time. However, few nurses are familiar enough with this technology to use smart homes for optimizing patient care or expanding their reach into the home between healthcare touch points. Objective: The objective of this work is to explore a partnership between nurses and smart homes for automated remote monitoring and assessing of patient health. We present a series of health event cases to demonstrate how this partnership may be harnessed to effectively detect and report on clinically relevant health events that can be automatically detected by smart homes. Participants: 25 participants with multiple chronic health conditions. Methods: Ambient sensors were installed in the homes of 25 participants with multiple chronic health conditions. Motion, light, temperature, and door usage data were continuously collected from participants' homes. Descriptions of health events and participants' associated behaviors were captured via weekly nursing telehealth visits with study participants and used to analyze sensor data representing health events. Two cases of participants with congestive heart failure exacerbations, one case of urinary tract infection, two cases of bowel inflammation flares, and four cases of participants with sleep interruption were explored. Results: For each case, clinically relevant health events aligned with changes from baseline in behavior data patterns derived from sensors installed in the participant's home. In some cases, the detected event was precipitated by additional behavior patterns that could be used to predict the event. Conclusions: We found evidence in this case series that continuous sensor-based monitoring of patient behavior in home settings may be used to provide automated detection of health events. Nursing insights into smart home sensor data could be used to initiate preventive strategies and provide timely intervention. Tweetable abstract: Nurses partnered with smart homes could detect exacerbations of health conditions at home leading to early intervention.
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In the history of humanity, we have never faced an invisible enemy like the COVID-19 pandemic. The medical field has never been overwhelmed so much. Scientists and engineers are working around the clock to develop a suitable vaccine for COVID-19. In order to fight this pandemic technology will need to be utilized to its full potential. A fourth industrial revolution (4IR) technology, called the Internet of things (IOT), is an interconnection of physical devices and the Internet, has been identified to fight the battle against COVID-19. Numerous papers have been written about the Internet of things technology and how the technology can be use in different environments, thereof it is imperative to review how this technology can be utilize in the the response to COVID-19 pandemic. In this paper, we examined literature on the coronavirus (SARS-COV-2) and IOT technologies that can fight COVID-19 to minimize its spread. Various challenges and open issues related to the used of the technology in the fight of COVID-19 where identified and discussed, such as security and privacy issue, limited spectrum and bandwidth, scalability and interoperability as a threat. This technology can be recommended for use in a pandemic period. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.