Remote Monitoring System of Patient Status in Social IoT Environments Using Amazon Web Services Technologies and Smart Health Care

BackgroundWith the rapid development of information and communication technologies, smart homes are being investigated as effective solutions for home health care. The increasing academic attention on smart home health care has primarily been on the development and application of smart home technologies. However, comprehensive studies examining the general landscape of diverse research areas for smart home health care are still lacking.ObjectiveThis study aims to determine the intellectual structure of smart home health care in a time series by conducting a coword analysis and topic analysis. Specifically, it investigates (1) the intellectual basis of smart home health care through overall academic status, (2) the intellectual foci through influential keywords and their evolutions, and (3) intellectual trends through primary topics and their evolutions.MethodsAnalyses were conducted in 5 steps: (1) data retrieval from article databases (Web of Science, Scopus, and PubMed) and the initial dataset preparation of 6080 abstracts from the year 2000 to the first half of 2019; (2) data preprocessing and refinement extraction of 25,563 words; (3) a descriptive analysis of the overall academic status and period division (ie, 4 stages of 3-year blocks); (4) coword analysis based on word co-occurrence networks for the intellectual foci; and (5) topic analysis for the intellectual trends based on latent Dirichlet allocation (LDA) topic modeling, word-topic networks, and researcher workshops.ResultsFirst, regarding the intellectual basis of smart home health care, recent academic interest and predominant journals and research domains were verified. Second, to determine the intellectual foci, primary keywords were identified and classified according to the degree of their centrality values. Third, 5 themes pertaining to the topic evolution emerged: (1) the diversification of smart home health care research topics; (2) the shift from technology-oriented research to technological convergence research; (3) the expansion of application areas and system functionality of smart home health care; (4) the increased focus on system usability, such as service design and experiences; and (5) the recent adaptation of the latest technologies in health care. Based on these findings, the pattern of technology diffusion in smart home health care research was determined as the adaptation of technologies, the proliferation of application areas, and an extension into system design and service experiences.ConclusionsThe research findings provide academic and practical value in 3 aspects. First, they promote a comprehensive understanding of the smart home health care domain by identifying its multifaceted intellectual structure in a time series. Second, they can help clinicians discern the development and dispersion level of their respective disciplines. Third, the pattern of technology diffusion in smart home health care could help scholars comprehend current and future research trends and identify research opportunities based on upcoming research waves of newly adapted technologies in smart home health care.

With the rapid development of mobile Internet technology and applications, more and more people begin to pay attention to and deepen the understanding of wearable devices, especially smart medical and health care devices. At the same time, as the extension of the traditional medical equipment, the market potential of smart medical and health care devices is huge, so it has also been paid more attention and promoted by many related industries and enterprises. However, according to the survey report, it shows that there is lower user adoption rate for the device's usage. In this study, we construct the user acceptance behavior model of smart medical and health care device, and analysis the factors influencing the adoption of smart medical and health care devices on the basis of UTAUT2 (the extended unified theory of acceptance and use of technology). In the study, we will pertinently modify the original UTAUT2 model, and add the two new constructs, including the health concern as the key features of health care equipment and the socio-economic status to carry on the research analysis. It aims to put forward suggestions on the operation and promotion of the future smart medical devices industry through the research in this paper.

In recent years, the use of information technology (IT) in the medical and healthcare sectors has been rising rapidly. Variously called “e-health,” “digital health (care),” “smart health (care),” and so forth, these concepts differ slightly conceptually, but are all having an impact on the health insurance industry. In e-health, described as “a mechanism for collecting information from organizations involved with medical and healthcare information including hospitals, and making it available to individuals and organizations that require it,” (Nikkei Business Online Edition 2010, p. 167) health insurance companies are positioned as one type of organization involved in medical and healthcare information that collect healthcare information from citizens and use healthcare information acquired from other organizations. In digital health, described as “represented by digitalization of healthcare and nursing systems,” (Japan Association of Corporate Executives 2015, p. 1) the expansion of private health insurance in conjunction with a review of the scope of application of health insurance systems is anticipated as a future issue. In addition, regarding smart healthcare, described as “approaches to link systems and services to acquire physiological data from people and process it through a network,” (IoT News) promotion of the penetration of private health insurance products interlocked with individuals’ health can be seen targeted in related Japanese industries (Next-Generation Healthcare Industry Council 2016, p. 16).

The concept of smart health care is rapidly increasing due to the advancement in technology. Smart Health care is defined as diagnosis the disease, improve the quality of patient’s life and enhance the quality of service with the advancement features in Information and Communication Technologies. The technologies used for smart health care are Cloud Computing, Big Data, Internet of Things, Artificial Intelligence and Block Chain. The most acceptable technology for providing quality and enhanced service to the patients is the cloud computing. The impact of cloud computing is expected to be stronger and more positive in forthcoming years. This paper is aims to provide the importance of cloud computing in health care. First, the concept and the role of cloud in smart health are discussed. Then the benefits and the risks of cloud in health care are explained.

Smart health care is an important aspect of connected living. Health care is one of the basic pillars of human need, and smart health care is projected to produce several billion dollars in revenue in the near future. There are several components of smart health care, including the Internet of Things (IoT), the Internet of Medical Things (IoMT), medical sensors, artificial intelligence (AI), edge computing, cloud computing, and next-generation wireless communication technology. Many papers in the literature deal with smart health care or health care in general. Here, we present a comprehensive survey of IoT- and IoMT-based edge-intelligent smart health care, mainly focusing on journal articles published between 2014 and 2020. We survey this literature by answering several research areas on IoT and IoMT, AI, edge and cloud computing, security, and medical signals fusion. We also address current research challenges and offer some future research directions.

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as “Smart Healthcare.” By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the estimation of intrusion detection.

This paper presents the development of an on-line condition monitoring system and a fault diagnosis approach for PEMFCs based on the electrochemical impedance spectroscopy measurement. More specifically, a condition monitoring system for PEMFCs is developed by using the stack impedance at a certain frequency for fault detection, which will provide the health condition of the PEMFC systems. On the basis of the state of health from the condition monitoring system, an EIS-based fault diagnosis approach is proposed for the stack by using the fuzzy logic. The effectivenesses of the proposed condition monitoring system and the fault diagnosis approach are demonstrated through a PEMFC test bench. The experimental results show that the proposed condition monitoring and fault diagnosis system can detect the PEMFC condition on-line and achieve accurate fault diagnosis results.

This paper describes the development of the service-oriented architecture (SOA)-based Remote Condition Monitoring and Fault Diagnosis System in the field of equipments remote monitoring and fault diagnosis. The architecture is based on Web Services, Smart Client, and Extensible Markup Language (XML) technologies etc. More specifically, the paper analyzes the structures and the requirements in equipments remote monitoring and fault diagnosis. Finally, the SOA-based Enterprise Monitoring and Fault Diagnosis System has been developed and realized with Visual Studio 2005, which has been applied to industrial field. It verifies the effectiveness and practicality of the system.

Growth and advancement of the Deep Learning (DL) and the Internet of Things (IoT) are figuring out their way over the modern contemporary world through integrating various technologies in distinct fields viz, agriculture, manufacturing, energy, transportation, supply chains, cities, healthcare, and so on. Researchers had identified the feasibility of integrating deep learning, cloud, and IoT to enhance the overall automation, where IoT may prolong its application area through utilizing cloud services and the cloud can even prolong its applications through data acquired by IoT devices like sensors and deep learning for disease detection and diagnosis. This study explains a summary of various techniques utilized in smart healthcare, i.e., deep learning, cloud-based-IoT applications in smart healthcare, fog computing in smart healthcare, and challenges and issues faced by smart healthcare and it presents a wider scope as it is not intended for a particular application such aspatient monitoring, disease detection, and diagnosing and the technologies used for developing this smart systems are outlined. Smart health bestows the quality of life. Convenient and comfortable living is made possible by the services provided by smart healthcare systems (SHSs). Since healthcare is a massive area with enormous data and a broad spectrum of diseases associated with different organs, immense research can be done to overcome the drawbacks of traditional healthcare methods. Deep learning with IoT can effectively be applied in the healthcare sector to automate the diagnosing and treatment process even in rural areas remotely. Applications may include disease prevention and diagnosis, fitness and patient monitoring, food monitoring, mobile health, telemedicine, emergency systems, assisted living, self-management of chronic diseases, and so on.

The smart healthy elderly care city takes the integration of healthy elderly care resources with information technology as its basic feature, and innovates the healthy elderly care service model to accurately serve the elderly. In recent years, all parts of the country have gradually promoted the construction of smart, healthy and elderly care cities, which have achieved good results, but also face many problems. In order to evaluate the current situation of the construction of smart healthy elderly care cities, the author has built a smart healthy elderly care city evaluation index system based on the research results and standards of smart city and healthy elderly care city evaluation index system. The evaluation index system of smart health care city involves five fields, including smart health care infrastructure, smart health care environment, smart health care economy and smart health care services, which can comprehensively and systematically reflect the construction of smart health care city. Taking Foshan City, Guangdong Province as an example, this paper empirically analyzes the current situation and problems of the construction of a smart healthy elderly care city using the data from 2020-2021, and then puts forward high-quality development strategies for the construction of a smart healthy elderly care city in Foshan.

The growth of IoT is tremendous and is making its presence into nearly every space from industries to health care. The healthcare industry is now getting embraced by technological innovations. IoT is making the promise of smart and connected care a reality. Leading technologies such as Big Data, IoT, advanced analytics, and many other technological modernizations have turned the old-style health care into smart health care. Smart health care can be defined as using mobile and electronic technology for efficient diagnosis of the disease, better-quality treatment of the patients, and improved quality of lives. The healthcare industry is rapidly adopting Internet of things technologies in everything from wearable’s to patient monitoring, in order to improve precision, endorse efficiency, cut costs, and boost health and augment safety. The newest research conducted by industry experts shows how the market for smart healthcare solutions is growing at a tremendous pace. IoT is an enabler to drive better asset utilization; new revenues achieve improved care for patients and reduced costs. In addition, it has the potential to revolutionize how health care is delivered. The new features offered by distributed analytics and edge intelligence, if successfully applied for time-sensitive healthcare applications, have great potential to accelerate the discovery of early notification of emergency situations to support smart decision-making. Smart health care, which monitors users’ living settings and health status using wearable sensing devices and collecting their data over a network under daily life, is expected as a new trend. It is getting more attention along with the increase of demands of preventive care. This chapter gives an overview on the range of applications for the Internet of Things, share examples that illustrate how IoT products and services are being deployed around the globe, by heathcare industry in some areas like image management, visualization, remote health and monitoring, healthcare asset tracking, health monitoring using wearable devices, enhanced drug management, and patient flow analysis. IoT customer case studies help to demonstrate the breadth of possibilities for IoT applications in health care.

8 - Smart healthcare using blockchain technologies: The importance, applications, and challenges

Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.

Smart health-care is undergoing rapid transformation from the conventional specialist and hospital-focused style to a distributed patient-focused manner. Several technological developments have encouraged this rapid revolution of health-care vertical. Currently, 4G and other communication standards are used in health-care for smart health-care services and applications. These technologies are crucial for the evolution of future smart health-care services. With the growth in the health-care industry, several applications are expected to produce a massive amount of data in different format and size. Such immense and diverse data needs special treatment concerning the end-to-end delay, bandwidth, latency and other attributes. It is difficult for current communication technologies to fulfil the requirements of highly dynamic and time-sensitive health care applications of the future. Therefore, the 5G networks are being designed and developed to tackle the diverse communication needs of health-care applications in Internet of Things (IoT). 5G assisted smart health-care networks are an amalgamation of IoT devices that require improved network performance and enhanced cellular coverage. Current connectivity solutions for IoT face challenges, such as the support for a massive number of devices, standardisation, energy-efficiency, device density, and security. In this paper, we present a comprehensive review of 5G assisted smart health-care solutions in IoT. We present a structure for smart health-care in 5G by categorizing and classifying existing literature. We also present key requirements for successful deployment of smart health-care systems for certain scenarios in 5G. Finally, we discuss several open issues and research challenges in 5G smart health-care solutions in IoT.

The Internet-of-Things (IoT) has taken over the business spectrum, and its applications vary widely from agriculture and health care to transportation. A hospital environment can be very stressful, especially for senior citizens and children. With the ever-increasing world population, the conventional patient-doctor appointment has lost its effectiveness. Hence, smart health care becomes very important. Smart health care can be implemented at all levels, starting from temperature monitoring for babies to tracking vital signs in the elderly.