ABSTRACT
Smart cities are concepts much loved by politicians and technologists but are very difficult to bring about in practice. There are many isolated applications in cities such as operating streetlamps, but very few, if any, examples of integrated applications sharing data and managing the city as a holistic entity rather than a set of disparate and unconnected applications. This is despite hundreds of trials and indicates how difficult bringing about a smart city will be. The key challenge is the wide range of interested parties in a city including the elected city authority, subcontractors and suppliers to the authority, emergency services, transport providers, businesses, residents, workers, tourists, and other visitors. Some of these entities will be primarily driven by finance, such as businesses and transport providers. Some will be driven by political considerations. Some will be concerned with the quality of life as well as financial costs. In some cases, there will be conflicting interests-the city may want as much information as possible on people in the city, whereas individuals may want privacy and the minimum data stored concerning their movements and attributes. COVID-19 does not change any of these issues, but it does increase the importance of some applications such as smart health, logistics, people surveillance, data security, and crisis management, while reducing the importance of others such as traffic management. It may result in more willingness for monitoring and data sharing if this can be shown to result in better control of the virus. © 2023 the authors.
ABSTRACT
Ubiquitous environments are not fixed in time. Entities are constantly evolving;they are dynamic. Ubiquitous applications therefore have a strong need to adapt during their execution and react to the context changes, and developing ubiquitous applications is still complex. The use of the separation of needs and model-driven engineering present the promising solutions adopted in this approach to resolve this complexity. The authors thought that the best way to improve efficiency was to make these models intelligent. That's why they decided to propose an architecture combining machine learning with the domain of modeling. In this article, a novel tool is proposed for the design of ubiquitous applications, associated with a graphical modeling editor with a drag-drop palette, which will allow to instantiate in a graphical way in order to obtain platform independent model, which will be transformed into platform specific model using Acceleo language. The validity of the proposed framework has been demonstrated via a case study of COVID-19. © 2023 IGI Global. All rights reserved.
ABSTRACT
With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient's activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.
Subject(s)
COVID-19 , Heart Failure , Internet of Things , Humans , Artificial Intelligence , Internet , Heart Failure/diagnosisABSTRACT
Background: The advancement of biomedical research generates myriad healthcare-relevant data, including medical records and medical device maintenance information. The COVID-19 pandemic significantly affects the global mortality rate, creating an enormous demand for medical devices. As information technology has advanced, the concept of intelligent healthcare has steadily gained prominence. Smart healthcare utilises a new generation of information technologies, such as the Internet of Things (loT), big data, cloud computing, and artificial intelligence, to completely transform the traditional medical system. With the intention of presenting the concept of smart healthcare, a predictive model is proposed to predict medical device failure for intelligent management of healthcare services. Methods: Present healthcare device management can be improved by proposing a predictive machine learning model that prognosticates the tendency of medical device failures toward smart healthcare. The predictive model is developed based on 8,294 critical medical devices from 44 different types of equipment extracted from 15 healthcare facilities in Malaysia. The model classifies the device into three classes; (i) class 1, where the device is unlikely to fail within the first 3 years of purchase, (ii) class 2, where the device is likely to fail within 3 years from purchase date, and (iii) class 3 where the device is likely to fail more than 3 years after purchase. The goal is to establish a precise maintenance schedule and reduce maintenance and resource costs based on the time to the first failure event. A machine learning and deep learning technique were compared, and the best robust model for smart healthcare was proposed. Results: This study compares five algorithms in machine learning and three optimizers in deep learning techniques. The best optimized predictive model is based on ensemble classifier and SGDM optimizer, respectively. An ensemble classifier model produces 77.90%, 87.60%, and 75.39% for accuracy, specificity, and precision compared to 70.30%, 83.71%, and 67.15% for deep learning models. The ensemble classifier model improves to 79.50%, 88.36%, and 77.43% for accuracy, specificity, and precision after significant features are identified. The result concludes although machine learning has better accuracy than deep learning, more training time is required, which is 11.49 min instead of 1 min 5 s when deep learning is applied. The model accuracy shall be improved by introducing unstructured data from maintenance notes and is considered the author's future work because dealing with text data is time-consuming. The proposed model has proven to improve the devices' maintenance strategy with a Malaysian Ringgit (MYR) cost reduction of approximately MYR 326,330.88 per year. Therefore, the maintenance cost would drastically decrease if this smart predictive model is included in the healthcare management system.
ABSTRACT
IoT-based smart healthcare system allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the smart healthcare system generates enormous data which contains sensitive personal information. To protect patients’privacy, we propose a secure blockchain-assisted access control scheme for smart healthcare system in fog computing. All the operations of users are recorded on the blockchain by smart contract in order to ensure transparency and reliability of the system. We present a blockchain-assisted Multi-Authority Attribute-Based Encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the blockchain-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient. IEEE
ABSTRACT
Consider the most important lessons learned from the global achievements and disappointments of the previous year. It was a year filled with pandemics that exacerbated massive geopolitical, social, and economic shocks on a worldwide scale, bringing out the worst and best in people. However, the past two years have demonstrated the fragility of global institutions in numerous industries, including medicine, hospitality, travel, and commerce. It also reflects the resilience of the international system with the introduction of various vaccinations and concentrated worldwide efforts against pandemic threats. Conventional and cutting-edge technology approaches are needed to attack COVID-19 and put the situation under control. This paper's primary purpose is to systematically study trends in technology solutions for smart healthcare systems - for example, artificial intelligence (AI) and big data (BD) analytics, which will help save the world. These AI solutions facilitate innovative administrations, adaptability, productivity, and efficiency by developing related frameworks. Specifically, this study identifies AI and Big Data contributions that should be incorporated into smart healthcare systems. It also studies the application of big data analytics and AI to offer users insights and help them to plan and presents models for intelligent healthcare systems based on AI and big data analytics. © 2023 IEEE.
ABSTRACT
The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.
ABSTRACT
Provisioning of health services such as care, monitoring, and remote surgery is being improved thanks to fifth-generation cellular technology (5G). As 5G expands globally, more smart healthcare applications have been developed due to its extensive eMBB (Enhanced Mobile Broadband) and URLLC (Ultra-Reliable Low Latency Communications) features that can be used to generate healthcare systems that allow minimizing the face-to-face assistance of patients at hospital centers. This powerful network provides high transmission speeds, ultra-low latency, and a network capacity greater than that of 4G. Fifth-generation cellular technology is expected to be a means to provide excellent quality of medical care, through its technological provision to the use of IoMT (Internet of Medical Things) devices. Due to the numerous contributions in research on this topic, it is necessary to develop a review that provides an orderly perspective on research trends and niches for researchers to use as a starting point for their work. In this context, this article presents a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), with article selection based on inclusion and exclusion criteria that avoid bias. This research was based on research questions that were answered from the included works. These questions focus on technical characteristics, health benefits, and security protocols necessary for the development of smart healthcare applications. We have identified that a high percentage of existing works in the literature are proposals (56.81%, n = 25) and theoretical studies (22.73%, n = 10);few implementations (15.91%, n = 7) and prototypes (4.55%, n = 2) exist, due to the limited global deployment of 5G. However, the panorama looks promising based on proposals and future work that these technological systems allow, all based on improving healthcare for people.
ABSTRACT
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.
ABSTRACT
Physical fitness is the prime priority of people these days as everyone wants to see himself as healthy. There are numbers of wearable devices available that help human to monitor their vital body signs through which one can get an average idea of their health. Advancements in the efficiency of healthcare systems have fueled the research and development of high-performance wearable devices. There is significant potential for portable healthcare systems to lower healthcare costs and provide continuous health monitoring of critical patients from remote locations. The most pressing need in this field is developing a safe, effective, and trustworthy medical device that can be used to reliably monitor vital signs from various human organs or the environment within or outside the body through flexible sensors. Still, the patient should be able to go about their normal day while sporting a wearable or implanted medical device. This article highlights the current scenario of wearable devices and sensors for healthcare applications. Specifically, it focuses on some widely used commercially available wearable devices for continuously gauging patient's vital parameters and discusses the major factors influencing the surge in the demand for medical devices. Furthermore, this paper addresses the challenges and countermeasures of wearable devices in smart healthcare technology. © 2023 IEEE.
ABSTRACT
The COVID-19 pandemic has affected people all over the world. As a result, smart healthcare services in developed countries have been increasingly growing. However, implementing such healthcare systems in developing countries is difficult due to the high level of solutions required, such as delicate infrastructure and internet of things (IoT) technology with sensors. More medical personnel are also required in developed countries with limited resources, such as Thailand. This chapter discusses the implementation of standard sensors and actuators as part of Smart Healthcare in the post-COVID-19 environment. It is a real-world case of Smart Healthcare delivery both before and after the COVID-19 pandemic. Smart healthcare aids in collecting diagnostic records, the organization of patient lines, and monitoring patient well-being. The most important part of every day is screening patients before they meet with physicians. The risks posed to clinical professionals would be reduced as a result of smart healthcare infrastructure. Most hospitals require significant investment in prefabricated screening equipment, so this study provided for a smart healthcare system: (1) scanning using sensor technologies in combination with current screening devices;(2) adding data to the hospital information systems database;(3) gathering medical records and the pharmacy automation system;(4) notifying the patient of the next appointment, and (5) delivering medical guidance and patient follow-up after meeting with the patient. © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
Since the outbreak of the novel coronavirus disease 2019 (COVID-19), the epidemic has gradually slowed down in various countries and people's lives have gradually returned to normal. To monitor the spread of the epidemic, studies discussing the design of related healthcare information systems have been increasing recently. However, these studies might not consider the aspect of user-centric design when developing healthcare information systems. This study examined these innovative technology applications and rapidly built prototype systems for smart healthcare through a systematic literature review and a study of patient innovation. The design guidelines for the Smart Healthcare System (SHS) were then compiled through an expert review process. This will provide a reference for future research and similar healthcare information system development.
ABSTRACT
In recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia. First, a pre-processing method based on a Gaussian filter and logarithmic operator is applied to input chest X-ray (CXR) images to improve the poor-quality images by enhancing the contrast, reducing the noise, and smoothing the image. Second, robust features are extracted from each enhanced chest X-ray image using a Convolutional Neural Network (CNNs) transformer and an optimal collection of grey-level co-occurrence matrices (GLCM) that contain features such as contrast, correlation, entropy, and energy. Finally, based on extracted features from input images, a random forest machine learning classifier is used to classify images into three classes, such as COVID-19, pneumonia, or normal. The predicted output from the model is combined with Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation for diagnosis. (3) Results: Our work is evaluated using public datasets with three different train-test splits (70-30%, 80-20%, and 90-10%) and achieved an average accuracy, F1 score, recall, and precision of 97%, 96%, 96%, and 96%, respectively. A comparative study shows that our proposed method outperforms existing and similar work. The proposed approach can be utilised to screen COVID-19-infected patients effectively. (4) Conclusions: A comparative study with the existing methods is also performed. For performance evaluation, metrics such as accuracy, sensitivity, and F1-measure are calculated. The performance of the proposed method is better than that of the existing methodologies, and it can thus be used for the effective diagnosis of the disease.
ABSTRACT
The provision of physical healthcare services during the isolation phase is one of the major challenges associated with the current COVID-19 pandemic. Smart healthcare services face a major challenge in the form of human behavior, which is based on human activities, complex patterns, and subjective nature. Although the advancement in portable sensors and artificial intelligence has led to unobtrusive activity recognition systems, very few studies deal with behavior tracking for addressing the problem of variability and behavior dynamics. In this regard, we propose the fusion of PRocess mining and Paravector Tensor (PROMPT)-based physical health monitoring framework that not only tracks subjective human behavior, but also deals with the intensity variations associated with inertial measurement units. Our experimental analysis of a publicly available dataset shows that the proposed method achieves 14.56% better accuracy in comparison to existing works. We also propose a generalized framework for healthcare applications using wearable sensors and the PROMPT method for its triage with physical health monitoring systems in the real world. © 2001-2012 IEEE.
ABSTRACT
This work presents the development of an individual protection device, a smart and active ventilated face shield that can also incorporate an air filtering system. The device can work with the front shield or just with the ventilation structure. The system was produced by additive manufacturing technology, based on a chassis that incorporates an electronic control unit, a rechargeable battery, a fan, and a humidity/temperature sensor. The performed system tests showed that the forced ventilation system prevents fogging even in the most adverse situations and increases user comfort when it is used simultaneously with an individual protective face mask, due to the air flow generated by the integrated fan. The filtered ventilated air guarantees the user's safety. Results also show that, with or without the front visor, the equipment prevents fogging, both on the face shield and glasses for users who wear them. The forced air flow promotes isolation of the breathing zone, decreasing the contact with potentially contaminated aerosols, thus reducing the risk of contagion. © 2022 IEEE.
ABSTRACT
The healthcare system had been on high vigilant due to the sudden outbreak of the Covid-19 pandemic. This stimulated the need for a smart healthcare system for early diagnosis, prevention from spreading, treatment, and simplified living. Remote monitoring, telemedicine, and body sensors are certain areas where the adaptation of IoMT has proved to be a boon. The advancement in the technology for healthcare devices and their security has been challenging too. The sensor implanted, employed in remote healthcare are vulnerable to attacks, as well as, is resource constraint when used in an IoT environment. Privacy is the biggest issue with IoT, as all linked devices transmit data in real time. This paper proposes, the framework for utilizing blockchain model-based parameters that will be distributed in nature and prevent malicious attacks that may threaten healthcare applications. We also discuss the various vulnerabilities on IoMT devices and methods that are employed for authentication and secure access to medical records for IoMT healthcare management. © 2022 IEEE.
ABSTRACT
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. [ FROM AUTHOR]
ABSTRACT
On the outbreak of the global COVID-19 pandemic, high-risk and vulnerable groups in the population were at particular risk of severe disease progression. Pregnant women were one of these groups. The infectious disease endangered not only the physical health of pregnant women, but also their mental well-being. Improving the mental health of pregnant women and reducing their risk of an infectious disease could be achieved by using remote home monitoring solutions. These would allow the health of the mother and fetus to be monitored from the comfort of their home, a reduction in the number of physical visits to the doctor and thereby eliminate the need for the mother to venture into high-risk public places. The most commonly used technique in clinical practice, cardiotocography, suffers from low specificity and requires skilled personnel for the examination. For that and due to the intermittent and active nature of its measurements, it is inappropriate for continuous home monitoring. The pandemic has demonstrated that the future lies in accurate remote monitoring and it is therefore vital to search for an option for fetal monitoring based on state-of-the-art technology that would provide a safe, accurate, and reliable information regarding fetal and maternal health state. In this paper, we thus provide a technical and critical review of the latest literature and on this topic to provide the readers the insights to the applications and future directions in fetal monitoring. We extensively discuss the remaining challenges and obstacles in future research and in developing the fetal monitoring in the new era of Fetal monitoring 4.0, based on the pillars of Healthcare 4.0.
Subject(s)
COVID-19 , Pandemics , Pregnancy , Female , Humans , Pandemics/prevention & control , COVID-19/epidemiology , COVID-19/prevention & control , Fetal Monitoring , Cardiotocography/methods , Prenatal CareABSTRACT
China announced the Healthy China Initiative (2019-2030) in 2019, an action program aimed to support the country's current long-term health policy, Healthy China 2030, which focuses on public health promotion and health awareness. Following the implementation of the policy, China had the COVID-19 pandemic, which had an influence on both the public's degree of health awareness and the adoption of the HCI. This research examines whether the COVID-19 epidemic has increased public understanding and acceptance of China's long-term health policy. In addition, it analyzes whether the Chinese public's awareness of health policy has been impacted by China's usage of smart healthcare in its response to the pandemic. To correspond to these study aims, we used a questionnaire based on the research questions and recent relevant research. The results of the study, based on an examination of 2488 data, demonstrate that the Healthy China Initiative is still poorly understood. More than 70% of respondents were unfamiliar with it. However, the results imply that respondents are becoming more aware of smart healthcare and that public acceptance of official health policies can be aided by the sharing of knowledge about this. As a result, we examine the situation and draw the conclusion that the spread of cutting-edge health-related technology can enhance the communication of health policy and provide participants and policymakers with fresh insights. Finally, this study also can provide lessons for other countries in the early stages of policy dissemination, particularly health policy advocacy and promotion during epidemics.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Pandemics , China/epidemiology , Health Policy , Health PromotionABSTRACT
This work presents the development of an individual protection device, a smart and active ventilated face shield that can also incorporate an air filtering system. The device can work with the front shield or just with the ventilation structure. The system was produced by additive manufacturing technology, based on a chassis that incorporates an electronic control unit, a rechargeable battery, a fan, and a humidity/temperature sensor. The performed system tests showed that the forced ventilation system prevents fogging even in the most adverse situations and increases user comfort when it is used simultaneously with an individual protective face mask, due to the air flow generated by the integrated fan. The filtered ventilated air guarantees the user's safety. Results also show that, with or without the front visor, the equipment prevents fogging, both on the face shield and glasses for users who wear them. The forced air flow promotes isolation of the breathing zone, decreasing the contact with potentially contaminated aerosols, thus reducing the risk of contagion. © 2022 IEEE.