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1.
Comput Intell Neurosci ; 2021: 8016525, 2021.
Article in English | MEDLINE | ID: covidwho-1598096

ABSTRACT

Smart health surveillance technology has attracted wide attention between patients and professionals or specialists to provide early detection of critical abnormal situations without the need to be in direct contact with the patient. This paper presents a secure smart monitoring portable multivital signal system based on Internet-of-Things (IoT) technology. The implemented system is designed to measure the key health parameters: heart rate (HR), blood oxygen saturation (SpO2), and body temperature, simultaneously. The captured physiological signals are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before sending them to the cloud. An ESP8266 integrated unit is used for processing, encryption, and providing connectivity to the cloud over Wi-Fi. On the other side, trusted medical organization servers receive and decrypt the measurements and display the values on the monitoring dashboard for the authorized specialists. The proposed system measurements are compared with a number of commercial medical devices. Results demonstrate that the measurements of the proposed system are within the 95% confidence interval. Moreover, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Error (MRE) for the proposed system are calculated as 1.44, 1.12, and 0.012, respectively, for HR, 1.13, 0.92, and 0.009, respectively, for SpO2, and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.


Subject(s)
Cloud Computing , Internet of Things , Communication , Humans , Reproducibility of Results
2.
Contrast Media Mol Imaging ; 2021: 3257035, 2021.
Article in English | MEDLINE | ID: covidwho-1495706

ABSTRACT

The pandemic of COVID-19 is continuing to wreak havoc in 2021, with at least 170 million victims around the world. Healthcare systems are overwhelmed by the large-scale virus infection. Luckily, Internet of Things (IoT) is one of the most effective paradigms in the intelligent world, in which the technology of artificial intelligence (AI), like cloud computing and big data analysis, is playing a vital role in preventing the spread of the pandemic of COVID-19. AI and 5G technologies are advancing by leaps and bounds, further strengthening the intelligence and connectivity of IoT applications, and conventional IoT has been gradually upgraded to be more powerful AI + IoT (AIoT). For example, in terms of remote screening and diagnosis of COVID-19 patients, AI technology based on machine learning and deep learning has recently upgraded medical equipment significantly and has reshaped the workflow with minimal contact with patients, so medical specialists can make clinical decisions more efficiently, providing the best protection not only to patients but also to specialists themselves. This paper reviews the latest progress made in combating COVID-19 with both IoT and AI and also provides comprehensive details on how to combat the pandemic of COVID-19 as well as the technologies that may be applied in the future.


Subject(s)
Artificial Intelligence , COVID-19/prevention & control , Delivery of Health Care/standards , Internet of Things/statistics & numerical data , Machine Learning , SARS-CoV-2/isolation & purification , COVID-19/virology , Humans
3.
Sensors (Basel) ; 21(21)2021 Oct 27.
Article in English | MEDLINE | ID: covidwho-1488702

ABSTRACT

The COVID-19 pandemic has significantly threatened the health and well-being of humanity. Contact tracing (CT) as an important non-pharmaceutical intervention is essential to containing the spread of such an infectious disease. However, current CT solutions are fragmented with limited use of sensing and computing technologies in a scalable framework. These issues can be well addressed with the use of the Internet of Things (IoT) technologies. Therefore, we need to overview the principle, motivation, and architecture for a generic IoT-based CT system (IoT-CTS). A novel architecture for IoT-CTS solutions is proposed with the consideration of peer-to-peer and object-to-peer contact events, as well as the discussion on key topics, such as an overview of applicable sensors for CT needs arising from the COVID-19 transmission methods. The proposed IoT-CTS architecture aims to holistically utilize essential sensing mechanisms with the analysis of widely adopted privacy-preserving techniques. With the use of generic peer-to-peer and object-to-peer sensors based on proximity and environment sensing mechanisms, the infectious cases with self-directed strategies can be effectively reduced. Some open research directions are presented in the end.


Subject(s)
COVID-19 , Internet of Things , Contact Tracing , Humans , Pandemics , SARS-CoV-2
4.
Sensors (Basel) ; 20(21)2020 Oct 22.
Article in English | MEDLINE | ID: covidwho-1450862

ABSTRACT

Empowered by the ubiquitous sensing capabilities of Internet of Things (IoT) technologies, smart communities could benefit our daily life in many aspects. Various smart community studies and practices have been conducted, especially in China thanks to the government's support. However, most intelligent systems are designed and built individually by different manufacturers in diverging platforms with different functionalities. Therefore, multiple individual systems must be deployed in a smart community to have a set of functions, which could lead to hardware waste, high energy consumption and high deployment cost. More importantly, current smart community systems mainly focus on the technologies involved, while the effects of human activity are neglected. In this paper, a fourth-order tensor model representing object, time, location and human activity is proposed for human-centered smart communities, based on which a unified smart community system is designed. Thanks to the powerful data management abilities of a high-order tensor, multiple functions can be integrated into our system. In addition, since the tensor model embeds human activity information, complex functions could be implemented by exploring the effects of human activity. Two exemplary applications are presented to demonstrate the flexibility of the proposed unified fourth-order tensor-based smart community system.


Subject(s)
Computers , Technology , China , Environment Design , Human Activities , Humans , Internet of Things
5.
Biosens Bioelectron ; 195: 113655, 2022 Jan 01.
Article in English | MEDLINE | ID: covidwho-1432989

ABSTRACT

Rapid and accurate testing tools for SARS-CoV-2 detection are urgently needed to prevent the spreading of the virus and to take timely governmental actions. Internet of things (IoT)-based diagnostic devices would be an ideal platform for point-of-care (POC) screening of COVID-19 and ubiquitous healthcare monitoring for patients. Herein, we present an advanced IoT-based POC device for real-time direct reverse-transcription-loop mediated isothermal amplification assay to detect SARS-CoV-2. The diagnostic system is miniaturized (10 cm [height] × 9 cm [width] × 5.5 cm [length]) and lightweight (320 g), which can be operated with a portable battery and a smartphone. Once a liquid sample was loaded into an integrated microfluidic chip, a series of sample lysis, nucleic amplification, and real-time monitoring of the fluorescent signals of amplicons were automatically performed. Four reaction chambers were patterned on the chip, targeting As1e, N, E genes and a negative control, so multiple genes of SARS-CoV-2 could be simultaneously analyzed. The fluorescence intensities in each chamber were measured by a CMOS camera upon excitation with a 488 nm LED light source. The recorded data were processed by a microprocessor inside the IoT-based POC device and transferred and displayed on the wirelessly connected smartphone in real-time. The positive results could be obtained using three primer sets of SARS-CoV-2 with a limit of detection of 2 × 101 genome copies/µL, and the clinical sample of SARS-CoV-2 was successfully analyzed with high sensitivity and accuracy. Our platform could provide an advanced molecular diagnostic tool to test SARS-CoV-2 anytime and anywhere.


Subject(s)
Biosensing Techniques , COVID-19 , Internet of Things , Humans , Molecular Diagnostic Techniques , Nucleic Acid Amplification Techniques , Point-of-Care Systems , RNA, Viral , SARS-CoV-2 , Sensitivity and Specificity
6.
Biomed Res Int ; 2021: 5546790, 2021.
Article in English | MEDLINE | ID: covidwho-1405239

ABSTRACT

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control/methods , Machine Learning , Algorithms , Artificial Intelligence , COVID-19/prevention & control , COVID-19/transmission , Cities/epidemiology , Contact Tracing/methods , Delivery of Health Care , Humans , Internet of Things , Pandemics , SARS-CoV-2/pathogenicity
7.
J Healthc Eng ; 2021: 7358874, 2021.
Article in English | MEDLINE | ID: covidwho-1404512

ABSTRACT

The 2019-2020 coronavirus pandemic had far-reaching consequences beyond the spread of the disease and efforts to cure it. Today, it is obvious that the pandemic devastated key sectors ranging from health to economy, culture, and education. As far as education is concerned, one direct result of the spread of the pandemic was the resort to suspending traditional in-person classroom courses and relying on remote learning and homeschooling instead, by exploiting e-learning technologies, but many challenges are faced by these technologies. Most of these challenges are centered around the efficiency of these delivery methods, interactivity, and knowledge testing. These issues raise the need to develop an advanced smart educational system that assists home-schooled students, provides teachers with a range of smart new tools, and enable a dynamic and interactive e-learning experience. Technologies like the Internet of things (IoT) and artificial intelligence (AI), including cognitive models and context-awareness, can be a driving force in the future of e-learning, opening many opportunities to overcome the limitation of the existing remote learning systems and provide an efficient reliable augmented learning experience. Furthermore, virtual reality (VR) and augmented reality (AR), introduced in education as a way for asynchronous learning, can be a second driving force of future synchronous learning. The teacher and students can see each other in a virtual class even if they are geographically spread in a city, a country, or the globe. The main goal of this work is to design and provide a model supporting intelligent teaching assisting and engaging e-learning activity. This paper presents a new model, ViRICTA, an intelligent system, proposing an end-to-end solution with a stack technology integrating the Internet of things and artificial intelligence. The designed system aims to enable a valuable learning experience, providing an efficient, interactive, and proactive context-aware learning smart services.


Subject(s)
Artificial Intelligence , Computer-Assisted Instruction , Internet of Things , COVID-19 , Cognition , Humans , Pandemics
8.
Int J Environ Res Public Health ; 18(17)2021 08 27.
Article in English | MEDLINE | ID: covidwho-1374402

ABSTRACT

Nowadays people are mostly focused on their work while ignoring their health which in turn is creating a drastic effect on their health in the long run. Remote health monitoring through telemedicine can help people discover potential health threats in time. In the COVID-19 pandemic, remote health monitoring can help obtain and analyze biomedical signals including human body temperature without direct body contact. This technique is of great significance to achieve safe and efficient health monitoring in the COVID-19 pandemic. Existing remote biomedical signal monitoring methods cannot effectively analyze the time series data. This paper designs a remote biomedical signal monitoring framework combining the Internet of Things (IoT), 5G communication and artificial intelligence techniques. In the constructed framework, IoT devices are used to collect biomedical signals at the perception layer. Subsequently, the biomedical signals are transmitted through the 5G network to the cloud server where the GRU-AE deep learning model is deployed. It is noteworthy that the proposed GRU-AE model can analyze multi-dimensional biomedical signals in time series. Finally, this paper conducts a 24-week monitoring experiment for 2000 subjects of different ages to obtain real data. Compared with the traditional biomedical signal monitoring method based on the AutoEncoder model, the GRU-AE model has better performance. The research has an important role in promoting the development of biomedical signal monitoring techniques, which can be effectively applied to some kinds of remote health monitoring scenario.


Subject(s)
COVID-19 , Internet of Things , Artificial Intelligence , Humans , Pandemics , SARS-CoV-2
9.
Int J Environ Res Public Health ; 18(16)2021 Aug 13.
Article in English | MEDLINE | ID: covidwho-1354973

ABSTRACT

The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.


Subject(s)
COVID-19 , Internet of Things , Artificial Intelligence , Big Data , Data Mining , Humans , Machine Learning , Pandemics , SARS-CoV-2
10.
Sensors (Basel) ; 21(16)2021 Aug 06.
Article in English | MEDLINE | ID: covidwho-1348686

ABSTRACT

This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019-2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Internet of Things , Air Pollutants/analysis , Air Pollution/analysis , Communicable Disease Control , Environmental Monitoring , Humans , Reproducibility of Results , SARS-CoV-2
11.
Sensors (Basel) ; 21(15)2021 Jul 24.
Article in English | MEDLINE | ID: covidwho-1325761

ABSTRACT

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.


Subject(s)
COVID-19 , Internet of Things , Humans , Lung/diagnostic imaging , Radiography , SARS-CoV-2 , Supervised Machine Learning
12.
Sensors (Basel) ; 21(7)2021 Mar 26.
Article in English | MEDLINE | ID: covidwho-1308415

ABSTRACT

We live in complex times in the health, social, political, and energy spheres, and we must be aware of and implement new trends in intelligent social health systems powered by the Internet of Things (IoT). Sustainable development, energy efficiency, and public health are interrelated parameters that can transform a system or an environment for the benefit of people and the planet. The integration of sensors and smart devices should promote energy efficiency and ensure that sustainable development goals are met. This work is carried out according to a mixed approach, with a literature review and an analysis of the impact of the Sustainable Development Goals on the applications of the Internet of Things and smart systems. In the analysis of results, the following questions are answered about these systems and applications: (a) Are IoT applications key to the improvement of people's health and the environment? (b) Are there research and case studies implemented in cities or territories that demonstrate the effectiveness of IoT applications and their benefits to public health?


Subject(s)
Internet of Things , Sustainable Development , Cities , Delivery of Health Care , Goals , Humans
13.
Comput Methods Programs Biomed ; 208: 106231, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1271604

ABSTRACT

BACKGROUND AND OBJECTIVES: The Internet of Things (IoT) paradigm has been extensively applied to several sectors in the last years, ranging from industry to smart cities. In the health domain, IoT makes possible new scenarios of healthcare delivery as well as collecting and processing health data in real time from sensors in order to make informed decisions. However, this domain is complex and presents several technological challenges. Despite the extensive literature about this topic, the application of IoT in healthcare scarcely covers requirements of this sector. METHODS: A literature review from January 2010 to February 2021 was performed resulting in 12,108 articles. After filtering by title, abstract, and content, 86 were eligible and examined according to three requirement themes: data lifecycle; trust, security, and privacy; and human-related issues. RESULTS: The analysis of the reviewed literature shows that most approaches consider IoT application in healthcare merely as in any other domain (industry, smart cities…), with no regard of the specific requirements of this domain. CONCLUSIONS: Future efforts in this matter should be aligned with the specific requirements and needs of the health domain, so that exploiting the capabilities of the IoT paradigm may represent a meaningful step forward in the application of this technology in healthcare.


Subject(s)
Internet of Things , Delivery of Health Care , Forecasting , Humans , Internet , Privacy , Technology
14.
Sensors (Basel) ; 21(12)2021 Jun 14.
Article in English | MEDLINE | ID: covidwho-1282571

ABSTRACT

The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.


Subject(s)
Blockchain , Internet of Things , Algorithms , Delivery of Health Care , Humans
15.
J Healthc Eng ; 2021: 3277988, 2021.
Article in English | MEDLINE | ID: covidwho-1277006

ABSTRACT

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.


Subject(s)
Artificial Intelligence , COVID-19 Testing , COVID-19/diagnosis , Internet of Things , SARS-CoV-2 , Brazil , China , Computer Simulation , Computer Systems , Databases, Factual , Deep Learning , Diagnosis, Computer-Assisted , Humans , Pattern Recognition, Automated , Radiography, Thoracic , United States , X-Rays
16.
J Healthc Eng ; 2021: 9999504, 2021.
Article in English | MEDLINE | ID: covidwho-1262424

ABSTRACT

Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications. The current study has discussed the role of the aforesaid technology in transforming healthcare services. The study also presented various functionalities of the ideal IoT-aided robotic systems and their importance in healthcare applications. Furthermore, the study focused on the application of the IoT and robotics in providing healthcare services such as rehabilitation, assistive surgery, elderly care, and prosthetics. Recent developments, current status, limitations, and challenges in the aforesaid area have been presented in detail. The study also discusses the role and applications of the aforementioned technology in managing the current pandemic of COVID-19. A comprehensive knowledge has been provided on the prospect of the functionality, application, challenges, and future scope of the IoT-aided robotic system in healthcare services. This will help the future researcher to make an inclusive idea on the use of the said technology in improving the healthcare services in the future.


Subject(s)
Delivery of Health Care , Internet of Things , Medical Informatics Applications , Robotics , Humans , Monitoring, Physiologic , Remote Sensing Technology , Telemedicine
17.
Sensors (Basel) ; 21(11)2021 Jun 01.
Article in English | MEDLINE | ID: covidwho-1259573

ABSTRACT

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic's potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.


Subject(s)
COVID-19 , Internet of Things , Cities , Delivery of Health Care , Humans , SARS-CoV-2
18.
Psychiatriki ; 32(2): 99-102, 2021 Jul 10.
Article in Greek, English | MEDLINE | ID: covidwho-1248520

ABSTRACT

The idea of a network of small devices that would be able to connect each other, appeared in the early 80s. In a prophetic article, Mark Weiser,1 described such a connection, that it is now known under the term of Internet of Things (IoT). In a broadest sense, the term IoT encompasses everything connected to the internet, but it is increasingly being used to define objects that "talk" to each other, creating a network from simple sensors to smartphones and wearables connected. During the recent years this network of communicating devices has been combined with other technological achievements, and particularly with the Virtual Reality (VR)2 and the Artificial Intelligence (AI).3 The emerge of COVID-19 pandemic in 2019, resulted to the poor response and healthcare failures of many countries globally.4 One of the main reasons for such a failure, was the inability of accurate data collection from different sources. Apparently, it was the first time, humanity realized the need for massive amounts of heterogeneous data to be collected, interpreted, and shared. Amid the ongoing COVID-19 pandemic, several innovators and public authorities are looking to leverage IoT tools to reduce the burden on the healthcare systems.5 Mental health is one of the areas that seems to benefit the most of such technologies. A significant decrease of the total amount of ER visits and a dramatic increase of internet access from the patients and care givers along to the development of applications for mental health issues, followed the outbreak of SARS-CoV-2.6 Such technologies proved to be efficient to help mentally ill patients and pioneer the path in the future. Probably the most obvious use of these emerged technologies is the improvement of the telehealth options. Patients who suffer from mental illness face significant problems towards the continuity of care during the crisis.7 Nonetheless, they usually have other health problems, that deprive them from an equitable health care provision. Improved telehealth platforms can give them a single point access to address all their problems. The use of electronic health records can reduce the fragmentary health services and improve the outcome.8 However, this is only the beginning. The COVID-19 crisis and the subsequent social isolation, to reduce both the contamination and the spread of the disease, highlighted the necessity for providing accurate and secure diagnoses and treatments from a safe distance. Virtual reality combined with IoT and AI technologies seem to be a reliable alternative to the classic physical and mental examination and treatment in many areas of mental and neurological diseases.2 These novel techniques can spot the early signs and detect mental illnesses with high accuracy. However, caution and more work are required to bridge the space between these recently thrived technologies and mental health care.7 It is worth mentioning, that internet-oriented health care procedures can also help to reduce the gaps caused by the stigma of mental illness. For example, the development of AI chatbots (an application used to chat directly with a human) can alleviate the fears of judgment of the help seeking persons and provide the professionals with a supplemental support toward improved services to their patients.9 A final remark for conclusion. Humanity is more and more depended to the "intelligent" machines. However, we must not forget that we humans are responsible to set the rules of such co-existence.


Subject(s)
COVID-19 , Health Information Systems , Health Services Accessibility , Mental Health/trends , Social Interaction , Telemedicine/methods , Artificial Intelligence , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control/methods , Health Information Systems/organization & administration , Health Information Systems/standards , Health Information Systems/trends , Health Services Accessibility/standards , Health Services Accessibility/trends , Humans , Internet of Things , Needs Assessment , SARS-CoV-2 , Virtual Reality
19.
Sensors (Basel) ; 21(9)2021 Apr 26.
Article in English | MEDLINE | ID: covidwho-1238946

ABSTRACT

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices' security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.


Subject(s)
Internet of Things , Cities , Computer Security , Confidentiality , Delivery of Health Care , Humans
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