Federated learning based Covid-19 detection.

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

Redefining Industry 5.0 in Ophthalmology and Digital Metrology: A Global Perspective

The demand for ophthalmic diagnosis and monitoring equipment is high due to day-by-day increasing eye-related diseases. These diseases are growing very fast due to changes in lifestyle, the aging crowd, and chronic diseases. During COVID-19, old ophthalmic diagnostic devices failed to fulfill the patients' needs due to social distancing and took more diagnosis time, making patients uncomfortable and unsatisfied to visit the clinic. Seeing all these problems during the COVID-19 time, patients are demanding personalized healthcare services and clinical home services to protect themselves from the COVID-19 virus attack. To fulfill the mass personalized needs and easily accesses clinical services from the patient's home, there is a requirement to embrace Industry 5.0 with its emerging digital technologies. The current work is based on the theoretical view of Industry 5.0 in ophthalmology and its supporting digital technology, various models and challenges faced by the healthcare system in ophthalmology during the COVID-19 pandemic time, limitations of the study, and its future scope, digital metrology, and strength, limitation, opportunities, and threat analysis in brief. © 2023, Metrology Society of India.

Heart Rate Evaluation by Smartphone: An Overview

In this paper, an overview of the smartphone measurement methods for Heart Rate (HR) and Heart Rate Variability (HRV) is presented. HR and HRV are important vital signs to be evaluated and monitored especially in a sudden heart crisis and in the case of COVID-19. Unlike other specific medical devices, the smartphone can always be present with a person, and it is equipped with sensors that can be used to estimate or acquire such vital signs. Furthermore, their computation and connection capabilities make them suitable for Internet of Things applications. Although in the literature many interesting solutions for evaluating HR and HRV are proposed, often a lack in the analysis of the measurement uncertainty, the description of the measurement procedure for their validation, and the use of a common gold standard for testing all of them is highlighted. The lack of standardization in experimental protocol, processing methodology, and validation procedures, impacts the comparability of results and their general validity. To stimulate the research activities to fill this gap, the paper gives an analysis of the most recent literature together with a logical classification of the measurement methods by highlighting their main advantages and disadvantages from a metrological point of view together with the description of the measurement methods and instruments proposed by authors for their validation. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Heart Rate Evaluation by Smartphone: An Overview

In this paper, an overview of the smartphone measurement methods for Heart Rate (HR) and Heart Rate Variability (HRV) is presented. HR and HRV are important vital signs to be evaluated and monitored especially in a sudden heart crisis and in the case of COVID-19. Unlike other specific medical devices, the smartphone can always be present with a person, and it is equipped with sensors that can be used to estimate or acquire such vital signs. Furthermore, their computation and connection capabilities make them suitable for Internet of Things applications. Although in the literature many interesting solutions for evaluating HR and HRV are proposed, often a lack in the analysis of the measurement uncertainty, the description of the measurement procedure for their validation, and the use of a common gold standard for testing all of them is highlighted. The lack of standardization in experimental protocol, processing methodology, and validation procedures, impacts the comparability of results and their general validity. To stimulate the research activities to fill this gap, the paper gives an analysis of the most recent literature together with a logical classification of the measurement methods by highlighting their main advantages and disadvantages from a metrological point of view together with the description of the measurement methods and instruments proposed by authors for their validation. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Designing Digital COVID-19 Screening: Insights and Deliberations.

Due to the global COVID-19 pandemic, public health control and screening measures have been introduced at healthcare facilities, including those housing our most vulnerable populations. These warning measures situated at hospital entrances are presently labour-intensive, requiring additional staff to conduct manual temperature checks and risk-assessment questionnaires of every individual entering the premises. To make this process more efficient, we present eGate, a digital COVID-19 health-screening smart Internet of Things system deployed at multiple entry points around a children's hospital. This paper reports on design insights based on the experiences of concierge screening staff stationed alongside the eGate system. Our work contributes towards social-technical deliberations on how to improve design and deploy of digital health-screening systems in hospitals. It specifically outlines a series of design recommendations for future health screening interventions, key considerations relevant to digital screening control systems and their implementation, and the plausible effects on the staff who work alongside them.

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects.

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen-antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.

IoMT Enabled Prototype Edge Computing Healthcare System for Isolated Patients

Continuous patient care and the use of multiple medical machines are two challenges facing today's healthcare sector in terms of patient's healthcare. During the pandemic situation, many people isolated in their home, such as covid-19 positive patients, elderly people living away from their families, bedridden patients, etc., need regular health checks and controls, but during this pandemic is lacking. Recent advances in the Internet of Medical Things (IoMT) has been able to give good results in collecting health data of patients at home environment. Deep learning (DL) applications can able to run on edge nodes, it locally processes, computes and analyzes data from IOMT devices to make inferences on patient health information. This ensures the privacy and security of the patient's physiological information and also and allows patient health information to remain at the patient's side. Send all this information to healthcare professionals and relatives of patients. This framework will provide safety for isolated patients and a health support systemas a whole. © 2022 IEEE.

Applications of cognitive internet of medical things in modern healthcare.

The sudden outbreak of the novel coronavirus disease in 2019, known as COVID-19 has impacted the entire globe and has forced governments of various countries to a partial or full lockdown in the fear of the rapid spread of this disease. The major lesson learned from this pandemic is that there is a need to implement a robust system by using non-pharmaceutical interventions for the prevention and control of new contagious viruses. This goal can be achieved using the platform of the Internet of Things (IoT) because of its seamless connectivity and ubiquitous sensing ability. This technology-enabled healthcare sector is helpful to monitor COVID-19 patients properly by adopting an interconnected network. IoT is useful for improving patient satisfaction by reducing the rate of readmission in the hospital. The presented work discusses the applications and technologies of IoT like smart and wearable devices, drones, and robots which are used in healthcare systems to tackle the Coronavirus pandemic This paper focuses on applications of cognitive radio-based IoT for medical applications, which is referred to as "Cognitive Internet of Medical Things" (CIoMT). CIoMT is a disruptive and promising technology for dynamic monitoring, tracking, rapid diagnosis, and control of pandemics and to stop the spread of the virus. This paper explores the role of the CIoMT in the health domain, especially during pandemics, and also discusses the associated challenges and research directions.

Preventing COVID-19 Using Edge Intelligence in Internet of Medical Things

Internet of Medical Things (IoMT) is a smart interwoven technology enabled by the advancements made in multi-disciplined fields of medical devices, networking technologies, healthcare applications and artificial intelligence. The current spread of the coronavirus disease (COVID-19) globally has thrown innumerable challenges against human survival. To overcome this pandemic situation, an innovative healthcare solution is vital for saving human lives and mitigating the viral spread. We propose an E-Health+ system that can provide remote patient assistance anytime, anywhere. E-Health+ makes use of artificial intelligence in edge nodes for data processing coupled with Federated learning for swift prognostic medical advice for connected patients during their critical times in IoMT. The medical advice or assistance provided is based on the requests arising in a real-time basis with minimal response times, thereby reducing latency and also the much-needed privacy preservation towards the sensitive patient data.

Factors of adoption of Artificial Intelligence (AI) and Internet of Medical Things (IOMT) amongst Healthcare Workers: A Descriptive Analysis

The technologies like Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) have revolutionized the healthcare system. The Covid-19 pandemic has been a major force behind this revolutionary technology usage. This study finds the challenges in the adoption of this new knowledge i.e. in the adoption of AI and IOMT by the healthcare workforce based on self-designed questionnaires having questions on an interval scale to identify the challenges in the adoption of these technologies. This research was conducted from July 2020 to April 2021, taking a sample of 350 healthcare workers inclusive of doctors and paramedical staff however only three hundred respondents filled the questionnaire. Different challenges have been found for adopting AI and IoMT via an absence of a regulatory Framework;unexplained return on investment in absence of research with low funding, and Huge Data and Operational Mediocrity with all-time processing requirements are challenges for the adoption of these technologies. The finding of the study suggests better equipped and technologically aware healthcare workers for the betterment of services, especially in tough times like the Covid-19 pandemic. © 2022, International Journal of Systematic Innovation. All Rights Reserved.

Smart Vital Signs Monitoring System for Covid-19 Patients in Quarantine

A healthcare monitoring system based on the Internet of Things has been designed to monitor the vital signs of patients with COVID-19 while they are isolated at home. The complete system is designed using an embedded controller, medical sensors, a mobile application, and a cloud server. This Internet of Medical Things (IoMT) device is used to evaluate COVID-19 patients' critical levels during home isolation by monitoring their heart rate, body temperature, oxygen saturation (SpO2), cough intensity counts and geographic position. This approach enables physicians to assess COVID-19 patients without the need for direct contact, thereby minimizing the risk of infection. An embedded hardware device with internet connectivity collects and displays vital signs of COVID-19 patients and transmits the data to an IoT platform. The cloud layer and smartphone application store COVID-19 patient records via the API interface. Additionally, the Hospital Management System (HMS) is utilized to manage physician appointments and the prescriptions given to patients. Mobile applications and email notifications are sent to physicians and patients' families in case of an emergency, allowing them to respond quickly. The users can also view their vital signs and an alert message on the OLED screen. © 2022 IEEE.

Scalable Federated-Learning and Internet-of-Things enabled architecture for Chest Computer Tomography image classification

The recent proliferation of the Internet of Medical Things (IoMT), Federated Learning (FL), and Deep learning have opened new dimensions of research across the globe. This paper proposes the combined use of these paradigms to detect COVID-19 in Computer Tomography (CT) images. Initially, the framework collects the CT images at the various local hospital using IoMT and aggregated them in an Hadoop Distributed File system (HDFS) Spark big data framework for storage. Later, the proposed framework performs the model training in isolation with the trained parameters being sent to a centralized server for aggregation using federated Learning. The comprehensive experimentation is performed on three different COVID-19 databases to test the efficacy of the proposed work. The numerical investigation revealed that the proposed work outperforms existing techniques by a good margin. Also, the global server, when compared to the local server, achieves a 7.57% performance improvement in terms of accuracy and 3.33% in terms of Area Under Curve (AUC).

Resource Aware Fog Based Remote Health Monitoring System

In today's world of medical science, remote patient monitoring devices are becoming more important and a future need particularly in the present COVID-19 situation as individuals are preferred to be kept isolated. Patients would be benefited from a suitable monitoring system that measures their important medical parameters such as pulse rate, oxygen saturation or SpO2, body temperature, blood pressure, and Galvanized Skin Response (GSR). This system can increase the medical staff efficiency by drastically decreasing their duties in hospitals and the need to attend to them individually. Patients in their home isolation may utilize the device as well, and their vital indicators may be checked by doctors remotely. In this work, we are prototyping a powerefficient, wearable medical kit and a resource-aware fog network set up to handle the Internet of Things (IoT) data traffic. The idea behind the design is to process the critical medical sensors' data in the fog nodes which are deployed at the edge of the network. The data thus received, is used for a machine learning-based solution for personal health anomalies and COVID-19 infection risk analysis. © 2022 IEEE.

The evolving roles and impacts of 5G enabled technologies in healthcare: The world epidemic COVID-19 issues.

The latest 5G technology is being introduced the Internet of Things (IoT) Era. The study aims to focus the 5G technology and the current healthcare challenges as well as to highlight 5G based solutions that can handle the COVID-19 issues in different arenas. This paper provides a comprehensive review of 5G technology with the integration of other digital technologies (like AI and machine learning, IoT objects, big data analytics, cloud computing, robotic technology, and other digital platforms) in emerging healthcare applications. From the literature, it is clear that the promising aspects of 5G (such as super-high speed, high throughput, low latency) have a prospect in healthcare advancement. Now healthcare is being adopted 5G-based technologies to aid improved health services, more effective medical research, enhanced quality of life, better experiences of medical professionals and patients in anywhere-anytime. This paper emphasizes the evolving roles of 5G technology for handling the epidemiological challenges. The study also discusses various technological challenges and prospective for developing 5G powered healthcare solutions. Further works will incorporate more studies on how to expand 5G-based digital society as well as to resolve the issues of safety-security-privacy and availability-accessibility-integrity in future health crises.

IoMT-fog-cloud based architecture for Covid-19 detection.

Limitations of available literature: Nowadays, coronavirus disease 2019 (COVID-19) is the world-wide pandemic due to its mutation over time. Several works done for covid-19 detection using different techniques however, the use of small datasets and the lack of validation tests still limit their works. Also, they depend only on the increasing the accuracy and the precision of the model without giving attention to their complexity which is one of the main conditions in the healthcare application. Moreover, the majority of healthcare applications with cloud computing use centralization transmission process of various and vast volumes of information what make the privacy and security of personal patient's data easy for hacking. Furthermore, the traditional architecture of the cloud showed many weaknesses such as the latency and the low persistent performance. Method proposed by the author with technical information: In our system, we used Discrete Wavelet transform (DWT) and Principal Component Analysis (PCA) and different energy tracking methods such as Teager Kaiser Energy Operator (TKEO), Shannon Wavelet Entropy Energy (SWEE), Log Energy Entropy (LEE) for preprocessing the dataset. For the first step, DWT used to decompose the image into coefficients where each coefficient is vector of features. Then, we apply PCA for reduction the dimension by choosing the most essential features in features map. Moreover, we used TKEO, SHEE, LEE to track the energy in the features in order to select the best and the most optimal features to reduce the complexity of the model. Also, we used CNN model that contains convolution and pooling layers due to its efficacity in image processing. Furthermore, we depend on deep neurons using small kernel windows which provide better features learning and minimize the model's complexity.The used DWT-PCA technique with TKEO filtering technique showed great results in terms of noise measure where the Peak Signal-to-Noise Ratio (PSNR) was 3.14 dB and the Signal-to-Noise Ratio (SNR) of original and preprocessed image was 1.48, 1.47 respectively which guaranteed the performance of the filtering techniques.The experimental results of the CNN model ensure the high performance of the proposed system in classifying the covid-19, pneumonia and normal cases with 97% of accuracy, 100% of precession, 97% of recall, 99% of F1-score, and 98% of AUC. Advantages and application of proposed method: The use of DWT-PCA and TKEO optimize the selection of the optimal features and reduce the complexity of the model.The proposed system achieves good results in identifying covid-19, pneumonia and normal cases.The implementation of fog computing as an intermediate layer to solve the latency problem and computational cost which improve the Quality of Service (QoS) of the cloud.Fog computing ensure the privacy and security of the patients' data.With further refinement and validation, the IFC-Covid system will be real-time and effective application for covid-19 detection, which is user friendly and costless.

A Novel Internet of Medical Things Model to Progress COVID-19 Testing

The COVID-19 pandemic has devastated the public health infrastructure of the globe. The crucial strategy has been to carry out aggressive testing, which could be the only way to get back to normalcy. The COVID-19 testing is carried out through the Reverse Transcriptase Polymerase Chain Reaction (RT-PCR), which is considered to be the gold standard. However, these tests are sometimes known to provide inaccurate results which might be due to improper sample storage and transportation techniques. The swab samples transported in a viral transport medium need to be maintained under optimum environmental conditions. The proposed model involves tagging humidity and thermologger devices with the sample container box. This would record the real-time temperature and humidity, which would be stored in the cloud server. This would predict any breakage in the cold chain using AI-powered pattern analysis techniques. This would intimate the authorities of a possible cold chain breakage, thereby assuring the quality of the samples. This would drastically reduce the possibility of false outcomes. This would help the healthcare workers to trace, isolate and treat the right affected individuals, preventing the further spread of the disease. This model could also be used for distribution of COVID-19 vaccines whenever they are available. This could preserve the potency of the vaccines, thereby significantly reducing the wastage of vaccines. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Federated Transfer Learning-Based IDS for the Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) is a set of medical devices and applications that connect to healthcare systems through the Internet. Those devices are equipped with communication technologies that allow them to communicate with each other and the Internet. Reliance on the IoMT is increasing with the increase in epidemics and chronic diseases such as COVID-19 and diabetes;with the increase in the number of IoMT users and the need for electronic data sharing and virtual services, cyberattacks in the healthcare sector for accessing confidential patient data has been increasing in the recent years. The healthcare applications and their infrastructures have special requirements for handling sensitive users' data and the need for high availability. Therefore, securing healthcare applications and data has attracted special attention from both industry and researchers. In this paper, we propose a Federated Transfer Learning-based Intrusion Detection System (IDS) to secure the patient's healthcare-connected devices. The model uses Deep Neural Network (DNN) algorithm for training the network and transferring the knowledge from the connected edge models to build an aggregated global model and customizing it for each one of the connected edge devices without exposing data privacy. CICIDS2017 dataset has been used to evaluate the performance in terms of accuracy, detection rate, and average training time. In addition to preserving data privacy of edge devices and achieving better performance, our comparison indicates that the proposed model can be generalized better and learns incrementally compared to other baseline ML/DL algorithms used in the traditional centralized learning schemes. © 2021 IEEE.

Real-Time Internet of Medical Things System for Detecting Blood Leakage during Hemodialysis Using a Novel Multiple Concentric Ring Sensor.

Venous needle dislodgement (VND) is a major healthcare safety concern in patients undergoing hemodialysis. Although VND is uncommon, it can be life-threatening. The main objective of this study was to implement a real-time multi-bed monitoring system for VND by combining a novel leakage-detection device and IoMT (Internet of Medical Things) technology. The core of the system, the Acusense IoMT platform, consisted of a novel leakage-detection patch comprised of multiple concentric rings to detect blood leakage and quantify the leaked volume. The performance of the leakage-detection system was evaluated on a prosthetic arm and clinical study. Patients with a high risk of blood leakage were recruited as candidates. The system was set up in a hospital, and the subjects were monitored for 2 months. During the pre-clinical simulation experiment, the system could detect blood leakage volumes from 0.3 to 0.9 mL. During the test of the IoMT system, the overall success rate of tests was 100%, with no lost data packets. A total of 701 dialysis sessions were analyzed, and the accuracy and sensitivity were 99.7% and 90.9%, respectively. Evaluation questionnaires showed that the use of the system after training changed attitudes and reduced worry of the nursing staff. Our results show the feasibility of using a novel detector combined with an IoMT system to automatically monitor multi-bed blood leakage. The innovative concentric-circle design could more precisely control the warning blood-leakage threshold in any direction to achieve clinical cost-effectiveness. The system reduced the load on medical staff and improved patient safety. In the future, it could also be applied to home hemodialysis for telemedicine during the era of COVID-19.

Challenges of Internet of Medical Things for Electronic Healthcare

The internet of medical things is one of the greatest marvels of the 21st century. Research indicates that after the covid-19 pandemic IoMT has gained a lot of popularity due to its demand in E-health and more particularly in telehealth and telemedicine. However, all the existing IoMT initiatives are at their early stage of development and require a more advanced approach within their domain. More significant, concerning the use of IoT in both the software and hardware arena, the lack of knowledge and experience to manufacture IoMT devices is observed. Thus, the health system is aware that there are substantial challenges to implementing IoMT software and hardware. In this paper, we aim to provide a high-level review of existing IoMT data interoperability, product design, product's market adoption, data challenges. Also, we are providing practical suggestions through implementing semi-automated systems using cloud computing, and artificial intelligence via digital health platforms. Knowing these provided high-level suggestions will enhance the process of IoMT production and provide better and more reliable healthcare and remote monitoring system. © 2021 IEEE.