ABSTRACT
The COVID-19 pandemic has affected the entire world by causing widespread panic and disrupting normal life. Since the outbreak began in December 2019, the virus has killed thousands of people and infected millions more. Hospitals are struggling to keep up with large patient flows. In some situations, hospitals are lacking enough beds and ventilators to accommodate all of their patients or are running low on supplies such as masks and gloves. Predicting intensive care unit (ICU) admission of patients with COVID-19 could help clinicians better allocate scarce ICU resources. In this study, many machine and deep learning algorithms are tested over predicting ICU admission of patients with COVID-19. Most of the algorithms we studied are extremely accurate toward this goal. With the convolutional neural network (CNN), we reach the highest results on our metrics (90.09% accuracy and 93.08% ROC-AUC), which demonstrates the usability of these learning models to identify patients who are likely to require ICU admission and assist hospitals in optimizing their resource management and allocation during the COVID-19 pandemic or others. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The Covid-19 pandemic that hit us in 2020 changed our lifestyle in every way. There was tremendous damage to people's lives. It is now predicted that other variants of Coronavirus are affecting people's health throughout the world. We must remain vigilant against upcoming dangers. The Indian health ministry has also advised people to take the necessary precautions. In this paper, we will focus on automating temperature and oxygen monitoring using the Internet of Things. According to our proposed model, data generated by the temperature sensor (MLX90614) and oxygen saturation sensor (MAX30102) will be stored in a relational database. Using this data, future data analyses can be conducted. We are also going to visualize the data by building an interactive dashboard using Power BI. Overall, health monitoring will become much more convenient and speedier. © 2023 IEEE.
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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.
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Microbial pathogens have threatened the world due to their pathogenicity and ability to spread in communities. The conventional laboratory-based diagnostics of microbes such as bacteria and viruses need bulky expensive experimental instruments and skilled personnel which limits their usage in resource-limited settings. The biosensors-based point-of-care (POC) diagnostics have shown huge potential to detect microbial pathogens in a faster, cost-effective, and user-friendly manner. The use of various transducers such as electrochemical and optical along with microfluidic integrated biosensors further enhances the sensitivity and selectivity of detection. Additionally, microfluidic-based biosensors offer the advantages of multiplexed detection of analyte and the ability to deal with nanoliters volume of fluid in an integrated portable platform. In the present review, we discussed the design and fabrication of POCT devices for the detection of microbial pathogens which include bacteria, viruses, fungi, and parasites. The electrochemical techniques and current advances in this field in terms of integrated electrochemical platforms that include mainly microfluidic- based approaches and smartphone and Internet-of-things (IoT) and Internet-of-Medical-Things (IoMT) integrated systems have been highlighted. Further, the availability of commercial biosensors for the detection of microbial pathogens will be briefed. In the end, the challenges while fabrication of POC biosensors and expected future advances in the field of biosensing have been discussed. The integrated biosensor-based platforms with the IoT/IoMT usually collect the data to track the community spread of infectious diseases which would be beneficial in terms of better preparedness for current and futuristic pandemics and is expected to prevent social and economic losses.
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The Internet of Medical Things (IoMT) system plays a role in various areas of social activity, including healthcare. Telemetry of cardiovascular function, such as blood pressure and pulse, in daily life is useful in the treatment of cardiovascular disease and stress management. However, until now, brain function monitoring technology has not been installed in the IoMT system.In this study, we used near-infrared spectroscopy (NIRS) installed in the IoMT system to evaluate whether consumers who are not medical experts can measure their own brain function correctly. In addition, the IoMT system was used to assess the long-term effects of physical exercise on physical and mental health.We studied a total of 119 healthy adults recruited from a fitness gym in Koriyama, Japan. After receiving instruction in the usage of the IoMT monitoring system including NIRS, the subjects monitored their physical and mental conditions by themselves when they visited the gym. We evaluated the relations between blood pressure (BP), pulse rate (PR), body weight (BW) and age. In addition, we evaluated the left/right asymmetry of the prefrontal cortex (PFC) at rest and BP. We calculated the laterality index at rest (LIR) for assessment of left/right asymmetry of PFC activity; a positive LIR (>0) indicates right-dominant PFC activity associated with higher stress responses, while a negative LIR (<0) indicates left-dominant PFC activity associated with lower stress responses. We studied 47 out of 119 cases who monitored their physiological conditions before and after physical exercise for 6 months for this study.The results showed that the systolic blood pressure and mean blood pressure (p < 0.05) were significantly reduced after the physical exercise for 6 months; body weight did not change significantly (p > 0.05). In addition, NIRS demonstrated that LIR changed to plus values from minus values after exercise (p < 0.01).These results show that (1) consumers who are not-medical experts can measure their own brain function correctly using NIRS; (2) after long-term physical exercise, systemic blood pressure decreased, associated with modulation of PFC activity (i.e., from right-dominant PFC activity to left-dominant activity), indicating that long-term physical exercises caused relaxation in the brain and the autonomic nervous system.
Subject(s)
Prefrontal Cortex , Spectroscopy, Near-Infrared , Adult , Humans , Spectroscopy, Near-Infrared/methods , Prefrontal Cortex/physiology , Functional Laterality/physiology , Exercise Therapy , Arrhythmias, Cardiac , Body WeightABSTRACT
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.
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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.
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The year 2020 witnessed a major shift in our society and the global economy due to the onset of COVID-19. Many newer trends are expected to surface as people grow more digitally savvy and embrace technology while working from home. This has also impacted the medical industry worldwide and has made healthcare preventive, predictive, and personalized. In healthcare, the Internet of Things (IoT) refers to a network of connected medical devices that can generate, collect, and store data as well as connect to a network, analyze data, and transmit data of various types such as medical images, physiological and vital body signatures, and genomics data. Real-time monitoring, improved diagnostics, robotic surgical interventions, and other medical IoT applications can all help improve outcomes in healthcare. Medical IoT refers to IoT devices and applications tailored to healthcare demands and environments. It includes sensors and apps for monitoring healthcare remotely, telemedicine consultation, and delivery. Medical IoT also uses AI and machine learning to assist life-transforming advancements in existent medical devices, such as the smart inhaler for asthma sufferers. IoT devices offer a lot of new opportunities for patient monitoring, both by the doctors and by the patients themselves. This is made possible by a variety of wearable IoT devices that promise an array of benefits but also pose challenges for all stakeholders in the healthcare industry. Medical IoT devices enable the collection of patient data in real-time, which is processed and evaluated thereafter. The information gathered is centralized for computing, processing, and storage. Centralization can be hazardous as it is vulnerable to multiple threats: failure at one point, mistrust, manipulation, tampering of data, and privacy evasion. Blockchain can address such critical issues by offering decentralized computation and storage for IoT data. COVID-19 brought out the benefits of technology and has reinforced the need to develop and secure more advanced applications including Medical IoT. We have advanced much, but there is a huge scope to explore, expand, and establish. © 2022 Nova Science Publishers, Inc. All rights reserved.
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Recently, innovations in the Internet-of-Medical- Things (IoMT), information and communication technologies, and Machine Learning (ML) have enabled smart healthcare. Pooling medical data into a centralised storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated Learning (FL) overcomes the prior problems with a centralised aggregator server and a shared global model. However, there are two technical challenges: FL members need to be motivated to contribute their time and effort, and the centralised FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralised fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasise three main research streams based on a systematic analysis of blockchain-empowered (i) IoMT, (ii) Electronic Health Records (EHR) and Electronic Medical Records (EMR) management, and (iii) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications. IEEE
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The global spread of COVID-19 highlights the urgency of quickly finding drugs and vaccines and suggests that similar challenges will arise in the future. This underscores the need for ongoing efforts to overcome the obstacles involved in the development of potential treatments. Although some progress has been made in the use of Artificial Intelligence (AI) in drug discovery, virologists, pharmaceutical companies, and investors seek more long-term solutions and greater investment in emerging technologies. One potential solution to aid in the drug-development process is to combine the capabilities of the Internet of Medical Things (IoMT), edge computing (EC), and deep learning (DL). Some practical frameworks and techniques utilizing EC, IoMT, and DL have been proposed for the monitoring and tracking of infected individuals or high-risk areas. However, these technologies have not been widely utilized in drug clinical trials. Given the time-consuming nature of traditional drug- and vaccine-development methods, there is a need for a new AI-based platform that can revolutionize the industry. One approach involves utilizing smartphones equipped with medical sensors to collect and transmit real-time physiological and healthcare information on clinical-trial participants to the nearest edge nodes (EN). This allows the verification of a vast amount of medical data for a large number of individuals in a short time frame, without the restrictions of latency, bandwidth, or security constraints. The collected information can be monitored by physicians and researchers to assess a vaccine's performance.
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Internet of Medical Things is one of the fastest growing fields in technology, and it is predicted to bring about the largest technological delivery ever. Medical systems have been pushed to improve online services across the board by the COVID-19 pandemic, as the edge-enabled Online learning system in the healthcare system offers incredible opportunities. With the introduction of cutting-edge digital health technologies, patients will be able to avoid unneeded, preventative hospitalizations. Furthermore, the utilization of adaptable and readily available media by both patients and medical professionals would allow telehealth-based medical platforms to make healthcare more efficient and economical. © 2022 IEEE.
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Managing sensory data captured in leveraging is a challenge, especially during a pandemic when trying to capture the psychological, emotional, and physiology standards. The advanced technology of edge computing and IIoMT together help to reach promising outcome results from the home environment using psychological feelings and somatic health equivalent data. The basic application of Deep Learning leads to the asset-constraint of edge computing, which provides a way to move the data that is collected from IIoMT devices to various locations. All kinds of data related to health can exist in a particular place of user edge while assuring the security, privacy, and low latency of the inference system. In this article, an Internet of Medical system is developed that uses Deep Learning to detect risky types of health-related symptoms and generates reports and alerts for pandemic and epidemic situations, which helps in decision-making support. In these pandemic and epidemic situations, a lot of applications have been identified and implemented with their descriptions for the upcoming support for the real-time trials. We have developed smart applications in edge computing manuals. The overall output clearly allows us to view the fixed smart systems during the pandemic with the Smart Health Management system (SHMs). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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Detecting the baby's cry sounds is significant and is the first step that enables effective diagnosis in the branch of pediatrics. Despite the complexity in the analysis of the baby's cry signal, an automated cry signal segmentation system can be introduced for the diagnosis of earache, colic pain, cold, diaper rashes, or due to hunger. This is a challenging task as this type of automated cry sound segmentation algorithm is dependent on the wavelet coefficients extracted from the cry signal. These coefficients are the inputs to train the cry signal-oriented diagnostic system. A completely computerized segmentation algorithm is designed to extract the details and approximation coefficients of the cry signal during the expiration and inspiration process. These coefficients are used to train the convolutional neural networks (CNN). The prime focus of this work is to devise a smartphone-based app that will record the baby's cry signal, segment it using the wavelet transform, and classify them using CNN based on the diagnosis made to identify the earache, colic pain, cold, diaper rashes, fever, respiratory problem or hunger. This indigenous smartphone app will enable the young mothers to identify the problem existing with their infants and facilitate an easy nurturing of the newborn. This non-contact type of diagnosis finds a lot of importance in the present scenario, where the COVID-19 social distancing is followed enabling the physician, infant, and mother to be devoid of the fear of this pandemic situation. The main objective of this proposal is to design a cry signal based infant diagnostic system which focuses on scrutinizing the neonatal pathologies by extracting the features present in the signal of the baby's cry in a realistic clinical environment. This mobile app once developed, will be a part of the internet of medical things © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
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.
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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.
Subject(s)
COVID-19 , Internet of Things , Child , Humans , Pandemics/prevention & control , Internet , Hospitals, PediatricABSTRACT
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.
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The growth of the "Internet of Medical Things (IoMT)” allows for the collection and processing of data in healthcare systems. At the same time, it is challenging to study the requirements of public health prevention. Here, mask-wearing is considered an efficient preventive measure for avoiding virus transfer. Hence, it is necessary to implement an automated mask identification model to prevent public epidemics. The main scope of the proposed method is to design a face mask detection model with IoT using a "Single Shot Multi-box Detector (SSD)” and a hybrid deep learning method. The novelty of the proposed model is that the enhancement made in the face detection and face classification with the developed ASMFO by optimizing the parameters like the threshold in SSD, steps per execution in ResNet, and learning rate in MobileNet, which makes it more efficient and to perform better the conventional models. Here, the parameter optimization is carried out using a hybrid optimization algorithm named Adaptive Sailfish Moth Flame Optimization (ASMFO). Then, the detected face images are given to the hybrid approach named Hybrid ResMobileNet (HResMobileNet)-based classification, where the parameters are tuned using the same ASMFO algorithm for achieving accurate mask detection results. However, the suggested mask identification model with IoT based on three standard datasets is compared with the conventional meta-heuristic algorithms and existing classifiers with various measures. Thus, the experimental analysis is conducted to analyze the effectiveness of the proposed framework over different meta-heuristic algorithms and existing classifiers. The implemented ASMFO-HResMobileNet provides 18.57%, 15.67%, 17.56%, 16.24%, and 19.2% elevated accuracy than SVM, CNN, VGG16-LSTM, ResNet 50, MobileNetv2, and ResNet 50-MobileNetv2. © 2022 Elsevier B.V.
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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.
Subject(s)
COVID-19 , Internet of Things , Humans , Artificial Intelligence , COVID-19/diagnosis , Technology , InternetABSTRACT
Background: The Internet of Medical Things (IoMT) is now being connected to medical equipment to make patients more comfortable, offer better and more affordable health care options, and make it easier for people to get good care in the comfort of their own homes. Objective(s): The primary purpose of this study is to highlight the architecture and use of IoMT (Internet of Medical Things) technology in the healthcare system. Method(s): Several sources were used to acquire the material, including review articles published in various journals that had keywords such as, Internet of Medical Things, Wireless Fidelity, Remote Healthcare Monitoring (RHM), Point-of-care testing (POCT), and Sensors. Result(s): IoMT has succeeded in lowering both the cost of digital healthcare systems and the amount of energy they use. Sensors are used to measure a wide range of things, from physiological to emotional responses. They can be used to predict illness before it happens. Conclusion(s): The term "Internet of Medical Things" refers to the broad adoption of healthcare solutions that may be provided in the home. Making such systems intelligent and efficient for timely prediction of important illnesses has the potential to save millions of lives while decreasing the burden on conventional healthcare institutions, such as hospitals. patients and physicians may now access real-time data due to advancements in IoM. Copyright © 2022 Wal et al.
ABSTRACT
The Internet of Medical Things (IoMT) is an extended version of the Internet of Things (IoT). It mainly concentrates on the integration of medical things for servicing needy people who cannot get medical services easily, especially rural area people and aged peoples living alone. The main objective of this work is to design a real time interactive system for providing medical services to the needy who do not have a sufficient medical infrastructure. With the help of this system, people will get medical services at their end with minimal medical infrastructure and less treatment cost. However, the designed system could be upgraded to address the family of SARs viruses, and for experimentation, we have taken COVID-19 as a test case. The proposed system comprises of many modules, such as the user interface, analytics, cloud, etc. The proposed user interface is designed for interactive data collection. At the initial stage, it collects preliminary medical information, such as the pulse oxygen rate and RT-PCR results. With the help of a pulse oximeter, they could get the pulse oxygen level. With the help of swap test kit, they could find COVID-19 positivity. That information is uploaded as preliminary information to the designed proposed system via the designed UI. If the system identifies the COVID positivity, it requests that the person upload X-ray/CT images for ranking the severity of the disease. The system is designed for multi-model data. Hence, it can deal with X-ray, CT images, and textual data (RT-PCR results). Once X-ray/CT images are collected via the designed UI, those images are forwarded to the designed AI module for analytics. The proposed AI system is designed for multi-disease classification. It classifies the patients affected with COVID-19 or pneumonia or any other viral infection. It also measures the intensity level of lung infection for providing suitable treatment to the patients. Numerous deep convolution neural network (DCNN) architectures are available for medical image classification. We used ResNet-50, ResNet-100, ResNet-101, VGG 16, and VGG 19 for better classification. From the experimentation, it observed that ResNet101 and VGG 19 outperform, with an accuracy of 97% for CT images. ResNet101 outperforms with an accuracy of 98% for X-ray images. For obtaining enhanced accuracy, we used a major voting classifier. It combines all the classifiers result and presents the majority voted one. It results in reduced classifier bias. Finally, the proposed system presents an automatic test summary report textually. It can be accessed via user-friendly graphical user interface (GUI). It results in a reduced report generation time and individual bias.