Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

Automatic Detection of Lung Ultrasound Artifacts using a Deep Neural Networks approach

The COVID-19 pandemic has challenged many of the healthcare systems around the world. Many patients who have been hospitalized due to this disease develop lung damage. In low and middle-income countries, people living in rural and remote areas have very limited access to adequate health care. Ultrasound is a safe, portable and accessible alternative;however, it has limitations such as being operator-dependent and requiring a trained professional. The use of lung ultrasound volume sweep imaging is a potential solution for this lack of physicians. In order to support this protocol, image processing together with machine learning is a potential methodology for an automatic lung damage screening system. In this paper we present an automatic detection of lung ultrasound artifacts using a Deep Neural Network, identifying clinical relevant artifacts such as pleural and A-lines contained in the ultrasound examination taken as part of the clinical screening in patients with suspected lung damage. The model achieved encouraging preliminary results such as sensitivity of 94%, specificity of 81%, and accuracy of 89% to identify the presence of A-lines. Finally, the present study could result in an alternative solution for an operator-independent lung damage screening in rural areas, leading to the integration of AI-based technology as a complementary tool for healthcare professionals. © 2023 SPIE.

Applying Machine Learning and Model-Driven Approach for the Identification and Diagnosis of Covid-19

Ubiquitous environments are not fixed in time. Entities are constantly evolving;they are dynamic. Ubiquitous applications therefore have a strong need to adapt during their execution and react to the context changes, and developing ubiquitous applications is still complex. The use of the separation of needs and model-driven engineering present the promising solutions adopted in this approach to resolve this complexity. The authors thought that the best way to improve efficiency was to make these models intelligent. That's why they decided to propose an architecture combining machine learning with the domain of modeling. In this article, a novel tool is proposed for the design of ubiquitous applications, associated with a graphical modeling editor with a drag-drop palette, which will allow to instantiate in a graphical way in order to obtain platform independent model, which will be transformed into platform specific model using Acceleo language. The validity of the proposed framework has been demonstrated via a case study of COVID-19. © 2023 IGI Global. All rights reserved.

IoT Coupled Healthcare Systems: A Bibliometric Analysis

The adoption of the Internet of Things (IoT) has revolutionized the way the health care industry works. IoT en-abled smart and connected solutions like smart sensors, wearable devices, and smart health monitoring systems are used to unleash the potential growth of the health care industry. IoT based health care solutions are on greater priority among IoT service providers since the disruptions caused by the COVID-19. According to experts, there still exist white spots in research studies on the Internet of Things (IoT) and health care Systems. The study conducted in this paper aims to explore emerging global research trends and topical focus in the field of IoT in health care System. Bibliometric analysis is used to analyze the research articles on 'Internet of Things' and 'Health care Systems' extracted from SCOPUS and WoS database using VoS Viewer tool;the analysis used to assess the growth and research trends of different research fields over a period of time. The parameters considered during analysis include year-wise citations, year-wise publications, keyword clustering analysis, author-wise analysis, country-wise research trends and publication trend over the years. The results showcased that there has been significant change in utilization of IoT in healthcare systems continuously during the period under study conducted. © 2022 IEEE.

Framing blockchain-integrated digital twins for emergent healthcare management at local and city levels: A proof of concept

It has been witnessed that digital technology has the potential to improve the efficiency of emergent healthcare management in COVID-19, which however has not been widely adopted due to unclear definition and configuration. This research aims to propose a proof of concept of digital twins for emergent healthcare management through configuring the cyber and functional interdependencies of healthcare systems at local and city levels. Critical interdependencies of healthcare systems have been firstly identified at both levels, then the information and associated cyber and functional interdependencies embedded in seven critical hospital information systems (HISs) have been identified and mapped. The proposed conceptual digital twin-based approach has been then developed for information coordination amongst these critical HISs at both local and city levels based on permissioned blockchain to (1) integrate and manage the information from seven critical HISs, and further (2) predict the demands of medical resources according to patient trajectory. A case study has been finally conducted at three hospitals in London during the COVID-19 period, and the results showed that the developed framework of blockchain-integrated digital twins is a promising way to provide more accurate and timely procurement information to decision-makers and can effectively support evidence-based decisions on medical resource allocation in the pandemic. © 2023 ICE Publishing: All rights reserved.

Should age matter in COVID-19 triage? A deliberative study

The COVID-19 pandemic put a large burden on many healthcare systems, causing fears about resource scarcity and triage. Several COVID-19 guidelines included age as an explicit factor and practices of both triage and 'anticipatory triage' likely limited access to hospital care for elderly patients, especially those in care homes. To ensure the legitimacy of triage guidelines, which affect the public, it is important to engage the public's moral intuitions. Our study aimed to explore general public views in the UK on the role of age, and related factors like frailty and quality of life, in triage during the COVID-19 pandemic. We held online deliberative workshops with members of the general public (n = 22). Participants were guided through a deliberative process to maximise eliciting informed and considered preferences. Participants generally accepted the need for triage but strongly rejected 'fair innings' and 'life projects' principles as justifications for age-based allocation. They were also wary of the 'maximise life-years' principle, preferring to maximise the number of lives rather than life years saved. Although they did not arrive at a unified recommendation of one principle, a concern for three core principles and values eventually emerged: equality, efficiency and vulnerability. While these remain difficult to fully respect at once, they captured a considered, multifaceted consensus: utilitarian considerations of efficiency should be tempered with a concern for equality and vulnerability. This 'triad' of ethical principles may be a useful structure to guide ethical deliberation as societies negotiate the conflicting ethical demands of triage. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Internet of Things (IoT) in Smart Cities: A Statistical Survey

Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.

3D-Printed Face Mask with Integrated Sensors as Protective and Monitoring Tool

The outbreak of the recent Covid-19 pandemic changed many aspects of our daily life, such as the constant wearing of face masks as protection from virus transmission risks. Furthermore, it exposed the healthcare system's fragilities, showing the urgent need to design a more inclusive model that takes into account possible future emergencies, together with population's aging and new severe pathologies. In this framework, face masks can be both a physical barrier against viruses and, at the same time, a telemedical diagnostic tool. In this paper, we propose a low-cost, 3D-printed face mask able to protect the wearer from virus transmission, thanks to internal FFP2 filters, and to monitor the air quality (temperature, humidity, CO2) inside the mask. Acquired data are automatically transmitted to a web terminal, thanks to sensors and electronics embedded in the mask. Our preliminary results encourage more efforts in these regards, towards rapid, inexpensive and smart ways to integrate more sensors into the mask's breathing zone in order to use the patient's breath as a fingerprint for various diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

COVID-19 Vaccine and Booster Shots Compatibility Analyzer using CMOS-based Monochrome Encoding

The continuous increase in COVID-19 positive cases in the Philippines might further weaken the local healthcare system. As such, an efficient system must be implemented to further improve the immunization efforts of the country. In this paper, a contactless digital electronic device is proposed to assess the vaccine and booster brand compatibility. Using Logisim 2.7.1, the logic diagrams were designed and simulated with the help of truth tables and Boolean functions. Moreover, the finalized logic circuit design was converted into its equivalent complementary metal-oxide semiconductor (CMOS) and stick diagrams to help contextualize how the integrated circuits will be designed. Results have shown that the proposed device was able to accept three inputs of the top three COVID-19 vaccine brands (Sinovac, AstraZeneca, and Pfizer) and assess the compatibility of heterologous vaccinations. With the successful results of the circuit, it can be concluded that this low-power device can be used to manufacture a physical prototype for use in booster vaccination sites. © 2022 IEEE.

The Emerging Challenges and Strengths of the National Health Services: A Physician Perspective.

The National Health Services (NHS) is a British national treasure and has been highly valued by the British public since its establishment in 1948. Like other healthcare organizations worldwide, the NHS has faced challenges over the last few decades and has survived most of these challenges. The main challenges faced by NHS historically have been staffing retention, bureaucracy, lack of digital technology, and obstacles to sharing data for patient healthcare. These have changed significantly as the major challenges faced by NHS currently are the aging population, the need for digitalization of services, lack of resources or funding, increasing number of patients with complicated health needs, staff retention, and primary healthcare issues, issues with staff morale, communication break down, backlog in-clinic appointments and procedures worsened by COVID 19 pandemic. A key concept of NHS is equal and free healthcare at the point of need to everyone and anyone who needs it during an emergency. The NHS has looked after its patients with long-term illnesses better than most other healthcare organizations worldwide and has a very diversified workforce. COVID-19 also allowed NHS to adopt newer technology, resulting in adapting telecommunication and remote clinic. On the other hand, COVID-19 has pushed the NHS into a serious staffing crisis, backlog, and delay in patient care. This has been made worse by serious underfunding the coronavirus disease-19coronavirus disease-19 over the past decade or more. This is made worse by the current inflation and stagnation of salaries resulting in the migration of a lot of junior and senior staff overseas, and all this has badly hammered staff morale. The NHS has survived various challenges in the past; however, it remains to be seen if it can overcome the current challenges.

COVID-19 Pandemic and Challenges Faced by Healthcare Professionals in India.

Since the Great Influenza Pandemic of 1918, a pandemic of such magnitude as the COVID-19 pandemic was yet to be confronted. While the pandemic led to unforeseen challenges globally as well as at the country level, it also brought forth certain perennial issues. This editorial is an attempt to revisit some of the major challenges faced by healthcare professionals in India during the pandemic. Timely interventions by the government of India dealt with several challenges confronted by the healthcare sector. However, issues about working hours, mental health, safety, and security of healthcare professionals also need to be looked into in the future.

Three-Stage Power Supply System Model for a Wearable IoT Device for COVID-19 Patients.

During the current crisis caused by the COVID-19 pandemic, Wearable IoT (WIoT) health devices have become essential resources for remote monitoring of the main physiological signs affected by this disease. As well as sensors, microprocessor, and wireless communication elements are widely studied, the power supply unit has the same importance for the WIoT technology, since the autonomy of the system between recharges is of great importance. This letter presents the design of the power supply system of a WIoT device capable of monitoring oxygen saturation and body temperature, sending the collected data to an IoT platform. The supply system is based on a three-stage block consisting of a rechargeable battery, battery charge controller, and dc voltage converter. The power supply system is designed and implemented as a prototype in order to test performance and efficiency. The results show that the designed block provides a stable supply voltage avoiding energy losses, which makes it an efficient and rapidly developing system.

Effect of the Ongoing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Pandemic on Dental Service Utilization in Patients With Comorbidities.

BACKGROUND: The keystone of safe and effective patient management is to approach a patient with up-to-date medical information. Assessment of patients for their medical conditions has changed during the coronavirus disease 2019 (COVID-19) pandemic and the need for appropriate research infrastructure has increased. Considering an updated list of high-risk underlying conditions in the post-COVID-19 era, this study aimed to evaluate the utilization of dental services by patients with comorbidities during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. METHODS:  Data of patients with comorbidities seeking dental care at a dental school during the COVID-19 pandemic were retrospectively evaluated. Demographic variables (age, gender) and medical history of the participants were recorded. The patients were classified according to their diagnosis. Data were analyzed using descriptive statistics and Chi-square analysis. The significance level was determined at α=0.05. RESULTS:  The study included data from 1067 patient visits between September 1, 2020 and November 1, 2021. Among these patients, 406 (38.1%) were males and 661 (61.9%) were females, with a mean age of 38.28 ± 14.36 years. Comorbidities were identified in 38.3% of the patients with predominance in females (74.1% n=303). Single comorbidity was observed in 28.1% while multi-morbidity was detected in 10.2% of the cohort. The most prevalent comorbidity was hypertension (9.7%), followed by diabetes (6.5%), thyroid disorders (5%), various psychological diseases (4.5%), COVID-19 infection (4.5%), and different allergies (4%). The presence of one or more co-morbidities was observed mostly in the 50-59 years age group. CONCLUSIONS:  The seeking of dental care among the adult population with comorbidities was high during the SARS-CoV-2 pandemic. It would be beneficial to develop a template for obtaining a medical history from patients by taking full account of the consequences of the pandemic. The dental profession needs to respond accordingly.

Improving care for patients with Clostridioides difficile infection: A clinical practice and healthcare systems perspective.

Introduction: Arriving at a C. difficile infection (CDI) diagnosis, treating patients and dealing with recurrences is not straightforward, but a comprehensive and well-rounded understanding of what is needed to improve patient care is lacking. This manuscript addresses the paucity of multidisciplinary perspectives that consider clinical practice related and healthcare system-related challenges to optimizing care delivery. Methods: We draw on narrative review, consultations with clinical experts and patient representatives, and a survey of 95 clinical and microbiology experts from the UK, France, Italy, Australia and Canada, adding novel multi-method evidence to the knowledge base. Results and discussion: We examine the patient pathway and variations in clinical practice and identify, synthesize insights on and discuss associated challenges. Examples of key challenges include the need to conduct multiple tests for a conclusive diagnosis, treatment side-effects, the cost of some antibiotics and barriers to access of fecal microbiota transplantation, difficulties in distinguishing recurrence from new infection, workforce capacity constraints to effective monitoring of patients on treatment and of recurrence, and ascertaining whether a patient has been cured. We also identify key opportunities and priorities for improving patient care that target both clinical practice and the wider healthcare system. While there is some variety across surveyed countries' healthcare systems, there is also strong agreement on some priorities. Key improvement actions seen as priorities by at least half of survey respondents in at least three of the five surveyed countries include: developing innovative products for both preventing (Canada, Australia, UK, Italy, and France) and treating (Canada, Australia, and Italy) recurrences; facilitating more multidisciplinary patient care (UK, Australia, and France); updating diagnosis and treatment guidelines (Australia, Canada, and UK); and educating and supporting professionals in primary care (Italy, UK, Canada, and Australia) and those in secondary care who are not CDI experts (Italy, Australia, and France) on identifying symptoms and managing patients. Finally, we discuss key evidence gaps for a future research agenda.

Predicting COVID-19 from Chest X-ray Images using a New Deep Learning Architecture

The spread of coronavirus disease in late 2019 caused huge damage to human lives and forced a chaos in health care systems around the globe. Early diagnosis of this disease can help separate patients from healthy people. Therefore, precise COVID-19 detection is necessary to prevent the spread of this virus. Many artificial intelligent technologies for example deep learning models have been applied successfully for this task by employing chest X-ray images. In this paper, we propose to classify chest X-ray images using a new end-To-end convolutional neural network model. This new model consists of six convolutional blocks. Each block consists of one convolutional layer, one ReLU layer, and one max-pooling layer. The new model was applied on a challenging imbalanced COVID19 dataset of 5000 images, divided into two classes, COVID and Non-COVID. In experiments, the input image is first resized to 256×256×3 before being fed to the model. Two metrics were used to test our new model: sensitivity and specificity. A sensitivity rate of 97% was achieved along with a specificity rate of 99.32%. These results are promising when compared to other deep learning models applied on the same dataset. © 2022 IEEE.

Artificial Intelligence of Things for Smarter Healthcare: A Survey of Advancements, Challenges, and Opportunities

Healthcare systems are under increasing strain due to a myriad of factors, from a steadily ageing global population to the current COVID-19 pandemic. In a world where we have needed to be connected but apart, the need for enhanced remote and at-home healthcare has become clear. The Internet of Things (IoT) offers a promising solution. The IoT has created a highly connected world, with billions of devices collecting and communicating data from a range of applications, including healthcare. Due to these high volumes of data, a natural synergy with Artificial Intelligence (AI) has become apparent –big data both enables and requires AI to interpret, understand, and make decisions that provide optimal outcomes. In this extensive survey, we thoroughly explore this synergy through an examination of the field of the Artificial Intelligence of Things (AIoT) for healthcare. This work begins by briefly establishing a unified architecture of AIoT in a healthcare context, including sensors and devices, novel communication technologies, and cross-layer AI. We then examine recent research pertaining to each component of the AIoT architecture from several key perspectives, identifying promising technologies, challenges, and opportunities that are unique to healthcare. Several examples of real-world AIoT healthcare use cases are then presented to illustrate the potential of these technologies. Lastly, this work outlines promising directions for future research in AIoT for healthcare. IEEE

An Enhanced Internet of Things Enabled Type-2 Fuzzy Logic for Healthcare System Applications

Due to advancements in information and communication technology, the Internet of Things has gained popularity in a variety of academic fields. In IoT-based healthcare systems, numerous wearable sensors are employed to collect various data from patients. The healthcare system has been challenged by the increase in the number of people living with chronic and infectious diseases. There are several existing IoT-based healthcare systems and ontology-based methods to judiciously diagnose, and monitor patients with chronic diseases in real-time and for a very long term. This was done to drastically minimize the vast manual labor in healthcare monitoring and recommendation systems. The current monitoring and recommendation systems generally utilised Type-1 Fuzzy Logic (T1FL) or ontology that is unsuitable owing to uncertainty and inconsistency in the processing, and analysis of observed data. Due to the expansion of risk and unpredictable factors in chronic and infectious patients such as diabetes, heart attacks, and COVID-19, these healthcare systems cannot be utilized to collect thorough physiological data about patients. Furthermore, utilizing the current T1FL ontology-based method to extract the ideal membership value of risk factors becomes challenging and problematic, resulting in unsatisfactory outcomes. Therefore, this chapter discusses the applicability of IoT-based enabled Type-2 Fuzzy Logic (T2FL) in the healthcare system, and the challenges and prospects of their applications were also reviewed. The chapter proposes an IoT-based enabled T2FL system for monitoring patients with diabetes by extracting the physiological factors from patients' bodies. The wearable sensors were used to capture the physiological factors of the patients, and the data capture was used for the monitoring of patients. The results from the experiment reveal that the model is very efficient and effective for diabetes patient monitoring, using patient risk factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Machine Learning based COVID-19 Patients Monitoring System using CNN

Ever since an anonymous disease broke out in late 2019, the whole world seems to have own ceased functioning. COVID-19 patients are proliferating at an exponential rate, straining healthcare systems around the world. Traditional techniques of screening every patient with a respiratory disease is unfeasible due to the restricted number of testing kits available. We presented a method for recognizing COVID-19 infected patients utilizing data collected from chest X-ray scans to overcome this challenge. This attempt will benefit both patients and doctors significantly. It becomes even more critical in nations where the number of people affected far outnumbers the number of laboratory kits available to test the disease. When current systems are confused whether to retain the patient on the ward with other patients or isolate them in COVID-19 zones, this could be useful in an inpatient setting. Apart from that, it would aid in the identification of patients with a high risk of COVID-19 and a false negative RT-PCR who would require a repeat. Most of the COVID-19 detection methods use traditional image classification models. This has the issue of low detection accuracy and incorrect COVID-19 detection. This method starts with a chest x-ray enhancement procedure like this: Rotation, translation, random conversion. The survey's accuracy has considerably increased as a result of this. For the COVID-19 infection, our model has 97.5 percent accuracy and 100 percent sensitivity (recall). In addition, we used a visualization technique that distinguishes our model from the others by displaying contaminated areas in X-ray pictures. © 2022 IEEE.

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

In the last three years, the coronavirus (COVID-19) pandemic put healthcare systems worldwide under tremendous pressure. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing many diseases (for example, COVID-19). Recently, intelligent systems (Machine Learning (ML) and Deep Learning (DL)) have been widely utilized to identify COVID-19 from other upper respiratory diseases (such as viral pneumonia and lung opacity). Nevertheless, identifying COVID-19 from the CXR images is challenging due to similar symptoms. To improve the diagnosis of COVID-19 using CXR images, this article proposes a new deep neural network model called Fast Hybrid Deep Neural Network (FHDNN). FHDNN consists of various convolutional layers and various dense layers. In the beginning, we preprocessed the dataset, extracted the best features, and expanded it. Then, we converted it from two dimensions to one dimension to reduce training speed and hardware requirements. The experimental results demonstrate that preprocessing and feature expansion before applying FHDNN lead to better detection accuracy and reduced speedy execution. Furthermore, the model FHDNN outperformed the counterparts by achieving an accuracy of 99.9%, recall of 99.9%, F1-Score has 99.9%, and precision of 99.9% for the detection and classification of COVID-19. Accordingly, FHDNN is more reliable and can be considered a robust and faster model in COVID-19 detection. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Comparison of Different Models in Predicting COVID-19 Severity Based on Chest X-Ray Scans

The global outbreak of COVID-19 has resulted in a surge in patients in hospitals and intensive care units. This unprecedented demand for medical resources has severely burdened healthcare systems. Chest X-Ray (CXR) images can be used by hospitals and small clinics to predict COVID-19 severity to maximize efficiency and allot medical resources to patients with severe COVID-19. This research compares the accuracies of four convolutional neural network models in predicting COVID-19 severity using chest X-Rays images. The CNN models include VGG-16, ResNet 50, Xception, and a custom CNN model. Through the comparison, VGG-16 had the highest COVID-19 severity prediction accuracy of all four models, with 95.56% testing accuracy and 88.33% validation accuracy. Using a machine learning method, disease progression can be tracked more accurately and help prioritize patients to ensure effective and timely treatment. © 2022 IEEE.