Design and development of a mobile application for level monitoring of oxygen cylinders using block chain technology

Due to improvements in information and communication technology and growth of sensor technologies, Internet of Things is now widely used in medical field for optimal resource management and ubiquitous sensing. In hospitals, many IoT devices are linked together via gateways. Importance of gateways in modernization of hospitals cannot be overstated, but their centralized nature exposes them to a variety of security threats, including integrity, certification, and availability. Block chain technology for level monitoring in oxygen cylinders is a scattered record containing the data related to oxygen levels in the cylinder, patient's name, patient's ID number, patient's medical history, and all connected information carried out and distributed among the hospitals (nodes) present in the locality (network). Designing an oxygen level monitoring technique in an oxygen cylinder used as the support system for COVID-19-affected patients is a challenging task. Monitoring the level of oxygen in the cylinders is very important because they are used for saving the lives of the patients suffering from COVID-19. Not only the COVID-19 patients are dependent on this system, but this system will also be helpful for other patients who require oxygen support. The present scenario many COVID-19 hospitalized patients rely upon oxygen supply through oxygen cylinders and manual monitoring of oxygen levels in these cylinders has become a challenging task for the healthcare professionals due to overcrowding. If this level monitoring of oxygen cylinders are automated and developed as a mobile App, it would be of great use to the medical field, saving the lives of the patients who are left unmonitored during this pandemic. This proposal is entitled to develop a system to measure oxygen level using a smartphone App which will send instantaneous values about the level of the oxygen inside the cylinder. Pressure sensors and load cell are fitted to the oxygen cylinders, which will measure the oxygen content inside the cylinder in terms of the pressure and weight. The pressure sensors and load cells are connected to the Arduino board and are programmed to display the actual level of oxygen inside the cylinder in terms of numerical values. A beep sound is generated as an indicator to caution the nurses and attendants of the patients regarding the level of the oxygen inside the cylinder when it is only 15% of the total oxygen level in the cylinder in correlation to the pressure and weight. The signal with respect to the level corresponding to the measured pressure and weight of the cylinder is further transmitted to the monitoring station through Global System for Mobile communication (GSM). Graphical display is used at monitoring end to indicate the level of oxygen inside all oxygen cylinders to facilitate actions like 100% full, 80% full, 60% full, 40% full, 20% full which states that either the oxygen cylinder is in good condition, or requires a replacement of empty cylinders with filled ones in correlation to the pressure and weight being sensed by the sensors. The levels of the oxygen monitored inside the cylinder and other related data can also be stored on a cloud storage which will facilitate the retrieval of the status at any point of time, as when required by the physicians and nurses. These results reported, are valued in monitoring the level of the oxygen cylinder remotely connected to the patients, affected by COVID-19, using a smartphone App. This mobile phone App is an effective tool for investigating the oxygen cylinder level used as a life-support system for COVID-19-affected patients. A virtual model of the partial system is developed using TINKER CAD simulation package. In real time, the sensor data analysis with cloud computing will be deployed to detect and track the level of the oxygen cylinders. © 2023 Elsevier Inc. All rights reserved.

Machine Learning Algorithm for Trend Analysis in Short term Forecasting of COVID-19 using Lung X-ray Images

With the development of medical technology, the diagnosis of lung diseases relies more on the determination of medical images. With increasingly huge data, a powerful data processing model is urgently needed to provide favorable support for this field. The goal of this study is to develop a computer-assisted method to identify COVID-19 from X-ray pictures of the lungs at the very beginning of the disease. The architecture is implemented as a software system on a computer that can assist in the affordable and accurate early identification of cardiac illness. The performance of CNN architecture is best among all other classification algorithms to detect COVID-9 from Lung X-ray images. The datasets consist of COVID-19 established cases for 4 weeks which included the X-ray images of the chest. Then the distribution of the data was examined according to the statistical distribution. For this prediction, time series models are used for forecasting the pandemic situation. The performances of the methods were compared according to the MSE metric and it was seen that the Convolutional Neural Networks (CNN) achieved the optimal trend pattern.

Internet of medical things for abnormality detection in infants using mobile phone app with cry signal analysis

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.

A study to evaluate the role of eosinophil count as a prognostic marker for assessing the outcome in patients with COVID-19 infection.

Background and Objectives: The triaging of COVID-19 patients is of paramount importance to plan further management. There are several clinical and laboratory parameters that help in categorizing the disease severity, triaging, and prognostication. Little is known about the prognostic significance of eosinopenia in predicting the severity of COVID-19 from large hospital data, especially from low- and middle-income countries. The objective of this study is to evaluate the level of eosinopenia as an early prognostic marker for assessing the outcomes in COVID-19 patients and to assess the superiority of eosinopenia as a prognostic marker for assessing the outcomes in COVID-19 patients compared to lymphopenia and neutrophil-to-lymphocyte ratio (NLR). Methods: The study was carried out in a tertiary care hospital. A retrospective longitudinal approach was adopted wherein the hospital records of COVID-19 patients were analyzed. In our study, two separate groups of patients were included for analysis to describe the association between initial eosinophil counts of the patients and the clinical outcomes. In the first group, the disease severity in terms of clinical and radiological parameters was compared in patients of COVID-19 presenting with and without the presence of initial eosinopenia. Commonly used markers for triage, namely lymphopenia and NLR, were compared with the presence of initial eosinopenia among the patients who progressed to moderate and severe disease. In the second group, an analysis of eosinopenia was made among the patients who succumbed to the illness. Results: It was seen that 29.6% of patients with eosinopenia had moderate and severe disease compared to those without eosinopenia where only 10.8% had moderate disease, none had severe disease. It was seen that 19.7% of patients with eosinopenia but no lymphopenia had more severe disease compared to patients with lymphopenia but no eosinopenia where 10.8% of the patients had moderate disease, none had severe disease. In patients younger than 60 years who died of COVID-19, it was found that initial eosinopenia was found in 86%, whereas a high NLR >17 was seen in only 25.6% of patients who died, thus implying that is eosinopenia is an important marker of disease severity in COVID-19. Conclusions: Eosinopenia is an important parameter in the evaluation of COVID-19 and the presence of it should alert the clinicians regarding the further progression of the disease. It is not only an important marker but also an early marker for severe disease.

Indigenous Non-invasive Detection of Malnutrition-Induced Anemia from Patient-Sourced Photos Using Smart Phone App

The proposed online-based malnutrition-induced anemia detection smart phone app is built, to remotely measure and monitor the anemia and malnutrition in humans by using a non-invasive method. This painless method enables user-friendly measurements of human blood stream parameters like hemoglobin (Hb), iron, folic acid, and vitamin B12 by embedding intelligent image processing algorithms which will process the photos of the fingernails captured by the camera in the smart phone. This smart phone app extracts the color and shape of the fingernails, will classify the anemic and vitamin B12 deficiencies as onset, medieval, and chronic stage with specific and accurate measurements instantly. On the other dimension, this novel technology will place an end to the challenge involved in the disposal of biomedical waste, thereby offering a contactless measurement system during this pandemic Covid-19 situation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Smart Phone App with Geo-Spatial Information for Forecasting COVID-19 from Lung X-ray Images

Background: Presently, the diagnosis of coronavirus-2019 (COVID-19) is a challenging task worldwide as the disease is spreading at a very faster rate when one person with the disease comes into contact with the other. Current information denotes that several people are detected with COVID-19 and the data analyst say that the rate of spread of the disease is increasing exponentially, across many countries in the world. Novelty: This investigation has facilitated the need for diagnosing the disease within a short duration of time by using the X-ray images of the lungs. This scheme deploys artificial intelligence like deep learning algorithms to diagnose COVID-19 among the affected people by maintaining social distancing. Real-time datasets are gathered from the government hospitals for those who are affected by COVID-19 and healthy people. Further investigation can direct the patients themselves to open the smart phone app which will record the respiratory sounds. Followed by this, the features are extracted using Discrete Wavelet Transform (DWT), where a threshold is applied to extract useful coefficients that can be used to train the deep learning neural networks using Fast Recurrent Convolutional Neural Networks (F-RCNN). The respiratory audio signals are captured to detect patients affected by coronavirus by a way of noncontact, nonintrusive approach. The results reported are valued in detection of COVID-19 by using a smart phone app which is available instantly. Objectives: This approach seems to be an indigenous, noninvasive, and cost-effective approach that will relive the patients from trauma of undergoing the swab test and awaiting the laboratory reports, which incurs time delay. Experimental results are obtained from 20,000 samples of patients suffering from COVID-19 and also persons who are normal. This mobile phone app is effective in diagnosing the COVID-19 from the X-ray images of the lungs. Even low-income people can also use this technology. Methods: The effectiveness of the proposed system which uses DWT, thresholding, and deep learning algorithms resulted with a performance whose F-measure is 96–98%. The classification is carried out to classify the COVID-19-positive and COVID-19-negative cases using Fast Recurrent Convolutional Neural Networks (F-RCNN). Expected Outcome: A smart phone app will be developed to detect the COVID-19 by using a noninvasive and easily affordable technique. The forecasted results were in the range of 89–95% for the above said algorithms. It is significant from the above results that the severe impact of COVID-19 can be diagnosed using a noninvasive mobile phone app using X-ray images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A smart automatic sanitizer dispenser and body temperature inspection machine

As we all know the saying "Health is wealth", it should be a mantra for everyone because we are in such a world. We need to have a contactless automatic sanitize dispenser machine (ASDM) so that we can protect ourselves from harmful bacteria. The novel 2019 Coronavirus (2019-nCoV) is new and widespread in the year 2019 and it is truly an unusual respiratory Coronavirus 2 (SARS-CoV-2). The spread of the disease is from person to person, which facilitates the spread. To reduce the spread of Coronavirus, any contact between people and potential carriers of the virus should be limited. Hand hygiene is important to control the spread of COVID-19 and it is more contagious in places where pollution is high and in public transport, markets, conclaves and other public places. Under the present circumstances, the social distance of public places and the need for constant disinfection. A low-cost and easy-to-access portable IoT based device is now implemented and installed where needed. This device not only spreads the sanitizer when a person's hand reaches the nozzle of the sanitizer-bottle but also checks the body temperature and it turns on the Red LED and alerts the person by making sound on the buzzer when the temperature limit is exceeded;otherwise it turns on the Green LED. © 2022 Author(s).

Radiographic Image Processing Analysis for Lung Infection - A Review

This paper presents a brief review on the developments of computer aided diagnosis system using image processing approaches. The rapid increase in lung infections which was in multiple during the current Corona virus infection has outcome with the need of automation system for an early detection of lung infection. Early detection of lung infection can avoid the spread of infection further and also act as an alarming intimation under critical cases. The need of such system has outcome with many researches in recent past towards developing new approaches toward improving the decision accuracy to reducing the system response time. This article review the past developments made in the area of developing automation systems with an analysis of attainted accuracy and methodology of image processing and classification system for automated lung infection detection. © 2022 IEEE.

Empirical analysis on the impact of online classroom on student’s perception

Covid 19 pandemic has shifted most of the classrooms to online mode. The primary stakeholders of the virtual classroom are students and many students are unable to attend classes due to various reasons. The Empirical Analysis of Online Classroom’s impact on Student Perception gives the review and analysis of the specified community. The paper deals with different online tools used by faculty of Engineering and Management colleges. Moreover to have a clear picture of the scenario, the data is gathered from students of Engineering & Management and this database is given to an online tool and analysis is done by using machine learning algorithm. The students have responded without being bias, as the chosen time to collect the data was when they were in their residence. The sample data is worked out and the pie charts represent perception of various students. Finally, the analysis depicts that students prefer offline classes and during such pandemic situation, they are ready to move online with some criteria.

Safe airway management of critically ill COVID-19 patients

As the COVID-19 pandemic evolves, health care workers (HCWs) are facing lots of challenges due to the highly contagious nature of the virus. Undoubtedly, one of the most crucial and substantial steps of patient care is airway management, which represents a major risk of infection spread to HCWs and others. Safe airway management of critically ill patients is always challenging and primarily depends on staff training, simulation drills, pre-defined management protocols, and the availability of appropriate equipment. We present a COVID-19 airway management algorithm that we developed in our institution in order to provide our service in a timely manner, as well as ensure HCWs and other patients safety.