Design and Implementation of IoT-Based Automatic Oxygen Flow Control in Response to the Covid-19 Crisis

Internet of Things (IoT) technology has brought a revolution in several ways to a common person's life by making everything smart and intelligent. During the Covid-19 crisis, health workers around the world needed to monitor patients' health and needed to provide sufficient oxygen, when necessary, as Covid-19 was responsible for many respiratory cases. Health workers were at high risk of being contaminated while treating Covid-19 patients. The study of this paper is to propose an IoT-based automatic oxygen flow control in response to the Covid-19 crisis. The proposed approach helped to real-time monitoring of SpO2, heartbeat, oxygen quantity of oxygen cylinder, and control of the flow of oxygen based on SpO2 value. A health worker can monitor a patient's health-related parameters and control the flow of oxygen without any physical contact with it. Also, provides an alarm to the health worker when SpO2 is below the threshold and re-measuring oxygen quantity of oxygen cylinder with the help of our developed android app. Implementation of IoT-based low-cost pulse oximeter and IoT-based pressure gauge helps to monitor and control different health parameters. The IoT-based system may potentially be valuable during the Covid-19 pandemic for accurate oxygen flow distribution and for saving people's lives. © 2022 IEEE.

A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images

There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life. © 2022 Institute of Advanced Engineering and Science. All rights reserved.