ABSTRACT
The probability of medical staff to get affected from COVID19 is much higher due to their working environment which is more exposed to infectious diseases. So, as a preventive measure the body temperature monitoring of medical staff at regular intervals is highly recommended. Infrared temperature sensing guns have proved its effectiveness and therefore such devices are used to monitor the body temperature. These devices are either used on hands or forehead. As a result, there are many issues in monitoring the temperature of frontline healthcare professionals. Firstly, these healthcare professionals keep wearing PPE (Personal Protective Equipment) kits during working hours and as a result it would be very difficult to monitor their body temperature. Secondly, these healthcare professionals also wear face shields and in such cases monitoring temperature by exposing forehead needs removal of face shield. Doing so after regular intervals is surely uncomfortable for healthcare professionals. To avoid such issues, this paper is disclosing a technologically advanced face shield equipped with sensors capable of monitoring body temperature instantly without the hassle of removing the face shield. This face shield is integrated with a built-in infrared temperature sensor. A total of 10 such face shields were printed and assembled within the university lab and then handed over to a group of ten members including faculty and students of nursing and health science department. This sequence was repeated four times and as a result 40 healthcare workers participated in the study. Thereafter, feedback analysis was conducted on questionnaire data and found a significant overall mean score of 4.59 out of 5 which indicates that the product is effective and worthy in every facet. Stress analysis is also performed in the simulated environment and found that the device can easily withstand the typically applied forces. The limitations of this product are difficulty in cleaning the product and comparatively high cost due to the deployment of electronic equipment.
ABSTRACT
COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country's economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named "C19D-Net", to detect "COVID-19" infection from "Chest X-Ray" (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model ("C19D-Net") and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of "precision", "accuracy", "F1-score" and "recall" in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed "C19D-Net" can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.