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COVID-19 Pandemic Prevention Mobile Application for on Campus Classroom
Author(s)
Date Issued
April 23, 2021
Type
Conference Paper
Abstract
COVID-19 pandemic is a novel coronavirus that has not been found in humans before. This virus can be transmitted to other humans primarily through respiratory secretions when an infected person coughs or talks. To avoid human to human transmission of this pandemic, the government extend the state of emergency policies which cause vital damage for many business sections worldwide including educational institution. Many schools decide to let the students learn at home. However, in practical courses such as a chemical workshop, students must come to the laboratory room to perform experiments which may increase the risk of infection. In order to prevent the spread of COVID-19 between students and staff, anybody entering the school must conduct a risk assessment, measure a body temperature and wear a face mask at all times. Many COVID-19 contact tracing platforms allow users to assess infection risk and notify if they have been exposed to infected persons. Unfortunately, they cannot be used effectively with the on-campus education system. The proposed mobile application was developed to handle the needs of the onsite education system during the ongoing COVID-19 situation in schools. The application contains three main functions which are a COVID-19 self-assessment, a roll-call, and a social distancing function. This paper focused on the roll-call function using face recognition and Global Positioning System (GPS). In a normal situation, the student just opens an application, shows his or her face to a smartphone camera then the application will detect a face part and easily recognize the student's identification. However, in the new normal situation where everyone must wear a mask, it will be a very difficult task to perform face recognition since almost half of the face is hidden. The convolutional neural network (CNN) was applied to train a CNN model using a dataset of 18 peoples with non face mask wearing and face mask wearing. The face mask wearing consisted of three different face mask types: Disposable surgical mask (DS), N95 face respirators (N95) and general 3D mask (3D). After that, the model was exported to the proposed mobile application. Experimental results on a realworld dataset show that the proposed model can be used with a high accuracy rate in non face mask samples. In face mask samples, the 3D mask has the highest accuracy rate.
Citation
2021 IEEE 6th International Conference on Computer and Communication Systems Icccs 2021, 1117-1121, 2021