COVID-19 patient breath monitoring and assessment with MEMS accelerometer-based DAQ - a Machine Learning Approach

Abstract

                The coronavirus-2 infection, which emerged in 2019, is a severe illness that can cause acute respiratory failure and death. The effects of the coronavirus-2 disease on the respiratory system have been studied. This paper aims to develop a wearable device that can provide reliable and cost-effective respiratory monitoring. The hardware has been installed on COVID-19 infected individuals and healthy individuals. The study's goal is to find abnormalities in the data sets that can be used to estimate the respiration rate.