Development of a monitoring system for COVID-19 monitoring in early stages

Abstract

Covid-19 is considered the most infectious virus today. Likewise, the struggle to mitigate the effects of the variants, the flexibility in some measures such as the use of face masks, the advancement of vaccination and prevention and self-care campaigns continue to be topics of research and of global interest. The world health authorities published that the disease was characterized by presenting the same symptoms as the flu along with a complex picture where in the most serious cases they lead to difficulty breathing due to pneumonia, sepsis and septic shock that can lead to death. Some systems implemented for taking body temperature such as thermographic cameras, digital thermometers, for the description of symptoms in the people they analyze at the time of carrying out the epidemiological fences are not enough, since they handle low precision, are taken in isolation, individually or randomly and is not suitable for characterizing interest groups. Then, establishing risk levels by measuring non-invasive variables can be considered inputs into prevention campaigns and a low-cost way of monitoring the community. This article shows the design of a non-invasive embedded device for the measurement of 5 priority variables for the detection of the risk of covid-19 infection. The proposed device was duly calibrated and synchronized for the acquisition of data from 594 people in the city of Bucaramanga, Colombia, who authorize the monitoring of the symptoms. The people must be in a state of rest to be able to acquire the data with great accuracy, in this way the data is entered into the system in charge of doing the monitoring analysis. Additionally, the implementation of an interface that allows the visualization of results, laying the foundations for the development of automatic learning techniques or models for the risk classification in future work. © The Author 2023. This work is licensed under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) that allows others to share and adapt the material for any purpose (even commercially), in any medium with an acknowledgement of the work's authorship and initial publication in this journal.