Abstract
Monitoring systems allow operators to accomplish the greatest comfort indoors, but, as a rule, the available parameters are not enough to analyse the epidemiological threat in buildings. Due to the pandemic and increasing incidence of the disease, there is a need for monitoring systems that can provide the necessary information to analyse the risk of infection. With timely notification of people about the risks, such a system could not only increase safety in buildings, but also save crucial resources such as the work of medical personnel. This paper presents an example of real-world implementation of a cheap and scalable system to indicate risks and inform people inside. To achieve this, an appropriate set of sensors and communication protocols was selected, and processing of indirect measurements with artificial intelligence (AI) algorithms was carried out on an embedded Jetson Nano computer. Based on the experiments and a review of the literature, the necessary parameters for measurements were selected. Detailed analysis of measured data for risk evaluation is provided in [1].
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