Since the beginning of the current COVID-19 pandemic, more than five million people have been infected and the numbers are still on the rise. Early symptom detection and proper hygienic standards are thus of utmost importance, especially in venues where people are in random or opportunistic contact with each other. To this end, automated systems with medical-grade body temperature measurement, hygienic compliance evaluation and individualized, person-to-person tracking, are essential, not only for disease spread intervention and prevention, but also to assure economic stability. Herein, we present a system that encapsulates all of the mentioned functionality via readily-available components (both hardware and software) and is further enhanced with preliminary RTLS data acquisition, enabling post-symptom detected, person-toperson interaction identification to asses potential infection vectors and mitigate further propagation thereof by means of smart quarantine.

Abstract-This paper introduces low-cost non-intrusive sensory that can collect traffic data based on Raspberry Pi single board computer. Image information acquired by Raspberry Pi HD camera module is analyzed for moving objects presence. After evaluation of detected object count, size, class and motion vector object properties are sent to server node by RF transceiver. Sensor low-power consumption ensures possibility to operate from battery for an extended period of time.

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Indoor environment is from the point of the wireless communication an extremely hostile environment. Despite this fact, wireless sensor network applications in the indoor environment are very common. Having signal propagation in a real environment, without considering interferences from other sources, we meet (not only indoors) four basic phenomenonspath-loss, reflection, diffraction and scattering. Each of these effects impact on the spread of the signal and contributes to attenuation and distortion at the receiver side. Detailed description of the electromagnetic wave propagation can theoretically obtain the solution of Maxwell's equations. However, this is too demanding and for practical cases unusable. In practice, to describe the signal propagation, the approximate models are used, which are often based on experimental results. This contribution includes case study on indoor radio frequency signal propagation at 2.4GHz ISM Band with related math, supported by implementation of the propagation models and experimental results.