Abstract

Introduction. In the modern world, the introduction of digital technologies in healthcare is one of the priorities of development, which opens up a wide range of opportunities from screening and monitoring to various health disorders. In 2020 we completed the work to evaluate the applied capabilities of contactless videoplethysmography based on the telemetric control system developed by us and patented using the DISITA software and hardware complex during pre-trip post-trip medical examinations in 19 drivers of passenger vehicles. The study aims to explore the possibilities of the data of variational heart rate monitoring using reflected video plethysmography in assessing drivers' performance in their work. We have identified the most sensitive and significant heart rate variability indicators that reflect the professional load's impact. Materials and methods. We carried out during the usual pre-trip examination, video plethysmography of the skin of the face of the subjects in parallel in conditions of both natural daylight and typical artificial lighting of medical and diagnostic rooms, at a distance of the recording WEB camera of the DISITA software and hardware complex from the face of the subject within 40-70 cm. Researchers examined the methodological recommendations developed by the Izmerov Research Institute of Occupational Health team and RT-Medicine JSC. Results. We use videoplethysmography to evaluate variational heart rate monitoring as a method for assessing functional states during mass pre-trip (pre-shift) and post-trip (post-shift) medical examinations. In this case, the heart rate estimated by palpation was identical to the heart rate obtained by videoplethysmography. The most sensitive parameters characterizing the impact of professional load on drivers were changes in the indicators of variational heart rate monitoring: SDNN, RMSSD, CV, TR, HF, LF, and the waves of vasomotion regulation. Conclusions. Videoplethysmography with an assessment of the data of variational heart rate monitoring can be used to predict the functional state (stability of the body) of drivers in the course of their professional activities.

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