The paper is devoted to the development and testing of a remote biomonitoring system based on the phenomenon of plethysmography. This phenomenon allows not only to measure a person’s pulse rate non-invasively, but also to assess physiological state of the person. At the first stage of the system operation, it is necessary to detect regions of interest. This operation can be effectively implemented using neural networks. The task of face recognition was performed by the YOLOv7-tiny architecture, due to its speed and the ability to run on embedded systems. For the detected face, a rectangle was created, whose coordinates indicated the boundaries of the face. Next, the average brightness of the selected areas is calculated and stored in the dataset. By performing fast Fourier transform (FFT) for a given set, it is possible to obtain a signal spectrum. Using methods of digital signal processing, it is possible to filter the signal and select the part of the spectrum of interest in the region of 0.7–3 Hz. The maximum amplitude of the harmonic will correspond to the current pulse.