The paper considers the problem of the impact of a complex of environmental factors and their impact on the health of young people. Young people aged 18 to 21 years old, who lived up to 18 years old in different environmental conditions, were examined in order to assess the general functional state of their body. The study used a developed non-invasive optical method for assessing the oxygen status of tissues and general functional state, as well as an independent biomedical study. The results of the experiment revealed the significance of the influence of the ecological factors of the region of birth on the functional state of health of the young generation.

An automated complex for the study of the oxygen status of human body tissues using an artificial trained analytical system is presented. It includes sensory, electronic and computer modules for measuring and analyzing data. System training is implemented according to the principle of dividing the subjects into groups using mathematical methods of data processing and comparing them with data on the degree of adaptive responses obtained in the course of independent biomedical research. After analytical system training, subsequent recognition of the “image” of the test subject and its assignment to one or another group of the type of reaction to the functional load was possible.

The article describes a new method for processing a pulse wave, which is formed from the recorded absorption and scattering signals of laser radiation on a blood vessel or human tissues. A new method of tuning the optical part of the pulse oximeter has been developed to increase the reliability of the results in the rapid diagnosis of the human condition. Experimental data on studies of various people are presented.

The article presents the results of application of the Kohonen artificial neural network (KANN) in assessing the oxygen status of human tissues, as well as for studying the adaptive-compensatory response of the body to functional load. In the experiment, the registered digital oxygen images of the tissue of 31 subjects were distributed into three classes using the KANN. Each group is characterised by different resistance of the organism to hypoxia. The research results have shown the effectiveness of using an artificial neural network structure and the possibility of its implementation for recognition of the functional state of a person under conditions of metabolic hypoxia; it seems relevant and has theoretical and practical significance in the framework of ecological physiology.