The article is devoted to the development of conceptual issues of the methodology for forming the readiness of miners for effective and safe activities in extreme situations in mines. The improvement of the efficiency of miners’ training is based on using the capabilities of digital educational technologies and the developed training complex. The article defines the competency structure of workers in hazardous industries, ensuring that they perform working functions in extreme situations and reduce the risk of their life and health in case of accidents. The problem factors in the system of professional retraining are researched. The psychological and pedagogical conditions for the success of the use of the training complex are determined. The main approaches to the formation of the content of theoretical and practical training are formulated. The structure of the training complex for the training of miners is described; the process of its adjustment and functioning is considered. The key stages of the developed methodology for the formation of readiness of miners for activities in emergency situations in mines are considered. Training, using the proposed methodology and training complex, has significantly improved the application of self-rescuers, which is confirmed by an increase in the time indicator of the protective effect of the breathing apparatus used by students after training on simulators.

In virtual reality (VR) systems, a problem is the accurate reproduction of the user’s body in a virtual environment using inverse kinematics because existing motion capture systems have a number of drawbacks, and minimizing the number of key tracking points (KTPs) leads to a large error. To solve this problem, it is proposed to use the concept of a digital shadow and machine learning technologies to optimize the number of KTPs. A technique for movement process data collecting from a virtual avatar is implemented, modeling of nonlinear dynamic processes of human movement based on a digital shadow is carried out, the problem of optimizing the number of KTP is formulated, and an overview of the applied machine learning algorithms and metrics for their evaluation is given. An experiment on a dataset formed from virtual avatar movements shows the following results: three KTPs do not provide sufficient reconstruction accuracy, the choice of five or seven KTPs is optimal; among the algorithms, the most efficient in descending order are AdaBoostRegressor, LinearRegression, and SGDRegressor. During the reconstruction using AdaBoostRegressor, the maximum deviation is not more than 0.25 m, and the average is not more than 0.10 m.

We consider the problem of implementing a running platform control system for organizing a comfortable motion of a person in virtual reality with the possibility of adapting the speed of the platform depending on the nature of the user’s motion; this will increase the comfort and degree of immersion in the virtual environment. The article uses a controlled running platform (implemented on the basis of a unidirectional treadmill) and a human positioning system based on virtual reality sensors (trackers), which ensures sufficient frequency and accuracy of human positioning. Software has been developed for the platform that can collect data on the user’s motion and compare various control algorithms. A set of platform control functions, including those developed in the framework of this study, were formalized, compared, and evaluated. In the course of experimental studies, the optimal control functions were determined for the considered running platform. The advantages and disadvantages of each function are revealed. The scientific novelty of the study lies in the implementation of new running platform control functions to increase its adaptability to user actions by reducing the average deviation of a person from the starting position and hence the negative effects of oscillation and inertia. The practical significance lies in the improvement of the process of pairing virtual reality with devices simulating human motion (in the framework of this study, a unidirectional treadmill) and the development of appropriate software. The results obtained can be adapted to omnidirectional running platforms.

Introduction. The analysis of approaches to tracking the human body identified problems when capturing movements in a three-dimensional coordinate system. The prospects of motion capture systems based on computer vision are noted. In existing studies on markerless motion capture systems, positioning is considered only in two-dimensional space. Therefore, the research objective is to increase the accuracy of determining the coordinates of the human body in three-dimensional coordinates through developing a motion capture method based on computer vision and triangulation algorithms.Materials and Methods. A method of motion capture was presented, including calibration of several cameras and formalization of procedures for detecting a person in a frame using a convolutional neural network. Based on the skeletal points obtained from the neural network, a three-dimensional reconstruction of the human body model was carried out using various triangulation algorithms.Results. Experimental studies have been carried out comparing four triangulation algorithms: direct linear transfer, linear least squares method, L2 triangulation, and polynomial methods. The optimal triangulation algorithm (polynomial) was determined, providing an error of no more than 2.5 pixels or 1.67 centimeters.Discussion and Conclusion. The shortcomings of existing motion capture systems were revealed. The proposed method was aimed at improving the accuracy of motion capture in three-dimensional coordinates using computer vision. The results obtained were integrated into the human body positioning software in three-dimensional coordinates for use in virtual simulators, motion capture systems and remote monitoring.