When patients perform musculoskeletal rehabilitation exercises, it is of great importance to observe the correctness of their performance. The aim of this study is to increase the accuracy of recognizing human movements during exercise. The process of monitoring and evaluating musculoskeletal rehabilitation exercises was modeled using various tracking systems, and the necessary algorithms for processing information for each of the tracking systems were formalized. An approach to classifying exercises using machine learning methods is presented. Experimental studies were conducted to identify the most accurate tracking systems (virtual reality trackers, motion capture, and computer vision). A comparison of machine learning models is carried out to solve the problem of classifying musculoskeletal rehabilitation exercises, and 96% accuracy is obtained when using multilayer dense neural networks. With the use of computer vision technologies and the processing of a full set of body points, the accuracy of classification achieved is 100%. The hypotheses on the ranking of tracking systems based on the accuracy of positioning of human target points, the presence of restrictions on application in the field of musculoskeletal rehabilitation, and the potential to classify exercises are fully confirmed.

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.