Ryjov A.P.

The article describes the process of forming a feature space for a system that allows monitoring the performance of physical exercises and developing personal recommendations for correcting their performance based on the rules used by an expert trainer. Human Pose Estimation (HPE) methods are used to digitally represent exercise performance. The feature space should represent HPE data (coordinates of human body parts) as a set of feature values for making recommendations (for example, exercise intensity, user endurance). The feature space plays a central role in the development of such systems: the richer it is, the better the recommendations can be implemented.

Physical education is the foundation of a healthy lifestyle. The effectiveness and correctness of physical exercises can't be ensured without the supervision of an expert. The article proposes a system architecture based on hybrid intelligence, in particular, methods for human pose estimation and fuzzy logical inference, which makes it possible to determine the indicators necessary to evaluate exercise performance and form personal recommendations with adjustments to achieve correct performance.