Exercise performance monitoring system by hybrid intelligence tools: feature space

Recieved: 01/22/2026

Accepted: 02/25/2026

Keywords: computer vision, human pose estimation, fuzzy logic, hubrid intelligence, control of physical exercises

Available in the on-line version with: 24.07.2026

To cite this article

Shergin I.A., Ryjov A.P. Exercise performance monitoring system by hybrid intelligence tools: feature space. // Moscow University Journal. Series 15. Computational Mathematics and Cybernetics. 2026. N 2, p.71-83 https://doi.org/10.55959/MSU/0137–0782–15–2026–50–2–71–83.

This work is licensed under a Сreative Commons Atribiution - NonCommercial 4.0 International (CC BY-NC 4.0)

Shergin I.A. (Lomonosov Moscow State University (MSU))
Ryjov A.P. (Lomonosov Moscow State University (MSU))

Abstract

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.