Adaptive Algorithms for Personalizing Physical Loads in the Physical Education of Students with Different Levels of Physical Fitness Based on Wearable Devices

Abstract

In the context of the digitalization of education, enhancing the effectiveness of physical education for students with varying levels of fitness has become increasingly important. Traditional approaches to organizing physical education classes do not always ensure adequate personalization of physical loads, which may reduce motivation for physical activity and increase the risk of overload. The aim of this study is to provide a theoretical justification for the use of adaptive algorithms to personalize physical loads within the system of physical education for students with different levels of preparedness through the use of wearable digital devices, and to analyze the prospects for integrating biometric data into the management of training activities in the educational environment.

Methods. A set of general scientific and specialized methods was employed, including analysis and synthesis of scholarly sources, systematization of theoretical approaches to the personalization of physical training, comparative analysis of the capabilities of digital technologies for monitoring physical activity, and theoretical modeling to determine the principles for designing adaptive algorithms to regulate physical loads based on indicators of students’ functional state.

Results. The findings indicate that the use of wearable devices enables continuous monitoring of biometric parameters, including heart rate (HR), levels of physical activity, and recovery indicators. This approach allows for automated adjustment of exercise intensity according to the individual level of student preparedness. The feasibility of applying algorithmic models of personalization that adapt training stimuli to the dynamics of the functional state and optimize the physical education process has been substantiated. The principal advantages of digital tools have been identified, including enhanced effectiveness of physical training, reduced risk of overload, and the development of individualized trajectories for improving students’ physical qualities.

Conclusions. The integration of adaptive algorithms for load personalization into the system of physical education contributes to improved instructional effectiveness and ensures the personalization of the educational process in accordance with students’ physiological capacities.