Abstract
Relevance. Scientific research, coaching practice, and athletic training necessitate real-time monitoring of key parameter such as the anaerobic component of exercise. While this metric can be derived from heart rate data, there is currently a lack of comprehensive domestic software solutions capable of automating these calculations and delivering immediate post-exercise results.
Aim. To develop an algorithm for calculating and displaying the anaerobic dose of physical load in real time, along with a prototype computer program for assessing physical performance parameters based on heart rate data obtained during standardized exercise tests.
Methods. The algorithm development utilized the standardized PWC170 test conducted via step-ergometry as a primary validation protocol, though the proposed methodology demonstrates extensibility to alternative exercise testing modalities. HR parameters were derived from
continuous heart rate monitoring using the Polar H10 chest-worn transmitter, with real-time data acquisition facilitated through Bluetooth protocol.
Implementation was achieved through a client-side web application architecture utilizing JavaScript (ECMAScript 6+) and HTML5 standards. The system demonstrates efficient computational performance, generating and displaying a comprehensive panel of 9 exercise load parameters immediately upon test termination. This near-instantaneous analytical capability provides significant advantages for real-time training monitoring and immediate physiological assessment.
Results. The study successfully developed an innovative algorithm for real-time assessment of anaerobic exercise load components using heart rate data. This computational approach enabled precise quantification of anaerobic contribution through advanced processing of cardiometric parameters. The software solution generates 9 key performance metrics immediately after test completion, with particular focus on anaerobic load evaluation The prototype has been made publicly available as a web application compatible with
standard heart rate monitors, demonstrating practical utility for sports science applications (https://github.com/Denisqe/box).
Conclusions. The developed software enables real-time heart rate data processing, instantly calculating exercise parameters post-test. This enhances training monitoring efficiency and decision-making while maintaining scientific accuracy, offering a practical tool for sports science applications. The solution combines methodological rigor with operational speed, providing reliable physiological assessments. Its immediate feedback and precision make it valuable for both research and athletic practice.