Personal Physical Fitness Modeling through Real-Time Predictive Models

Committingto sports as a vital component of a healthy lifestyle necessitates ongoing awareness of one’s body, emphasizing physical constraints relative to current health and activity levels. This research focuses on developing predictive models using machine learning algorithms based on real-time and accumulated personal data, encompassing vital functions and other physical and mental parameters. The objective is to optimize the recognition and prediction of an individual’s physical fitness for more effective participation in sports. A key element is the analysis of personal data to craft an individualized physical fitness model. By integrating data on vital functions and relevant parameters, the system aims to support decisions on optimal body load during sports activities. The quality of predicting an individual’s physical fitness relies on precise and comprehensive data, placing requirements on the monitoring and analysis system for bodily parameters. The data is going to be collected through various wearable devices and are presented to the user through an interactive iOS and Android application. In this work, practical examples were created for several individuals, illustrating the relationships of their actual values of physical fitness without using predictive models, considering the use of predictive models with the possibility of customization according to individual preferences.