DEVELOPMENT OF AN INTELLIGENT FITNESS TRACKER FOR PREDICTIVE FATIGUE ANALYSIS BASED ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS TECHNOLOGIES

Abstract

This paper discusses the task of developing a new generation intelligent fitness tracker designed for continuous monitoring of human physiological indicators and predictive analysis of the level of fatigue based on artificial intelligence and the Internet of Things (IoT) technologies. The relevance of the study is due to the growing need for personalized systems for monitoring the physical condition of athletes and people leading an active lifestyle, as well as the need to prevent overtraining and functional overload.

As part of the work, current trends in the field of wearable devices and biomonitoring systems are analyzed, their main limitations associated with the fragmentation of data and the lack of intelligent interpretation of indicators are identified. The architecture of the hardware and software complex of the fitness tracker is proposed, including a modular sensor system, a microcontroller, wireless data transmission channels and a cloud analytical platform. Heart rate, heart rate variability, physical activity level, body temperature and sleep indicators are used as recorded parameters.

Machine learning algorithms have been developed to process and analyze time series of biometric data, which predict the level of fatigue and assess the user's readiness for physical activity. The proposed solution makes it possible to generate personalized recommendations for the training process and recovery, as well as increases the accuracy and informative value of monitoring the physiological state in comparison with existing commercial analogues.