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Talk · IEEE International Conference on Smart Information Systems and Technologies · May 13, 2026

Development of an Intelligent Fitness Tracker for Predictive Fatigue Analysis Based on Artificial Intelligence and the Internet of Things Technologies

Faizullin Adil, Ilyas Kazambayev, Andrey Mikryakov, Aliya Aubakirova, Ainur Medetkhan

Ilyas Kazambayev
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Abstract

This paper discusses the development of a new generation intelligent fitness tracker designed for continuous monitoring of human physiological indicators and predictive analysis of fatigue levels based on artificial intelligence and Internet of Things (IoT) technologies. The relevance of the study is driven by the growing need for personalized systems to monitor the physical condition of athletes and physically active individuals, as well as to prevent overtraining and functional overload. Current trends in wearable devices and biomonitoring systems are analyzed, and their key limitations related to data fragmentation and lack of intelligent interpretation are identified. The architecture of the proposed hardware and software complex includes a modular sensor system, microcontroller, wireless data transmission, and a cloud analytical platform. Recorded parameters include heart rate, heart rate variability, physical activity level, body temperature, and sleep indicators. Machine learning algorithms are developed to analyze biometric time series data, predict fatigue levels, and assess readiness for physical activity. The proposed solution enables generation of personalized training and recovery recommendations and improves the accuracy and informativeness of physiological monitoring compared to existing commercial solutions.

Context-Aware Activity Recognition SystemsNon-Invasive Vital Sign MonitoringPhysical Education and Training Studieswearable activity trackersfatigue predictionwearable devices

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