OBJECTIVE Aim: To analyse current approaches to monitoring sports performance and health of athletes by developing an intelligent system that combines wearable devices, cloud computing and deep learning methods. PATIENTS AND METHODS Materials and Methods: The paper analyses related literature in sports medicine, informatics and artificial intelligence. The work is based on studying the effectiveness of devices such as Fitbit Charge 5, Garmin Venu 2, Samsung Galaxy Watch 4, and Oura Ring Gen 3. RESULTS Results: Showed that such systems provide high accuracy in predicting athletes' health status. The presented models allow real-time tracking of physiological parameters, analysing the data and generating health reports for prompt adjustment of the training process. These devices enable systematic monitoring of various indicators, such as heart rate, stress level, sleep quality and overall physical activity. Reading these indicators allows athletes to receive objective information about their condition. This, in turn, contributes to more effective training planning, recovery and injury prevention. CONCLUSION Conclusions: Integrating wearables, cloud computing, and deep learning methods presented on the latest devices is a promising approach to sports health monitoring. The analysed devices can improve athletes' performance, prevent injuries and optimise training programmes.
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The role of health monitoring technologies in optimising athletes' self-regulation.
Semantic Scholar · Medicine · 2025
Abstract
OBJECTIVE Aim: To analyse current approaches to monitoring sports performance and health of athletes by developing an intelligent system that combines wearable devices, cloud computing and deep learning methods.
PATIENTS AND METHODS Materials and Methods: The paper analyses related literature in sports medicine, informatics and artificial intelligence. The work is based on studying the effectiveness of devices such as Fitbit Charge 5, Garmin Venu 2, Samsung Galaxy Watch 4, and Oura Ring Gen 3.
RESULTS Results: Showed that such systems provide high accuracy in predicting athletes' health status. The presented models allow real-time tracking of physiological parameters, analysing the data and generating health reports for prompt adjustment of the training process. These devices enable systematic monitoring of various indicators, such as heart rate, stress level, sleep quality and overall physical activity. Reading these indicators allows athletes to receive objective information about their condition. This, in turn, contributes to more effective training planning, recovery and injury prevention.
CONCLUSION Conclusions: Integrating wearables, cloud computing, and deep learning methods presented on the latest devices is a promising approach to sports health monitoring. The analysed devices can improve athletes' performance, prevent injuries and optimise training programmes.