20.500.12556/DKUM-95632 The role of intelligent data analysis in selected endurance sports a systematic literature review In endurance sports, athletes and coaches shift increasingly from intuition-based decisionmaking to data-driven approaches powered by modern technology and analytics. Since 2018, the field has experienced significant advances, influencing endurance sports disciplines. This systematic literature review identified 75 peer-reviewed studies on intelligent data analysis in endurance sports training. Each study was categorized by its intelligent method (e.g., machine learning, deep learning, computational intelligence), the types of sensors and wearables used, and the specific training application and approach. Our synthesis reveals that machine learning and deep learning are among the most used approaches, with running and cycling identified as the most extensively studied sports. Physiological and environmental data, such as heart rate, biomechanical signals, and GPS, are often used to aid in generating personalized training plans, predicting injuries, and increasing athletes’ long-term performance. Despite these advancements, challenges remain, related to data quality and the small participant sample sizes. smart sports training endurance sports intelligent data analysis machine learning artificial intelligence computational intelligence systematic literature review pametni športni treningi vzdržljivostni športi inteligentna analiza podatkov strojno učenje umetna inteligenca računalniška inteligenca sistematični pregled literature true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/applsci © 2025 by the authors Članek v reviji 2025-10-02 14:27:38 2025-10-02 14:27:39 2025-10-03 03:32:52 0000-00-00 00:00:00 2025 0 0 28 str. iss. 18, [article no.] 10158 Vol. 15 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-03-04 2025-08-30 2025-09-17 004.8 250133507 10.3390/app151810158 2076-3417 applsci-15-10158-v2_(1).pdf applsci-15-10158-v2_(1).pdf 1 59576C17556F652FECD5A88DAE2805B5 c22341569402612cca4f450b078bffbd08724759c69176742ffc44470970bae1 6c66e8cc-9f8b-11f0-9873-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=199524 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0