| Title: | The role of intelligent data analysis in selected endurance sports : a systematic literature review |
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| Authors: | ID Rajšp, Alen (Author)
ID Rek, Patrik (Author)
ID Kokol, Peter (Author)
ID Fister, Iztok (Author) |
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| Files: |  applsci-15-10158-v2_(1).pdf (655,98 KB)
MD5: 59576C17556F652FECD5A88DAE2805B5
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.02 - Review Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | 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. |
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| Keywords: | smart sports training, endurance sports, intelligent data analysis, machine learning, artificial intelligence, computational intelligence, systematic literature review |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 04.03.2025 |
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| Article acceptance date: | 30.08.2025 |
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| Publication date: | 17.09.2025 |
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| Publisher: | MDPI |
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| Year of publishing: | 2025 |
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| Number of pages: | 28 str. |
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| Numbering: | Vol. 15, iss. 18, [article no.] 10158 |
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| PID: | 20.500.12556/DKUM-95632  |
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| UDC: | 004.8 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 250133507 |
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| DOI: | 10.3390/app151810158 |
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| Copyright: | © 2025 by the authors |
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| Publication date in DKUM: | 02.10.2025 |
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| Views: | 224 |
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| Downloads: | 12 |
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| Metadata: |    |
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| Categories: | Misc.
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