1. The role of intelligent data analysis in selected endurance sports : a systematic literature reviewAlen Rajšp, Patrik Rek, Peter Kokol, Iztok Fister, 2025, review article 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.
Keywords: smart sports training, endurance sports, intelligent data analysis, machine learning, artificial intelligence, computational intelligence, systematic literature review
Published in DKUM: 02.10.2025; Views: 0; Downloads: 6
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2. New perspectives in the development of the artificial sport trainerIztok Fister, Sancho Salcedo-Sanz, Andres Iglesias, Dušan Fister, Akemi Gálvez, Iztok Fister, 2021, original scientific article Abstract: The rapid development of computer science and telecommunications has brought new
ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually
suffers from a lack of automatisation in realization and control phases of the training. In this study,
the Digital Twin is proposed as a framework for helping athletes, during realization of training
sessions, to make the proper decisions in situations they encounter. The digital twin for artificial
sport trainer is based on the cognitive model of humans. This concept has been applied to cycling,
where a version of the system on a Raspberry Pi already exists. The results of porting the digital twin
on the mentioned platform shows promising potential for its extension to other sport disciplines.
Keywords: artificial sport trainer, digital twin, cognitive models, computational intelligence
Published in DKUM: 20.06.2025; Views: 0; Downloads: 9
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3. Computer aided decision support in product design engineeringMarina Novak, 2012, original scientific article Abstract: Product design engineering is a complex discipline, which is undergoing a transformation from informal and largely experience-based domain to scientific oriented domain. Computational intelligence can contribute greatly to product design process, as it is becoming more and more evident that adding intelligence to existing computer aids, such as computer aided design systems, can lead to significant improvements in terms of effectiveness and reliability of various tasks within product design engineering. Providing computer aided decision support is one of the computational intelligence methods that proved to be effective in enabling more intelligent and less experience-dependent design performance. In this paper, some of the most crucial areas of product design engineering process that require additional computational intelligence in terms of computer aided decision support are presented together with some examples of intelligent knowledge-based modules applied to this areas.
Keywords: product development, design engineering, design for X, computational intelligence, decision support, knowledge-based modules
Published in DKUM: 10.07.2015; Views: 1913; Downloads: 106
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