The rapid transformation of our communities and our way of life due to modern technologies has impacted sports as well. Artificial intelligence, computational intelligence, data mining, the Internet of Things (IoT), and machine learning have had a profound effect on the way we do things. These technologies have brought changes to the way we watch, play, compete, and also train sports. What was once simply training is now a combination of smart IoT sensors, cameras, algorithms, and systems just to achieve a new peak: The optimum one. This paper provides a systematic literature review of smart sport training, presenting 109 identified studies. Intelligent data analysis methods are presented, which are currently used in the field of Smart Sport Training (SST). Sport domains in which SST is already used are presented, and phases of training are identified, together with the maturity of SST methods. Finally, future directions of research are proposed in the emerging field of SST.

We present a novel method for generating cycling training routes from geographical property graphs based on an Evolutionary Algorithm. The algorithm operators of crossover and mutation are adjusted for use in the Property Graph domain. Data fusion of geographical data from the OpenStreetMap, EU-DEM digital surface model, and existing training records is performed as a basis of the intersections-paths property graph. The proposed approach allows route generation based on their starting and ending points in the property graph and their distance and ascent. A property graph of all intersections and cycling roads is shown and generated for the regions of Podravje and Pomurje. The property graph used in the proposed algorithm’s feasibility demonstration is shown. This is done by presenting four different cases of routes generated with our algorithm. The algorithm allows for generating classic cycling routes of A to B nature, routes with more than two fixed points, and cyclical training routes. The research is concluded by offering further directions on route generation research.

Navigating through a real-world map can be represented in a bi-directed graph with a group of nodes representing the intersections and edges representing the roads between them. In cycling, we can plan training as a group of nodes and edges the athlete must cover. Optimizing routes using artificial intelligence is a well-studied phenomenon. Much work has been done on finding the quickest and shortest paths between two points. In cycling, the solution is not necessarily the shortest and quickest path. However, the optimum path is the one where a cyclist covers the suitable distance, ascent, and descent based on his/her training parameters. This paper presents a Neo4j graph-based dataset of cycling routes in Slovenia. It consists of 152,659 nodes representing individual road intersections and 410,922 edges representing the roads between them. The dataset allows the researchers to develop and optimize cycling training generation algorithms, where distance, ascent, descent, and road type are considered.

In endurance sports, athletes and coaches shift increasingly from intuition-based decision-making 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.