Abstract
Automatic planning of sport training sessions with Swarm Intelligence algorithms has been proposed recently in the scientific literature that influences the sports training process in practice dramatically. These algorithms are capable of generating sophisticated training plans based on an archive of the existing sports training sessions. In recent years, training plans have been generated for various sport disciplines, like road cycling, mountain biking, running. These plans have also been verified by professional sport trainers confirming that the proposed training plans correspond with the theory of sports training. Unfortunately, not enough devotion has been given to adapting the generated sports training plans due to the changing conditions that may occur frequently during their realization and causes a break in continuity of the sports training process. For instance, athletes involved in the training process can become ill or injured. These facts imply disruption of the systematic increase of the athlete’s capacity. In this paper, therefore, we propose a novel solution that is capable of adapting training plans due to the absence of an athlete from the training process.
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References
Banister, E.: Modeling elite athletic performance. Physiol. Test. Elite Athletes 403, 403–424 (1991)
Dick, F.W.: Sports Training Principles: An Introduction to Sports Science, 6th edn. Bloomsbury Sport, London (2015)
Fister Jr., I., Fister, I.: Generating the training plans based on existing sports activities using swarm intelligence. In: Nakamatsu, K., Patnaik, S., Yang, X.S. (eds.) Nature-Inspired Computing and Optimization: Theory and Applications, pp. 79–94. Springer International Publishing, Switzerland (2017)
Fister Jr., I., Ljubič, K., Suganthan, P.N., Perc, M., Fister, I.: Computational intelligence in sports: challenges and opportunities within a new research domain. Appl. Math. Comput. 262, 178–186 (2015)
Fister Jr., I., Yang, X.-S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. Elektroteh. Vestn. 80(3), 116–122 (2013)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: 1995 Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Omalu, B.I., DeKosky, S.T., Minster, R.L., Kamboh, M.I., Hamilton, R.L., Wecht, C.H.: Chronic traumatic encephalopathy in a national football league player. Neurosurgery 57(1), 128–134 (2005)
Søvik, M.L.: Evaluating the implementation of the empowering coaching program in Norway. Ph.D thesis (2017)
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Fister, I., Iglesias, A., Osaba, E., Mlakar, U., Brest, J., Fister, I. (2019). Adaptation of Sport Training Plans by Swarm Intelligence. In: Matoušek, R. (eds) Recent Advances in Soft Computing . MENDEL 2017. Advances in Intelligent Systems and Computing, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-319-97888-8_5
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DOI: https://doi.org/10.1007/978-3-319-97888-8_5
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