Advertisement

Adaptation of Sport Training Plans by Swarm Intelligence

  • Iztok FisterJr.
  • Andres Iglesias
  • Eneko Osaba
  • Uroš Mlakar
  • Janez Brest
  • Iztok Fister
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 837)

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.

Keywords

Artificial sport trainer Multisport Sport training Swarm intelligence Running 

References

  1. 1.
    Banister, E.: Modeling elite athletic performance. Physiol. Test. Elite Athletes 403, 403–424 (1991)Google Scholar
  2. 2.
    Dick, F.W.: Sports Training Principles: An Introduction to Sports Science, 6th edn. Bloomsbury Sport, London (2015)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)MathSciNetGoogle Scholar
  5. 5.
    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)MATHGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Søvik, M.L.: Evaluating the implementation of the empowering coaching program in Norway. Ph.D thesis (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Iztok FisterJr.
    • 1
  • Andres Iglesias
    • 3
  • Eneko Osaba
    • 2
  • Uroš Mlakar
    • 1
  • Janez Brest
    • 1
  • Iztok Fister
    • 1
  1. 1.Faculty of electrical engineering and computer scienceUniversity of MariborMariborSlovenia
  2. 2.University of DeustoBilbaoSpain
  3. 3.University of Cantabria, E.T.S.I. Caminos, Canales y PuertosSantanderSpain

Personalised recommendations