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Training Plans Optimization Using Approximation and Visualization of Pareto Frontier

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Proceedings of the 12th International Symposium on Computer Science in Sport (IACSS 2019) (IACSS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1028))

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Abstract

The article presents an approach to the formation of optimal training plans, based on models of performance depending on the training effects and the method of multidimensional multi-criteria optimization - approximation and visualization of the Pareto frontier.

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References

  1. Timme, E.A.: Optimization of training plans. In: All-Russian Scientific and Practical Conference on Sports Science in Children’s and Youth Sports and High Performance Sports, 30 November–2 December 2016. Collection of Materials, pp. 222–225. MCAST, Moscow (2016). (in Russian)

    Google Scholar 

  2. Calvert, T.W., Banister, E.W., Savage, M.V., Bach, T.: A systems model of the effects of training on physical performance. IEEE Trans. Syst. Man Cybern. 6(2), 94–102 (1976)

    Article  Google Scholar 

  3. Busso, T., Candau, R., Lacour, J.R.: Fatigue and fitness modelled from the effects of training on performance. Eur. J. Appl. Physiol. 69, 50–54 (1994)

    Article  Google Scholar 

  4. Perl, J.: PerPot: a metamodel for simulation of load performance interaction. Eur. J. Sport Sci. 1, 1–13 (2001)

    Article  Google Scholar 

  5. Busso, T.: From an indirect response pharmacodynamic model towards a secondary signal model of dose-response relationship between exercise training and physical performance. Sci. Rep. 7, 40422 (2017)

    Article  Google Scholar 

  6. Turner, J.D., Mazzoleni, M.J., Little, J.A., Sequeira, D., Mann, B.P.: A nonlinear model for the characterization and optimization of athletic training and performance. Biomed. Hum. Kinet. 9, 82–93 (2017)

    Article  Google Scholar 

  7. http://or.nsfc.gov.cn/bitstream/00001903-5/422669/1/1000014250749.pdf

  8. Ljung, L., Soederstroem, T.: Theory and Practice of Recursive Identification. Signal Processing, Optimization, and Control. The MIT Press, Cambridge (1983)

    Google Scholar 

  9. Busso, T., Denis, C., Bonnefoy, R., Geyssant, A., Lacour, J.R.: Modeling of adaptations to physical training by using a recursive least squares algorithm. J. Appl. Physiol. (1985) 82, 1685–1693 (1997)

    Article  Google Scholar 

  10. Busso, T., Carasso, C., Lacour, J.R.: Adequacy of a systems structure in the modeling of training effects on performance. J. Appl. Physiol. 71(5), 2044–2049 (1991)

    Article  Google Scholar 

  11. Fitz-Clarke, J.R., Morton, R.H., Banister, E.W.: Optimizing athletic performance by influence curves. J. Appl. Physiol. 71, 1151–1158 (1991)

    Article  Google Scholar 

  12. Thomas, L., Mujika, I., Busso, T.: A model study of optimal training reduction during pre-event taper in elite swimmers. J. Sports Sci. 26, 643–652 (2008)

    Article  Google Scholar 

  13. Schaefer, D., Asteroth, A., Ludwig, M.: Training plan evolution based on training models. In: International Symposium Innovations in Intelligent Systems and Applications (INISTA), Madrid, pp. 1–8. IEEE (2015)

    Google Scholar 

  14. Thomas, L., Busso, T.: A theoretical study of taper characteristics to optimize performance. Med. Sci. Sports Exerc. 37, 1615–1621 (2005)

    Article  Google Scholar 

  15. Kumyaito, N., Yupapin, P., Kreangsak, T.: Personalized sports training plans with physiological constraints using the ε-constraint method with a genetic algorithm. Far East J. Electron. Commun. 17, 475–496 (2017)

    Article  Google Scholar 

  16. Kumyaito, N., Yupapin, P., Tamee, K.: Planning a sports training program using Adaptive Particle Swarm Optimization with emphasis on physiological constraints. BMC Res. Notes 11, 9 (2018)

    Article  Google Scholar 

  17. Lotov, A., Bushenkov, V.A., Kamenev, G.K., O.L., C.: Computer and compromise. In: Method of Achievable Goals. Nauka, Moscow (1997). (in Russian)

    Google Scholar 

  18. Lotov, A.V., Miettinen, K.: Visualizing the Pareto frontier. In: Multiobjective Optimization, pp. 213–243. Springer, Heidelberg (2008)

    Google Scholar 

  19. Podinovskii, V.V., Nogin, V.D.: Pareto-Optimal Solutions of Multicriteria Problems. Nauka, Moscow (1982). (in Russian)

    Google Scholar 

  20. Lotov, A.V., Bushenkov, V.A., Kamenev, G.K.: Interactive Decision Maps: Approximation and Visualization of Pareto Frontier. Springer, Boston (2013)

    MATH  Google Scholar 

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Correspondence to Egor A. Timme .

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Timme, E.A., Dayal, A.A., Kukushkin, Y.A. (2020). Training Plans Optimization Using Approximation and Visualization of Pareto Frontier. In: Lames, M., Danilov, A., Timme, E., Vassilevski, Y. (eds) Proceedings of the 12th International Symposium on Computer Science in Sport (IACSS 2019). IACSS 2019. Advances in Intelligent Systems and Computing, vol 1028. Springer, Cham. https://doi.org/10.1007/978-3-030-35048-2_9

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