Designing an e-Coach to Tailor Training Plans for Road Cyclists

  • Alessandro SilacciEmail author
  • Omar Abou Khaled
  • Elena Mugellini
  • Maurizio Caon
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1026)


Athletes seek to constantly improve their performances pushing their limits and overwork is often the direct consequence of this behavior. This is not a common problem for professionals, who usually are followed by a coach, but it is a growing phenomenon in amateur sports, which drives people to get injured because of overtraining or incorrect movements. Meantime, advances in artificial intelligence enabled the creation of new tools increasingly capable of understanding the complexity of our world. We therefore propose a novel e-coaching system for road cycling athletes, able to automatically follow and tailor their training plans. This paper describes the design of the machine learning algorithm, its model based on reinforcement learning and the metrics that were adopted for the scoring system. Finally, we report our tests, which show that the virtual coach already can compete with human experts in making a proper personalized training plan.


Road cycling Artificial intelligence Virtual coach e-Coaching Training plan 


  1. 1.
    Fister, 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). Scholar
  2. 2.
    Eyck, A.: Effect of a virtual coach on athletes’ motivation. In: IJsselsteijn, W.A., de Kort, Y.A.W., Midden, C., Eggen, B., van den Hoven, E. (eds.) Persuasive Technology, pp. 158–161. Springer, Heidelberg (2006)Google Scholar
  3. 3.
    Bonants, R., Kort, Y.D., Ijsselsteijn, W.: J. Westerink, M. de Jager, J. van HerkGoogle Scholar
  4. 4.
    Banos, O., Hermens, H., Nugent, C., Pomares, H.: Smart sensing technologies for personalised e-coaching. Sensors 18, 1751 (2018). Scholar
  5. 5.
    Aranki, D., Peh, G.X., Kurillo, G., Bajcsy, R.: The feasibility and usability of runningcoach: a remote coaching system for long-distance runners. Sensors. 18 (2018). Scholar
  6. 6.
    Rauter, S.: New approach for planning the mountain bike training with virtual coach. Trends Sport Sci. 69–74 (2018).
  7. 7.
    Rodríguez-Marroyo, J.A., Villa, G., García-López, J., Foster, C.: Comparison of heart rate and session rating of perceived exertion methods of defining exercise load in cyclists. J. Strength Cond. Res. 26, 2249–2257 (2012). Scholar
  8. 8.
    Pentland, A., Liu, A.: Modeling and prediction of human behavior. Neural Comput. 11, 229–242 (1999). Scholar
  9. 9.
    Allen, H., Coggan, A.: Training and Racing with a Power Meter. VeloPress, Boulder (2010)Google Scholar
  10. 10.
    Strava | Run and Cycling Tracking on the Social Network for Athletes.
  11. 11.
    Pavlov, I.P.: Lectures on conditioned reflexes. In: Conditioned Reflexes and Psychiatry, vol. II. International Publishers, New York (1941)Google Scholar
  12. 12.
    McLeod, S.: Skinner - Operant Conditioning, 12Google Scholar
  13. 13.
    Hausknecht, M., Stone, P.: Deep Recurrent Q-Learning for Partially Observable MDPs, 9Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alessandro Silacci
    • 1
    Email author
  • Omar Abou Khaled
    • 1
  • Elena Mugellini
    • 1
  • Maurizio Caon
    • 1
  1. 1.HES-SO, University of Applied Sciences and Arts Western SwitzerlandMontreuxSwitzerland

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