Advertisement

Running Sports Decision Aid Tool Based on Reinforcement Learning Approach

  • Rafał KozikEmail author
  • Joanna Morzyńska
  • Michał Choraś
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 681)

Abstract

Recently, an increasing number of people is interested in healthy lifestyle and gets involved in a variety of sports activities such as jogging, nordic walking, cycling, hiking or swimming. In order to better plan and track everyday training, people use a wide variety of smart mobile systems on their smartphones, smartbands and smartwatches. However, many of training mobile systems have their functional limitations and in many cases do not allow the user to perform in-depth data analysis in order to optimise the training. Therefore, in this paper, we propose adaptive decision aid tool that supports running sports practitioners in their daily training activities. This tool aims at suggesting the user the strategies for next training session (e.g. the maximum number of training days that can be skipped, duration of the training, the distance that should be covered) in order to meet the goals (in the experiments we have considered winning the one-year running competition). From the scientific standpoint, the tool adapts reinforcement learning in order to propose the runner suggestions that will allow for improving performance.

Keywords

User support Reinforcement learning Markov decision process Running sports Decision aid 

References

  1. 1.
    Endomondo training system (2017). https://www.endomondo.com/
  2. 2.
    Donciu, M., Ionita, M., Dascalu, M., Trausan-Matu, S.: The runner-recommender system of workout and nutrition for runners. In: 2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 230–238. IEEE (2011)Google Scholar
  3. 3.
    López-Matencio, P., Alonso, J.V., González-Castano, F., Sieiro, J., Alcaraz, J.: Ambient intelligence assistant for running sports based on k-nn classifiers. In: 2010 3rd Conference on Human System Interactions (HSI), pp. 605–611. IEEE (2010)Google Scholar
  4. 4.
    Schmidt, B., Wozniak, P., Knaving, K., Chen, C., Obaid, M.: ParkPal: towards ad-hoc route planning for runners. In: CoPDA@AVI, pp. 23–27 (2014)Google Scholar
  5. 5.
    Sidhu, G., Caffo, B., et al.: MONEYBaRL: exploiting pitcher decision-making using reinforcement learning. Ann. Appl. Stat. 8(2), 926–955 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Vales-Alonso, J., López-Matencio, P., Alcaraz, J., Sieiro-Lomba, J., Costa-Montenegro, E., González-Castaño, F.: A dynamic programming approach for ambient intelligence platforms in running sports based on Markov decision processes. In: Human-Computer Systems Interaction: Backgrounds and Applications, vol. 2, pp. 165–181. Springer (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Rafał Kozik
    • 1
    Email author
  • Joanna Morzyńska
    • 1
  • Michał Choraś
    • 1
  1. 1.Institute of Telecommunications and Computer ScienceUTP University of Science and Technology in BydgoszczBydgoszczPoland

Personalised recommendations