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PACSS 2021: Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference pp 61–68Cite as

Generating Weekly Training Plans in the Style of a Professional Swimming Coach Using Genetic Algorithms and Random Trees

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1426)

Abstract

Optimal training planning is a combination of art and science, a time-consuming task that requires expert knowledge. As such, it is often exclusively available to top tier athletes. Many athletes outside the elite do not have access or cannot afford to hire a professional coach to help them create their training plans. In this study, we investigate if it is possible to use the historical training logs of elite swimmers to construct detailed weekly training plans similar to how a specific professional coach would have planned. We present a software system based on machine learning and genetic algorithms for generation of detailed weekly training plans based on desired volume, intensity, training frequency, and athlete characteristics. The system schedules training sessions from a library extracted from training plans written by a professional swimming coach. Results show that the proposed system is able to generate highly accurate training plans in terms of training load, types of sessions, and structure, compared to the human coach.

Keywords

  • Swimming
  • Training planning
  • Training plan generation
  • Machine learning
  • Exercise intelligence

R. Eriksson and J. Nicander—Equally contributed.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Rikard Eriksson, Johan Nicander & Moa Johansson

  2. Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden

    C. Mikael Mattsson

  3. Health and Sports Technology Initiative, Blekinge Institute of Technology, Karlskrona, Sweden

    C. Mikael Mattsson

  4. Silicon Valley Exercise Analytics (Svexa), Menlo Park, CA, USA

    C. Mikael Mattsson

Authors
  1. Rikard Eriksson
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  2. Johan Nicander
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  3. Moa Johansson
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  4. C. Mikael Mattsson
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Corresponding author

Correspondence to Rikard Eriksson .

Editor information

Editors and Affiliations

  1. Institut für Sportwissenschaft Universität Wien, Wien, Austria

    Prof. Arnold Baca

  2. University of Vienna, Wien, Austria

    Prof. Juliana Exel

  3. Faculty of Sports and Health Sciences, Technical University Munich, München, Bayern, Germany

    Martin Lames

  4. Middlesex University, Hendon, UK

    Prof. Nic James

  5. The Burroughs, Middlesex University, Hendon, UK

    Prof. Nimai Parmar

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Eriksson, R., Nicander, J., Johansson, M., Mattsson, C.M. (2022). Generating Weekly Training Plans in the Style of a Professional Swimming Coach Using Genetic Algorithms and Random Trees. In: Baca, A., Exel, J., Lames, M., James, N., Parmar, N. (eds) Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference. PACSS 2021. Advances in Intelligent Systems and Computing, vol 1426. Springer, Cham. https://doi.org/10.1007/978-3-030-99333-7_9

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