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Exploiting sensor data in professional road cycling: personalized data-driven approach for frequent fitness monitoring

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Abstract

We present a personalized approach for frequent fitness monitoring in road cycling solely relying on sensor data collected during bike rides and without the need for maximal effort tests. We use competition and training data of three world-class cyclists of Team Jumbo–Visma to construct personalised heart rate models that relate the heart rate during exercise to the pedal power signal. Our model captures the non-trivial dependency between exertion and corresponding response of the heart rate, which we show can be effectively estimated by an exponential kernel. To construct the daily heart rate models that are required for day-to-day fitness estimation, we aggregate all sessions in the previous week and apply sampling. On average, the explained variance of our models is 0.86, which we demonstrate is more than twice as large as for models that ignore the temporal integration involved in the heart’s response to exercise. We show that the fitness of a cyclist can be monitored by tracking developments of parameters of our heart rate models. In particular, we monitor the decay constant of the kernel involved, and also analytically determine virtual aerobic and anaerobic thresholds. We demonstrate that our findings for the virtual anaerobic threshold on average agree with the results of exercise tests. We believe this work is an important step forward in performance optimization by opening up avenues for switching to adaptive training programs that take into account the current physiological state of an athlete.

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Notes

  1. Although this is the most common pattern for healthy persons, different relationship are also observed. For example, there could be a linear relationship for the entire range of exercise intensities or even an upward inflection at large power outputs (Hofmann et al. 1997).

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Acknowledgements

The research leading to these results received funding from ZonMW under project WielerFitheid, winner of the National SportInnovator Prize 2020.

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Author notes

  1. Mathieu Heijboer, Tim Verdonck, Arno Knobbe and Steven Latré have contributed equally to this work.

Authors and Affiliations

  1. University of Antwerp - imec, Sint-Pietersvliet 7, 2000, Antwerp, Belgium

    Arie-Willem de Leeuw, Tim Verdonck & Steven Latré

  2. Team Jumbo-Visma, Het Zuiderkruis 23, 5215, MV, ’s-Hertogenbosch, The Netherlands

    Mathieu Heijboer

  3. LIACS, Leiden University, Niels Bohrweg 1, 2333, CA, Leiden, The Netherlands

    Arno Knobbe

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  1. Arie-Willem de Leeuw
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Correspondence to Arie-Willem de Leeuw.

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The authors have no relevant financial or non-financial interests to disclose.

Ethics and reproducibility

Informed consent of the riders was obtained for using the data in this scientific study and publishing the results. The data are property of Team Jumbo-Visma and therefore cannot be shared publicly. However, peers in road cycling with access to similar power and heart rate data can reproduce all steps in our methodology with the descriptions given in the manuscript.

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de Leeuw, AW., Heijboer, M., Verdonck, T. et al. Exploiting sensor data in professional road cycling: personalized data-driven approach for frequent fitness monitoring. Data Min Knowl Disc 37, 1125–1153 (2023). https://doi.org/10.1007/s10618-022-00905-5

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