Sensor-Based Performance Monitoring in Track Cycling

Steyaert, Michaël; Bock, Jelle De; Verstockt, Steven

doi:10.1007/978-3-031-02044-5_14

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Recently, sensor data is also used for performance monitoring in cycling. Several machine learning techniques have been applied to construct a recommendation module for increasing the cycling performance (Demosthenous et al 2022) and automatic handslings performance monitoring is studied during a madison in track cycling (Steyaert et al 2022). Whereas these studies focus on specific performance measures, e.g., an increased speed with barely affecting the heart rate, we here consider fitness monitoring by extracting physiological characteristics of riders from sensor data.…”

Section: Introductionmentioning

confidence: 99%

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

Leeuw

Heijboer²,

Verdonck

et al. 2022

Data Min Knowl Disc

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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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Section: Introductionmentioning

confidence: 99%