Cycling modelling under uncontrolled outdoor conditions using a wearable sensor and different meteorological measurement methods

Abstract

The aim of this study was to model the cycling displacement under uncontrolled outdoor conditions with a wearable sensor and different meteorological measurement methods. One participant completed eight courses of a distance of 9.2 ± 2.4 km with varied profiles and directions. Data were recorded every second with a power meter, a GPS and a speed sensor. The aerodynamic drag coefficient, measured by a Notio wearable sensor, and the meteorological variables provided by the Notio, a Kestrel fixed meteorological station and the OpenWeather website were integrated into the Martin mathematical model to calculate the theoretical power output. The power calculated by the model on the basis of data from Notio, Kestrel and OpenWeather were, respectively, 1 ± 4 W higher, 7 ± 15 W lower and 67 ± 111 W higher than the power measured by the sensor. The overall RMSE and R2, including 7325 data points, were 12.8 W and 0.77 ( p < 0.001), respectively, between the power output measured by the sensor and the power output modelled with the data from Notio. The use of the model with the wearable sensor was more precise mainly due to the relative wind measures at all points of the course. Therefore, the Notio can be useful for coaches to follow the evolution of the CdA of athletes on the field. Moreover, the model has the potential to predict the time of a cyclist just before a time trial in order to optimise his pacing strategy taking into account actual weather conditions.