Estimating an individual’s oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study

Andrea Zignoli, Alessandro Fornasiero, Matteo Ragni, Barbara Pellegrini, Federico Schena, Francesco Biral and Paul B Laursen

PLOS ONE, 2020, vol. 15, issue 3, 1-15

Abstract: Measurement of oxygen uptake during exercise (V˙O2) is currently non-accessible to most individuals without expensive and invasive equipment. The goal of this pilot study was to estimate cycling V˙O2 from easy-to-obtain inputs, such as heart rate, mechanical power output, cadence and respiratory frequency. To this end, a recurrent neural network was trained from laboratory cycling data to predict V˙O2 values. Data were collected on 7 amateur cyclists during a graded exercise test, two arbitrary protocols (Prot-1 and -2) and an “all-out” Wingate test. In Trial-1, a neural network was trained with data from a graded exercise test, Prot-1 and Wingate, before being tested against Prot-2. In Trial-2, a neural network was trained using data from the graded exercise test, Prot-1 and 2, before being tested against the Wingate test. Two analytical models (Models 1 and 2) were used to compare the predictive performance of the neural network. Predictive performance of the neural network was high during both Trial-1 (MAE = 229(35) mlO2min-1, r = 0.94) and Trial-2 (MAE = 304(150) mlO2min-1, r = 0.89). As expected, the predictive ability of Models 1 and 2 deteriorated from Trial-1 to Trial-2. Results suggest that recurrent neural networks have the potential to predict the individual V˙O2 response from easy-to-obtain inputs across a wide range of cycling intensities.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0229466

DOI: 10.1371/journal.pone.0229466

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