Abstract
Measurement of oxygen uptake during exercise () is currently non-accessible to most individuals without expensive and invasive equipment. The goal of this pilot study was to estimate cycling 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 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 response from easy-to-obtain inputs across a wide range of cycling intensities.
Highlights
Aerobic metabolism, measured universally via oxygen uptake (V_ O2) [1], is the principal mechanism by which humans generate energy from ingested foodstuffs for life
We hypothesized that a recurrent neural network approach could be successfully used to accurately predict individual cycling V_O2 data from easy-to-collect inputs [21]
In their work, no past input values are passed to the neural network, and the heart rate dynamics is only considered by means of its “time derivative”
Summary
Aerobic metabolism, measured universally via oxygen uptake (V_ O2) [1], is the principal mechanism by which humans generate energy from ingested foodstuffs for life. To assess the prediction ability of the different models, a residual analysis was conducted. Residuals were calculated as the difference between the experimental V_ O2 values and the output V_ O2 values predicted by the models. A Bland-Altman analysis [45] was used to assess the level of agreement between measured and predicted data. The confidence limits of the mean bias were calculated with the significance level set to 0.05. Best practice suggested we define a priori a significant and meaningful level of maximal acceptable limits. This limit was set to 200 mlO2min-1, which, in our experience, is comparable with the magnitude of the typical noise underlying V_ O2 measurements at high exercise intensities
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