Key result
Recurrent neural network predicts cycling VO2 with ~35% lower error than traditional analytical models.
Why the study?
Measurement of oxygen uptake during exercise is non-accessible to most individuals without expensive and invasive equipment.
Does a recurrent neural network accurately predict individual oxygen uptake during cycling exercise from easy-to-obtain inputs in recreational cyclists?
Observational (n=7)
No
Does a recurrent neural network accurately predict individual oxygen uptake during cycling exercise from easy-to-obtain inputs in recreational cyclists?
Absolute Event Rate: 229% vs 355%
A recurrent neural network can accurately predict individual oxygen uptake during high-intensity cycling exercise using easy-to-obtain inputs like heart rate and power output.
Supports RNN-based VO2 prediction in small pilot; hypothesis-generating and requires larger validation before clinical use.
Measurement of oxygen uptake during exercise ([Formula: see text]) is currently non-accessible to most individuals without expensive and invasive equipment. The goal of this pilot study was to estimate cycling [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] response from easy-to-obtain inputs across a wide range of cycling intensities.
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Zignoli et al. (2020) conducted an observational in Healthy recreational cyclists (n=7). Recurrent neural network (RNN) vs. Analytical models (first-order and higher-order models) was evaluated on Mean Absolute Error (MAE) of VO2 prediction in Trial 1 (mlO2/min). A recurrent neural network accurately predicted individual oxygen uptake during cycling exercise from easy-to-obtain inputs (MAE = 229 mlO2/min, r = 0.94), outperforming traditional analytical models.
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