Machine Learning Regressors to Estimate Continuous Oxygen Uptakes (V˙O2)

Abstract

Oxygen consumption (V˙O2) estimation is vital for evaluating aerobic performance and cardiovascular fitness. This study explores various regression models to develop a real-time V˙O2 and V˙O2max estimation model. Utilizing a dataset from PhysioNet, encompassing cardiorespiratory measurements from 992 treadmill tests conducted at the University of Malaga’s Exercise Physiology and Human Performance Lab from 2008 to 2018, participants aged 10 to 63, including amateur and professional athletes, underwent breath-by-breath monitoring of physiological parameters. The study underlines the efficacy of regressor models in handling complex datasets and developing a robust real-time V˙O2 estimation model. After adjusting parameters to V˙O2 in “mL/kg/min” from “mL/min”, and selecting ‘Age’, ‘Weight’, ‘Height’, ‘HR’, ‘Sex’, and ‘Time’ as parameters for V˙O2 estimation, XGBoost emerged as the optimal choice. Validation using a test dataset of 132 participants yielded the following results for Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), Root Mean Squared Logarithmic Error (RMSLE), and Mean Absolute Percentage Error (MAPE) metrics: MAE of 0.1793, MSE of 0.1460, RMSE of 0.3821, R2 of 0.9991, RMSLE of 0.0140, and MAPE of 0.0066. This study demonstrates the effectiveness of various regressor models in developing a continuous V˙O2max estimation model that has promising performance metrics.