Machine Learning Regressors to Estimate Continuous Oxygen Uptakes (VO2)

Abstract

Oxygen consumption ((Formula presented.)) estimation is vital for evaluating aerobic performance and cardiovascular fitness. This study explores various regression models to develop a real-time (Formula presented.) and (Formula presented.) 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 (Formula presented.) estimation model. After adjusting parameters to (Formula presented.) in “mL/kg/min” from “mL/min”, and selecting ‘Age’, ‘Weight’, ‘Height’, ‘HR’, ‘Sex’, and ‘Time’ as parameters for (Formula presented.) 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 ((Formula presented.)), 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, (Formula presented.) 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 (Formula presented.) estimation model that has promising performance metrics.