ABSTRACT
Despite the importance of cardiorespiratory fitness, no practical method exists to estimate maximal oxygen consumption (VO2max) without a specific exercise protocol. We developed an estimation model of VO2max, using maximal activity energy expenditure (aEEmax) as a new feature to represent the level of physical activity. Electrocardiogram (ECG) and acceleration data were recorded for 4 days in 24 healthy young men, and reference VO2max levels were measured using the maximal exercise test. aEE was calculated using the measured acceleration data and body weight, while heart rate (HR) was extracted from the ECG signal. aEEmax was obtained using linear regression, with aEE and HR as input parameters. The VO2max was estimated from the aEEmax using multiple linear regression modeling in the training group (n = 16) and was verified in the test group (n = 8). High correlations between the estimated VO2max and the measured VO2max were identified in both groups, with a 15-hour recording being sufficient to produce a highly accurate VO2max estimate. Additional recording time did not significantly improve the accuracy of the estimation. Our VO2max estimation method provides a robust alternative to traditional approaches while only requiring minimal data acquisition time in daily life.
Subject(s)
Cardiorespiratory Fitness/physiology , Adult , Body Weight , Electrocardiography , Energy Metabolism , Exercise Test , Heart Rate , Humans , Linear Models , Male , Oxygen Consumption , Young AdultABSTRACT
Despite the importance of cardiorespiratory fitness, no practical method exists to estimate maximal oxygen consumption (VO₂max) without a specific exercise protocol. We developed an estimation model of VO₂max, using maximal activity energy expenditure (aEEmax) as a new feature to represent the level of physical activity. Electrocardiogram (ECG) and acceleration data were recorded for 4 days in 24 healthy young men, and reference VO₂max levels were measured using the maximal exercise test. aEE was calculated using the measured acceleration data and body weight, while heart rate (HR) was extracted from the ECG signal. aEEmax was obtained using linear regression, with aEE and HR as input parameters. The VO₂max was estimated from the aEEmax using multiple linear regression modeling in the training group (n = 16) and was verified in the test group (n = 8). High correlations between the estimated VO₂max and the measured VO₂max were identified in both groups, with a 15-hour recording being sufficient to produce a highly accurate VO₂max estimate. Additional recording time did not significantly improve the accuracy of the estimation. Our VO₂max estimation method provides a robust alternative to traditional approaches while only requiring minimal data acquisition time in daily life.
