Machine learning prediction of pulmonary oxygen uptake from muscle oxygen in cycling

ABSTRACT

The purpose of this study was to test whether a machine learning model can accurately predict VO₂ across different exercise intensities by combining muscle oxygen (MO₂) with heart rate (HR). Twenty young highly trained athletes performed the following tests: a ramp incremental exercise, three submaximal constant intensity exercises, and three severe intensity exhaustive exercises. A Machine Learning model was trained to predict VO₂, with model inputs including heart rate, MO₂ in the left (LM) and right legs (RM). All models demonstrated equivalent results, with the accuracy of predicting VO₂ at different exercise intensities varying among different models. The LM+RM+HR model performed the best across all intensities, with low bias in predicted VO₂ for all intensity exercises (0.08 ml/kg/min, 95% limits of agreement: −5.64 to 5.81), and a very strong correlation (r = 0.94, p < 0.001) with measured VO₂. Furthermore, the accuracy of predicting VO₂ using LM+HR or RM+HR was higher than using LM+RM, and higher than the accuracy of predicting VO₂ using LM, RM, or HR alone. This study demonstrates the potential of a machine learning model combining MO₂ and HR to predict VO₂ with minimal bias, achieving accurate predictions of VO₂ for different intensity levels of exercise.

KEYWORDS:

• Aerobic exercise
• energy expenditure
• wearable sensors
• oxyhaemoglobin
• deoxyhemoglobin

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.