A short review on the machine learning‐guided oxygen uptake prediction for sport science applications

In recent years, the rapid improvement in computing facilities combined with that achieved in algorithms and the immense amount of available data led to a great interest in machine learning (ML), which is a subset of artificial intelligence. Nowadays, the ML technique is used mostly in all applications for various purposes, whereby ML will be possible to learn from data, predict, identify patterns, and make decisions. In this regard, the ML was successfully used to predict the oxygen uptake during physical activity without the need for complicated procedures used in the direct measurement. Accordingly, in the present work, the state‐of‐art and recent advances related to the oxygen uptake prediction using ML were presented. Various exercise and non‐exer-cise predictive models also were discussed.