A Short Review on the Machine Learning-Guided Oxygen Uptake Prediction for Sport Science Applications

Abstract

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-exercise predictive models also were discussed.

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

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Cross-Domain Classification of Physical Activity Intensity: An EDA-Based Approach Validated by Wrist-Measured Acceleration and Physiological Data

“…As mentioned above, among the most common ML algorithms used to predict the PA intensity and based on literature analysis [43][44][45], the SVM and BT were selected to test the performance of a supervised learning approach. This approach is a learning tool, facilitating the classification processes, which maps each input to a specific output variable [46], hence, creating the model based on the relationships between the desired output and the input features, and then making predictions of the response values for a new unknown dataset.…”