A2 Review article, Literature review, Systematic review
Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review (2022)


Ashfaq, A., Cronin, N., & Müller, P. (2022). Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review. Informatics in Medicine Unlocked, 28, Article 100863. https://doi.org/10.1016/j.imu.2022.100863


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Publication details

All authors or editorsAshfaq, Atiqa; Cronin, Neil; Müller, Philipp

Journal or seriesInformatics in Medicine Unlocked

ISSN2352-9148

eISSN2352-9148

Publication year2022

Volume28

Article number100863

PublisherElsevier

Publication countryNetherlands

Publication languageEnglish

DOIhttps://doi.org/10.1016/j.imu.2022.100863

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/79610


Abstract

Maximal oxygen uptake ( max) is the maximum amount of oxygen attainable by a person during exercise. max is used in different domains including sports and medical sciences and is usually measured during an incremental treadmill or cycle ergometer test. The drawback of directly measuring max using the maximal test is that it is expensive and requires a fixed and controlled protocol. During the last decade, various machine learning models have been developed for max prediction and numerous studies have attempted to predict max using data from submaximal and non-exercise tests. This article gives an overview of the machine learning models developed over the past five years (2016–2021) for the prediction of max. Multiple linear regression, support vector machine, artificial neural network and multilayer perceptron are some of the techniques that have been used to build predictive models using different combinations of predictor variables. Model performance is generally assessed using correlation coefficient (R-value), standard error of estimate (SEE) and root mean squared error (RMSE), computed between ground truth and predicted values. The findings of this review indicate that models using ANN typically outperform other machine learning techniques. Moreover, the predictor variables used to build the model have a large influence on the model's predictive performance.


Keywordsmaximal oxygen uptakephysical fitnessmeasuring technologyfitness testsforecastsmodelling (representation)machine learningneural networks (information technology)

Free keywordsMaximal oxygen uptake (VO2 max); Graded exercise tests; Artificial neural network; Prediction models; Error metrics


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Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-03-04 at 17:26