Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review
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
Julkaistu sarjassa
Informatics in Medicine UnlockedPäivämäärä
2022Tekijänoikeudet
© 2022 The Authors. Published by Elsevier Ltd.
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.
...
Julkaisija
ElsevierISSN Hae Julkaisufoorumista
2352-9148Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/104091151
Metadata
Näytä kaikki kuvailutiedotKokoelmat
- Liikuntatieteiden tiedekunta [2920]
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Akatemiahanke, SALisätietoja rahoituksesta
This work was supported in part by the Academy of Finland, grants 323472 and 323473 (under consortium “GaitMaven: Machine learning for gait analysis and performance prediction”).Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network
Davidson, Pavel; Trinh, Huy; Vekki, Sakari; Müller, Philipp (MDPI AG, 2023)Oxygen uptake (V̇O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant ... -
Precision exercise medicine : predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
Joensuu, Laura; Rautiainen, Ilkka; Äyrämö, Sami; Syväoja, Heidi J; Kauppi, Jukka-Pekka; Kujala, Urho M; Tammelin, Tuija H (BMJ Publishing Group, 2021)Objectives: To assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier). Methods: ... -
Channel Increment Strategy-Based 1D Convolutional Neural Networks for Seizure Prediction Using Intracranial EEG
Wang, Xiaoshuang; Zhang, Chi; Kärkkäinen, Tommi; Chang, Zheng; Cong, Fengyu (Institute of Electrical and Electronics Engineers (IEEE), 2023)The application of intracranial electroencephalogram (iEEG) to predict seizures remains challenging. Although channel selection has been utilized in seizure prediction and detection studies, most of them focus on the ... -
Effects of exercise training on maximal oxygen uptake in heart failure : a systematic review and meta-analysis
Kaski, Hanna (2012)SUMMARY Aims. Low cardiorespiratory fitness is a common physical status among patients with heart failure. Several studies have examined the effects of exercise training on maximal oxygen uptake (VO2max) in heart ... -
Validity of Estimating the Maximal Oxygen Consumption by Consumer Wearables : A Systematic Review with Meta-analysis and Expert Statement of the INTERLIVE Network
Molina-Garcia, Pablo; Notbohm, Hannah L.; Schumann, Moritz; Argent, Rob; Hetherington-Rauth, Megan; Stang, Julie; Bloch, Wilhelm; Cheng, Sulin; Ekelund, Ulf; Sardinha, Luis B.; Caulfield, Brian; Brønd, Jan Christian; Grøntved, Anders; Ortega, Francisco B. (Springer, 2022)Background Technological advances have recently made possible the estimation of maximal oxygen consumption (VO2max) by consumer wearables. However, the validity of such estimations has not been systematically summarized ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.