<abstract xmlns="http://www.w3.org/1999/xhtml"><italic>Study aim</italic>: Oxygen Uptake (VO2) is avaluable metric for the prescription of exercise intensity and the monitoring of training progress. However, VO2 is difficult to assess in anon-laboratory setting. Recently, an artificial neural network (ANN) was used to predict VO2 responses during aset walking protocol on the treadmill [9]. The purpose of the present study was to test the ability of an ANN to predict VO2 responses during cycling at self-selected intensities using Heart Rate (HR), time derivative of HR, power output, cadence, and body mass data.<italic>Material and methods</italic>: 12 moderately-active adult males (age: 21.1 ± 2.5 years) performed a50-minute bout of cycling at a variety of exercise intensities. VO2, HR, power output, and cadence were recorded throughout the test. An ANN was trained, validated and tested using the following inputs: HR, time derivative of HR, power output, cadence, and body mass. A twelve-fold hold-out cross validation was conducted to determine the accuracy of the model.<italic>Results</italic>: The ANN accurately predicted the experimental VO2 values throughout the test (R2 = 0.91 ± 0.04, SEE = 3.34 ± 1.07 mL/kg/min).<italic>Discussion</italic>: This preliminary study demonstrates the potential for using an ANN to predict VO2 responses during cycling at varied intensities using easily accessible inputs. The predictive accuracy is promising, especially considering the large range of intensities and long duration of exercise. Expansion of these methods could allow ageneral algorithm to be developed for a more diverse population, improving the feasibility of oxygen uptake assessment.</abstract>

Study aim: Oxygen Uptake (VO2) is avaluable metric for the prescription of exercise intensity and the monitoring of training progress. However, VO2 is difficult to assess in anon-laboratory setting. Recently, an artificial neural network (ANN) was used to predict VO2 responses during aset walking protocol on the treadmill [9]. The purpose of the present study was to test the ability of an ANN to predict VO2 responses during cycling at self-selected intensities using Heart Rate (HR), time derivative of HR, power output, cadence, and body mass data.Material and methods: 12 moderately-active adult males (age: 21.1 ± 2.5 years) performed a50-minute bout of cycling at a variety of exercise intensities. VO2, HR, power output, and cadence were recorded throughout the test. An ANN was trained, validated and tested using the following inputs: HR, time derivative of HR, power output, cadence, and body mass. A twelve-fold hold-out cross validation was conducted to determine the accuracy of the model.Results: The ANN accurately predicted the experimental VO2 values throughout the test (R2 = 0.91 ± 0.04, SEE = 3.34 ± 1.07 mL/kg/min).Discussion: This preliminary study demonstrates the potential for using an ANN to predict VO2 responses during cycling at varied intensities using easily accessible inputs. The predictive accuracy is promising, especially considering the large range of intensities and long duration of exercise. Expansion of these methods could allow ageneral algorithm to be developed for a more diverse population, improving the feasibility of oxygen uptake assessment.

Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network

Department of Mechanical Engineering and Materials Science

Biomedical Human Kinetics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Study aim: Oxygen Uptake (VO2) is avaluable metric for the prescription of exercise intensity and the monitoring of training progress. However, VO2 is...

Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network

Publicado en línea: 26 mar 2019

Páginas: 60 - 68

Recibido: 16 sept 2018

Aceptado: 18 mar 2019

DOI: https://doi.org/10.2478/bhk-2019-0008

Palabras clave
VO, Cardiorespiratory fitness, Heart rate, Machine learning, Prediction model

© 2019 Andrew Borror et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network

Publicado en línea: 26 mar 2019

Páginas: 60 - 68

Recibido: 16 sept 2018

Aceptado: 18 mar 2019

DOI: https://doi.org/10.2478/bhk-2019-0008

Palabras claveVO, Cardiorespiratory fitness, Heart rate, Machine learning, Prediction model

© 2019 Andrew Borror et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
VO, Cardiorespiratory fitness, Heart rate, Machine learning, Prediction model