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

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

Biomedical Human Kinetics

Volume 11 (2019): Issue 1 (January 2019)

By: Andrew Borror, Michael Mazzoleni, James Coppock, Brian C. Jensen, William A. Wood, Brian Mann and Claudio L. Battaglini

Open Access

|Mar 2019

Authors

Andrew Borror

Department of Exercise and Sport Science, The University of North Carolina, Chapel Hill

Michael Mazzoleni

Department of Mechanical Engineering and Materials Science, Duke University, Durham

James Coppock

Department of Kinesiology, The University of North Carolina, Greensboro

Brian C. Jensen

Division of Cardiology, The University of North Carolina, Chapel Hill

William A. Wood

Lineberger Comprehensive Cancer Center, The University of North Carolina, Chapel Hill

Brian Mann

Department of Mechanical Engineering and Materials Science, Duke University, Durham

Claudio L. Battaglini

claudio@email.unc.edu

Department of Exercise and Sport Science, The University of North Carolina, Chapel Hill

DOI: https://doi.org/10.2478/bhk-2019-0008 | Journal eISSN: 2080-2234

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Language: English

Page range: 60 - 68

Submitted on: Sep 16, 2018

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Accepted on: Mar 18, 2019

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Published on: Mar 26, 2019

Published by: University of Physical Education in Warsaw

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

VO₂,

Cardiorespiratory fitness,

Machine learning,

Prediction model

Related subjects:

Basic medical science,

Basic medical science, other,

Clinical medicine,

Sports and recreation,

Sports and recreation, other,

Physical education

© 2019 Andrew Borror, Michael Mazzoleni, James Coppock, Brian C. Jensen, William A. Wood, Brian Mann, Claudio L. Battaglini, published by University of Physical Education in Warsaw
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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