Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network
By:
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
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
Language: English
Page range: 60 - 68
Submitted on: Sep 16, 2018
Accepted on: Mar 18, 2019
Published on: Mar 26, 2019
Published by: University of Physical Education in Warsaw
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Related subjects:
© 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.