Prediction of oxygen uptake kinetics during heavy-intensity cycling exercise by machine learning analysis-Reference-Cited by-同舟云学术

Prediction of oxygen uptake kinetics during heavy-intensity cycling exercise by machine learning analysis

Published:2023-06-01 Issue:6 Volume:134 Page:1530-1536
ISSN:8750-7587
Container-title:Journal of Applied Physiology
language:en
Short-container-title:Journal of Applied Physiology

Author:

Hedge Eric T.¹²^ORCID,Amelard Robert¹^ORCID,Hughson Richard L.¹^ORCID

Affiliation:

1. Schlegel-UW Research Institute for Aging, Waterloo, Ontario, Canada

2. Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada

Abstract

Machine learning analysis of wearable sensor data with a sequential model, which utilized a receptive field of approximately 3 min to make instantaneous oxygen uptake estimations, accurately predicted oxygen uptake kinetics from moderate through to higher-intensity exercise. This innovation will enable nonintrusive cardiorespiratory monitoring over a wide range of exercise intensities encountered in vigorous training and competitive sports.

Funder

Gouvernement du Canada | Canadian Institutes of Health Research

Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada

Publisher

American Physiological Society

Subject

Physiology (medical),Physiology

Link

https://journals.physiology.org/doi/pdf/10.1152/japplphysiol.00148.2023

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1. Faster adjustment to and recovery from submaximal exercise in the trained state

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