Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.

Maximal oxygen uptake (VO2max) is often used to assess an individual's cardiorespiratory fitness. However, measuring this variable requires an athlete to perform a maximal exercise test which may be impractical, since this test requires trained staff and specialized equipment, and may be hard t...

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Main Authors: Arne De Brabandere, Tim Op De Beéck, Kurt H Schütte, Wannes Meert, Benedicte Vanwanseele, Jesse Davis
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC6025864?pdf=render
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spelling doaj-caea8ac734d14e04937558d1be5ba2962020-11-25T02:33:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01136e019950910.1371/journal.pone.0199509Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.Arne De BrabandereTim Op De BeéckKurt H SchütteWannes MeertBenedicte VanwanseeleJesse DavisMaximal oxygen uptake (VO2max) is often used to assess an individual's cardiorespiratory fitness. However, measuring this variable requires an athlete to perform a maximal exercise test which may be impractical, since this test requires trained staff and specialized equipment, and may be hard to incorporate regularly into training programs. The aim of this study is to develop a new model for predicting VO2max by exploiting its relationship to heart rate and accelerometer features extracted during submaximal running. To do so, we analyzed data collected from 31 recreational runners (15 men and 16 women) aged 19-26 years who performed a maximal incremental test on a treadmill. During this test, the subjects' heart rate and acceleration at three locations (the upper back, the lower back and the tibia) were continuously measured. We extracted a wide variety of features from the measurements of the warm-up and the first three stages of the test and employed a data-driven approach to select the most relevant ones. Furthermore, we evaluated the utility of combining different types of features. Empirically, we found that combining heart rate and accelerometer features resulted in the best model with a mean absolute error of 2.33 ml ⋅ kg-1 ⋅ min-1 and a mean absolute percentage error of 4.92%. The model includes four features: gender, body mass, the inverse of the average heart rate and the inverse of the variance of the total tibia acceleration during the warm-up stage of the treadmill test. Our model provides a practical tool for recreational runners in the same age range to estimate their VO2max from submaximal running on a treadmill. It requires two body-worn sensors: a heart rate monitor and an accelerometer positioned on the tibia.http://europepmc.org/articles/PMC6025864?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Arne De Brabandere
Tim Op De Beéck
Kurt H Schütte
Wannes Meert
Benedicte Vanwanseele
Jesse Davis
spellingShingle Arne De Brabandere
Tim Op De Beéck
Kurt H Schütte
Wannes Meert
Benedicte Vanwanseele
Jesse Davis
Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.
PLoS ONE
author_facet Arne De Brabandere
Tim Op De Beéck
Kurt H Schütte
Wannes Meert
Benedicte Vanwanseele
Jesse Davis
author_sort Arne De Brabandere
title Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.
title_short Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.
title_full Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.
title_fullStr Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.
title_full_unstemmed Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.
title_sort data fusion of body-worn accelerometers and heart rate to predict vo2max during submaximal running.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2018-01-01
description Maximal oxygen uptake (VO2max) is often used to assess an individual's cardiorespiratory fitness. However, measuring this variable requires an athlete to perform a maximal exercise test which may be impractical, since this test requires trained staff and specialized equipment, and may be hard to incorporate regularly into training programs. The aim of this study is to develop a new model for predicting VO2max by exploiting its relationship to heart rate and accelerometer features extracted during submaximal running. To do so, we analyzed data collected from 31 recreational runners (15 men and 16 women) aged 19-26 years who performed a maximal incremental test on a treadmill. During this test, the subjects' heart rate and acceleration at three locations (the upper back, the lower back and the tibia) were continuously measured. We extracted a wide variety of features from the measurements of the warm-up and the first three stages of the test and employed a data-driven approach to select the most relevant ones. Furthermore, we evaluated the utility of combining different types of features. Empirically, we found that combining heart rate and accelerometer features resulted in the best model with a mean absolute error of 2.33 ml ⋅ kg-1 ⋅ min-1 and a mean absolute percentage error of 4.92%. The model includes four features: gender, body mass, the inverse of the average heart rate and the inverse of the variance of the total tibia acceleration during the warm-up stage of the treadmill test. Our model provides a practical tool for recreational runners in the same age range to estimate their VO2max from submaximal running on a treadmill. It requires two body-worn sensors: a heart rate monitor and an accelerometer positioned on the tibia.
url http://europepmc.org/articles/PMC6025864?pdf=render
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