Abstract
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.
Citation: De Brabandere A, Op De Beéck T, Schütte KH, Meert W, Vanwanseele B, Davis J (2018) Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running. PLoS ONE 13(6):
e0199509.
https://doi.org/10.1371/journal.pone.0199509
Editor: Alena Grabowski,
University of Colorado Boulder, UNITED STATES
Received: January 2, 2018; Accepted: June 9, 2018; Published: June 29, 2018
Copyright: © 2018 De Brabandere et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data underlying the findings are available from Figshare (http://dx.doi.org/10.6084/m9.figshare.5729043).
Funding: ADB, TODB, KHS, BV and JD are partially supported by the EU Interreg V A project Nano4Sports (http://www.grensregio.eu/projecten/nano4sports). TODB, KHS, BV and JD are partially supported by the KU Leuven Research Fund (C22/15/015, C32/17/036, https://www.kuleuven.be/). JD is partially supported by the KU Leuven Research Fund (C14/17/07, https://www.kuleuven.be/) and Fonds Wetenschappelijk Onderzoek Vlaanderen (SBO-150033, http://www.fwo.be/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Description
A model for predicting VO2max from submaximal running data.