ArticlePloS one2018
Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 28 citations in OpenAlex.
- Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features.Sensors (Basel, Switzerland) · 2025Article
- Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study.PloS one · 2025Article
- Accuracy of wearables for determining the maximal oxygen uptake and lactate threshold: a qualitative systematic review.Frontiers in sports and active living · 2025Review
- Wearable network for multilevel physical fatigue prediction in manufacturing workers.PNAS nexus · 2024Article
- Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models.Sensors (Basel, Switzerland) · 2023Article
- Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- Assessment of Peak Oxygen Uptake with a Smartwatch and its Usefulness for Training of Runners.International journal of sports medicine · 2022Article
- An Experimental Feasibility Study Evaluating the Adequacy of a Sportswear-Type Wearable for Recording Exercise Intensity.Sensors (Basel, Switzerland) · 2022Article
- Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis.Sensors (Basel, Switzerland) · 2022Article
- Greater Breast Support Is Associated With Reduced Oxygen Consumption and Greater Running Economy During a Treadmill Running Task.Frontiers in sports and active living · 2022Article
- Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners.User modeling and user-adapted interaction · 2022Article
- Estimation of Instantaneous Oxygen Uptake During Exercise and Daily Activities Using a Wearable Cardio-Electromechanical and Environmental Sensor.IEEE journal of biomedical and health informatics · 2021Article
- A New Fitness Test of Estimating VOFrontiers in physiology · 2021Article
- Sacral acceleration can predict whole-body kinetics and stride kinematics across running speeds.PeerJ · 2021Article
- Using Biosensors and Digital Biomarkers to Assess Response to Cardiac Rehabilitation: Observational Study.Journal of medical Internet research · 2020Observational
- A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.