ArticlePloS one2020
Estimating an individual's oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Machine Learning-Based Prediction of Track Sprint Cycling Performance in Elite-Level Male Cyclists: A Multinational Cross-Sectional Analysis.Sports medicine - open · 2026Article
- Prediction of oxygen uptake in dragon boat athletes using machine learning and multimodal physiological signals.Frontiers in physiology · 2026Article
- Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion.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
- A machine learning approach for saddle height classification in cycling.Frontiers in sports and active living · 2025Article
- Combined Impact of Heart Rate Sensor Placements with Respiratory Rate and Minute Ventilation on Oxygen Uptake Prediction.Sensors (Basel, Switzerland) · 2024Article
- Article
- Pro-cycling team cyclist assignment for an upcoming race.PloS one · 2024Article
- Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches.PloS one · 2024Article
- Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models.Sensors (Basel, Switzerland) · 2023Article
- Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities.NPJ digital medicine · 2021Article
- Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities.NPJ digital medicine · 2021Article
- Pop Music Trend and Image Analysis Based on Big Data Technology.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Measurement of oxygen uptake during exercise ([Formula: see text]) is currently non-accessible to most individuals without expensive and invasive equipment. The goal of this pilot study was to estimate cycling [Formula: see text] from easy-to-obtain inputs, such as heart rate, mechanical power output, cadence and respiratory frequency. To this end, a recurrent neural network was trained from laboratory cycling data to predict [Formula: see text] values. Data were collected on 7 amateur cyclists during a graded exercise test, two arbitrary protocols (Prot-1 and -2) and an "all-out" Wingate test. In Trial-1, a neural network was trained with data from a graded exercise test, Prot-1 and Wingate, before being tested against Prot-2. In Trial-2, a neural network was trained using data from the graded exercise test, Prot-1 and 2, before being tested against the Wingate test. Two analytical models (Models 1 and 2) were used to compare the predictive performance of the neural network. Predictive performance of the neural network was high during both Trial-1 (MAE = 229(35) mlO2min-1, r = 0.94) and Trial-2 (MAE = 304(150) mlO2min-1, r = 0.89). As expected, the predictive ability of Models 1 and 2 deteriorated from Trial-1 to Trial-2. Results suggest that recurrent neural networks have the potential to predict the individual [Formula: see text] response from easy-to-obtain inputs across a wide range of cycling intensities.
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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.