ArticleNPJ digital medicine2021
Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 2 citations in OpenAlex.
- Prediction of oxygen uptake in dragon boat athletes using machine learning and multimodal physiological signals.Frontiers in physiology · 2026Article
- Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning-Based VOBiosensors · 2025Article
- Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study.PloS one · 2025Article
- Personalized exercise programs in oncology.Oncology reviews · 2025Review
- Combined Impact of Heart Rate Sensor Placements with Respiratory Rate and Minute Ventilation on Oxygen Uptake Prediction.Sensors (Basel, Switzerland) · 2024Article
- Article
- Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches.PloS one · 2024Article
- Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data.PloS one · 2023Article
- Article
- Longitudinal assessment of cardiorespiratory fitness and body mass of young healthy adults during COVID-19 pandemic.Journal of applied physiology (Bethesda, Md. : 1985) · 2022Article
- Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Oxygen consumption ([Formula: see text]) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, [Formula: see text] monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of [Formula: see text] from wearable sensors during cycle ergometer exercise using a temporal convolutional network (TCN). Cardiorespiratory signals were acquired from a smart shirt with integrated textile sensors alongside ground-truth [Formula: see text] from a metabolic system on 22 young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of [Formula: see text] dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of [Formula: see text]. Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO2 A), with 187, 97, and 76 s yielding <3% deviation from the optimal validation loss. TCN-VO2 A showed strong prediction accuracy (mean, 95% CI) across all exercise intensities (-22 ml min
Identifiers
What OpenQuestion holds
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.