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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living
Thomas Beltrame, Robert Amelard, Alexander Wong, et al.
Scientific Reports (2017) Vol. 7, Iss. 1
Open Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field
Stephanie Soon, Hafdis S. Svavarsdottir, Candice Downey, et al.
BMJ Innovations (2020) Vol. 6, Iss. 2, pp. 55-71
Open Access | Times Cited: 94
Wearable Sensors as a Preoperative Assessment Tool: A Review
Aron Syversen, Alexios Dosis, David Jayne, et al.
Sensors (2024) Vol. 24, Iss. 2, pp. 482-482
Open Access | Times Cited: 11
Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction: A review
Atiqa Ashfaq, Neil J. Cronin, P Müller
Informatics in Medicine Unlocked (2022) Vol. 28, pp. 100863-100863
Open Access | Times Cited: 25
Prediction of peak oxygen consumption using cardiorespiratory parameters from warmup and submaximal stage of treadmill cardiopulmonary exercise test
Maciej Rosoł, Monika Petelczyc, Jakub S. Gąsior, et al.
PLoS ONE (2024) Vol. 19, Iss. 1, pp. e0291706-e0291706
Open Access | Times Cited: 4
Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors
Kimberly A. Ingraham, Daniel P. Ferris, C. David Remy
Journal of Applied Physiology (2019) Vol. 126, Iss. 3, pp. 717-729
Open Access | Times Cited: 39
Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models
Thomas Beltrame, Robert Amelard, Alexander Wong, et al.
Journal of Applied Physiology (2017) Vol. 124, Iss. 2, pp. 473-481
Open Access | Times Cited: 38
Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data
Maria Cecília Moraes Frade, Thomas Beltrame, Mariana de Oliveira Góis, et al.
PLoS ONE (2023) Vol. 18, Iss. 3, pp. e0282398-e0282398
Open Access | Times Cited: 9
Estimating an individual’s oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study
Andrea Zignoli, Alessandro Fornasiero, Matteo Ragni, et al.
PLoS ONE (2020) Vol. 15, Iss. 3, pp. e0229466-e0229466
Open Access | Times Cited: 26
Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors
João Lopes, Joana Figueiredo, Pedro Fonseca, et al.
Sensors (2022) Vol. 22, Iss. 20, pp. 7913-7913
Open Access | Times Cited: 14
The role of machine learning methods in physiological explorations of endurance trained athletes: a mini-review
Félix Boudry, Fabienne Durand, Henri Méric, et al.
Frontiers in Sports and Active Living (2024) Vol. 6
Open Access | Times Cited: 2
Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
Robert Amelard, Eric T. Hedge, Richard L. Hughson
npj Digital Medicine (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 16
The role of the microcirculation and integrative cardiovascular physiology in the pathogenesis of ICU-acquired weakness
Asher A. Mendelson, Dustin Erickson, Rodrigo Villar
Frontiers in Physiology (2023) Vol. 14
Open Access | Times Cited: 6
Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network
Andrew Borror, Michael J. Mazzoleni, James Coppock, et al.
Biomedical Human Kinetics (2019) Vol. 11, Iss. 1, pp. 60-68
Open Access | Times Cited: 16
Effects of Morning Versus Evening Home-Based Exercise on Subjective and Objective Sleep Parameters in Older Adults: A Randomized Controlled Trial
Jaehoon Seol, Yuya Fujii, T Inoue, et al.
Journal of Geriatric Psychiatry and Neurology (2020) Vol. 34, Iss. 3, pp. 232-242
Open Access | Times Cited: 15
Prediction of oxygen uptake kinetics during heavy-intensity cycling exercise by machine learning analysis
Eric T. Hedge, Robert Amelard, Richard L. Hughson
Journal of Applied Physiology (2023) Vol. 134, Iss. 6, pp. 1530-1536
Closed Access | Times Cited: 5
Enhancing instantaneous oxygen uptake estimation by non-linear model using cardio-pulmonary physiological and motion signals
Zhao Wang, Qiang Zhang, Ke Lan, et al.
Frontiers in Physiology (2022) Vol. 13
Open Access | Times Cited: 8
Accelerating the Estimation of Metabolic Cost Using Signal Derivatives: Implications for Optimization and Evaluation of Wearable Robots
Kimberly A. Ingraham, Elliott J. Rouse, C. David Remy
IEEE Robotics & Automation Magazine (2019) Vol. 27, Iss. 1, pp. 32-42
Closed Access | Times Cited: 12
State-of-the art concepts and future directions in modelling oxygen consumption and lactate concentration in cycling exercise
Andrea Zignoli, Alessandro Fornasiero, Enrico Bertolazzi, et al.
Sport Sciences for Health (2019) Vol. 15, Iss. 2, pp. 295-310
Closed Access | Times Cited: 11
Mean Normalized Gain: A New Method for the Assessment of the Aerobic System Temporal Dynamics during Randomly Varying Exercise in Humans
Thomas Beltrame, Richard L. Hughson
Frontiers in Physiology (2017) Vol. 8
Open Access | Times Cited: 10
Wearable Units
T. Tamura
Springer eBooks (2017), pp. 211-249
Closed Access | Times Cited: 9
A Short Review on the Machine Learning-Guided Oxygen Uptake Prediction for Sport Science Applications
Haneen Alzamer, Tamer Abuhmed, Kotiba Hamad
Electronics (2021) Vol. 10, Iss. 16, pp. 1956-1956
Open Access | Times Cited: 8
Frequency domain analysis to extract dynamic response characteristics for oxygen uptake during transitions to moderate- and heavy-intensity exercises
Eric T. Hedge, Richard L. Hughson
Journal of Applied Physiology (2020) Vol. 129, Iss. 6, pp. 1422-1430
Closed Access | Times Cited: 7
The Feasibility of Measuring Lung Hyperinflation With a Smart Shirt: An in Vitro Study
Denise Mannée, Hanneke van Helvoort, Frans H. de Jongh
IEEE Sensors Journal (2020) Vol. 20, Iss. 24, pp. 15154-15162
Open Access | Times Cited: 5
Nonparametric Model Prediction for Intelligent Regulation of Human Cardiorespiratory System to Prescribed Exercise Medicine
Hairong Yu, Yi Zhang, Lin Ye, et al.
IEEE Access (2020) Vol. 8, pp. 224621-224630
Open Access | Times Cited: 5
The relation between central variables, electromyography signals and peripheral microcirculation during intensive treadmill exercise
Anat Ratnovsky, Ran Yanovich, Dikla Kesner, et al.
Clinical Biomechanics (2019) Vol. 67, pp. 52-60
Closed Access | Times Cited: 4
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