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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:

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: a recurrent neural network solution
Ryan S. Alcantara, W. Brent Edwards, Guillaume Y. Millet, et al.
PeerJ (2022) Vol. 10, pp. e12752-e12752
Open Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors
Liangliang Xiang, Yaodong Gu, Zixiang Gao, et al.
Computers in Biology and Medicine (2024) Vol. 170, pp. 108016-108016
Closed Access | Times Cited: 8
Generative deep learning applied to biomechanics: A new augmentation technique for motion capture datasets
Metin Bicer, Andrew Phillips, Alessandro Melis, et al.
Journal of Biomechanics (2022) Vol. 144, pp. 111301-111301
Open Access | Times Cited: 29
Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation
Wenqi Liang, Fanjie Wang, Ao Fan, et al.
Sensors (2023) Vol. 23, Iss. 9, pp. 4229-4229
Open Access | Times Cited: 17
Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment
Seth R. Donahue, Michael E. Hahn
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 13
Consumer-priced wearable sensors combined with deep learning can be used to accurately predict ground reaction forces during various treadmill running conditions
Josh Carter, Xi Chen, Dario Cazzola, et al.
PeerJ (2024) Vol. 12, pp. e17896-e17896
Open Access | Times Cited: 4
Repeatability of Vertical Ground Reaction Force Estimation During Running on the Athletics Track on 3 Different Days
Bouke L. Scheltinga, Jaap H. Buurke, Joost N. Kok, et al.
Journal of Applied Biomechanics (2025), pp. 1-12
Closed Access
Biomechanical Changes and the Time Course of Recovery in Lower Extremities of Recreational Runners Following a Simulated Treadmill Half-Marathon
Wenjin Wang, Shuo Xu, Igor Komnik, et al.
Sports Medicine - Open (2025) Vol. 11, Iss. 1
Open Access
Estimating Running Ground Reaction Forces from Plantar Pressure during Graded Running
Eric C. Honert, Fabian Hoitz, Sam Blades, et al.
Sensors (2022) Vol. 22, Iss. 9, pp. 3338-3338
Open Access | Times Cited: 21
Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics
Taniel S. Winner, Michael C. Rosenberg, Kanishk Jain, et al.
PLoS Computational Biology (2023) Vol. 19, Iss. 10, pp. e1011556-e1011556
Open Access | Times Cited: 11
Estimating 3D ground reaction forces in running using three inertial measurement units
Bouke L. Scheltinga, Joost N. Kok, Jaap H. Buurke, et al.
Frontiers in Sports and Active Living (2023) Vol. 5
Open Access | Times Cited: 9
Prediction of gait kinetics using Markerless-driven musculoskeletal modeling
Zachary Ripic, Ilias Theodorakos, Michael Skipper Andersen, et al.
Journal of Biomechanics (2023) Vol. 157, pp. 111712-111712
Closed Access | Times Cited: 9
Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation
Tian Tan, Peter B. Shull, Jennifer L. Hicks, et al.
IEEE Transactions on Biomedical Engineering (2024) Vol. 71, Iss. 7, pp. 2095-2104
Open Access | Times Cited: 3
Human pose estimation using deep learning: review, methodologies, progress and future research directions
Pranjal Kumar, Siddhartha Chauhan, Lalit Kumar Awasthi
International Journal of Multimedia Information Retrieval (2022) Vol. 11, Iss. 4, pp. 489-521
Closed Access | Times Cited: 15
Predicting Free Achilles Tendon Strain From Motion Capture Data Using Artificial Intelligence
Zhengliang Xia, Daniel Devaprakash, Bradley M. Cornish, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Vol. 31, pp. 3086-3094
Open Access | Times Cited: 7
Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review
Sanchana Krishnakumar, Bert-Jan van Beijnum, C.T.M. Baten, et al.
Sensors (2024) Vol. 24, Iss. 7, pp. 2163-2163
Open Access | Times Cited: 2
Towards an accurate rolling resistance: Estimating intra-cycle load distribution between front and rear wheels during wheelchair propulsion from inertial sensors
Marit P. van Dijk, Louise I. Heringa, Monique Berger, et al.
Journal of Sports Sciences (2024) Vol. 42, Iss. 7, pp. 611-620
Open Access | Times Cited: 2
Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep Learning Methods Using Pressure Insoles
Amal Kammoun, Philippe Ravier, Olivier Buttelli
Sensors (2024) Vol. 24, Iss. 16, pp. 5318-5318
Open Access | Times Cited: 2
Prediction of continuous and discrete kinetic parameters in horses from inertial measurement units data using recurrent artificial neural networks
Jeanne Parmentier, Stephan Bosch, Berend Jan van der Zwaag, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 6
Predicting ankle and knee sagittal kinematics and kinetics using an ankle-mounted inertial sensor
Ting Long, Jereme Outerleys, Ted Yeung, et al.
Computer Methods in Biomechanics & Biomedical Engineering (2023) Vol. 27, Iss. 9, pp. 1057-1070
Open Access | Times Cited: 5
Sustained Limb‐Level Loading: A Ground Reaction Force Phenotype Common to Individuals at High Risk for and Those With Knee Osteoarthritis
Elizabeth Bjornsen, David J. Berkoff, J. Troy Blackburn, et al.
Arthritis & Rheumatology (2023) Vol. 76, Iss. 4, pp. 566-576
Closed Access | Times Cited: 5
Indoor running temporal variability for different running speeds, treadmill inclinations, and three different estimation strategies
Andrea Zignoli, Antoine Godin, Laurent Mourot
PLoS ONE (2023) Vol. 18, Iss. 7, pp. e0287978-e0287978
Open Access | Times Cited: 4
Deep-learning model for the prediction of lower-limb joint moments using single inertial measurement unit during different locomotive activities
Wenqi Liang, Fanjie Wang, Ao Fan, et al.
Biomedical Signal Processing and Control (2023) Vol. 86, pp. 105372-105372
Closed Access | Times Cited: 4
Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection
Margit M. Bach, Nadia Dominici, Andreas Daffertshofer
Frontiers in Sports and Active Living (2022) Vol. 4
Open Access | Times Cited: 7
Using Wearable Sensors to Estimate Vertical Ground Reaction Force Based on a Transformer
Yeqing Zhu, Di Xia, Heng Zhang
Applied Sciences (2023) Vol. 13, Iss. 4, pp. 2136-2136
Open Access | Times Cited: 3
Estimation of ground reaction force waveforms during fixed pace running outside the laboratory
Seth R. Donahue, Michael E. Hahn
Frontiers in Sports and Active Living (2023) Vol. 5
Open Access | Times Cited: 3
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