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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 Three-Dimensional Ground Reaction Forces in Running by Using Artificial Neural Networks and Lower Body Kinematics
Dimitrios-Sokratis Komaris, Eduardo Perez‐Valero, Luke Jordan, et al.
IEEE Access (2019) Vol. 7, pp. 156779-156786
Open Access | Times Cited: 48

Showing 1-25 of 48 citing articles:

Motion Capture Technology in Industrial Applications: A Systematic Review
Matteo Menolotto, Dimitrios-Sokratis Komaris, Salvatore Tedesco, et al.
Sensors (2020) Vol. 20, Iss. 19, pp. 5687-5687
Open Access | Times Cited: 207
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
Hanxun Jin, Enrui Zhang, Horacio D. Espinosa
Applied Mechanics Reviews (2023) Vol. 75, Iss. 6
Open Access | Times Cited: 73
CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data
Eva Dorschky, Marlies Nitschke, Christine F. Martindale, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 101
Real‐time biomechanics using the finite element method and machine learning: Review and perspective
Renzo Phellan, Bahe Hachem, Julien Clin, et al.
Medical Physics (2020) Vol. 48, Iss. 1, pp. 7-18
Open Access | Times Cited: 71
Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks
Glen A. Lichtwark, Robert W. Schuster, Luke A. Kelly, et al.
Journal of Biomechanics (2024) Vol. 166, pp. 112051-112051
Open Access | Times Cited: 9
Validity of recurrent neural networks to predict pedal forces and lower limb kinetics in cycling
Juan Cordero-Sánchez, Rodrigo Rico Bini, Gil Serrancolí
Journal of Biomechanics (2025) Vol. 183, pp. 112619-112619
Closed Access | Times Cited: 1
The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions
Mohsen Sharifi Renani, Abigail Eustace, Casey A. Myers, et al.
Sensors (2021) Vol. 21, Iss. 17, pp. 5876-5876
Open Access | Times Cited: 45
Machine learning for rapid estimation of lower extremity muscle and joint loading during activities of daily living
William S. Burton, Casey A. Myers, Paul J. Rullkoetter
Journal of Biomechanics (2021) Vol. 123, pp. 110439-110439
Closed Access | Times Cited: 42
Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review
Liangliang Xiang, Alan Wang, Yaodong Gu, et al.
Frontiers in Neurorobotics (2022) Vol. 16
Open Access | Times Cited: 26
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: 23
Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose
Marion Mundt, Zachery Born, Molly Goldacre, et al.
Sensors (2022) Vol. 23, Iss. 1, pp. 78-78
Open Access | Times Cited: 23
Optimization analysis of football match prediction model based on neural network
Shuo Guan, Xiaochen Wang
Neural Computing and Applications (2021) Vol. 34, Iss. 4, pp. 2525-2541
Closed Access | Times Cited: 30
Sacral acceleration can predict whole-body kinetics and stride kinematics across running speeds
Ryan S. Alcantara, Evan M. Day, Michael E. Hahn, et al.
PeerJ (2021) Vol. 9, pp. e11199-e11199
Open Access | Times Cited: 28
Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment
Seth Donahue, Michael E. Hahn
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 13
Jumping towards field-based ground reaction force estimation and assessment with OpenCap
Jasper Verheul, Mark A. Robinson, Sophie Burton
Journal of Biomechanics (2024) Vol. 166, pp. 112044-112044
Open Access | Times Cited: 4
Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players
Salvatore Tedesco, Colum Crowe, Andrew M. Ryan, et al.
Sensors (2020) Vol. 20, Iss. 11, pp. 3029-3029
Open Access | Times Cited: 31
Comparison of Ground Reaction Forces and Net Joint Moment Predictions: Skeletal Model Versus Artificial Neural Network-Based Approach
Juan Cordero-Sánchez, Bruno Bazuelo-Ruiz, Pedro Pérez‐Soriano, et al.
Journal of Applied Biomechanics (2025), pp. 1-14
Closed Access
Whole-cycle and time-specific validation of a GUI-based ground reaction force estimation tool for clinical gait analysis without a force plate
Pegah Jamali, Li‐Shan Chou, Robert D. Catena
Medical Engineering & Physics (2025), pp. 104366-104366
Open Access
Joint angle estimation with wavelet neural networks
Saaveethya Sivakumar, Alpha Agape Gopalai, King Hann Lim, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 22
Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors
Liangliang Xiang, Zixiang Gao, Alan Wang, et al.
Frontiers in Bioengineering and Biotechnology (2024) Vol. 12
Open Access | Times Cited: 3
Prediction of ground reaction forces using the artificial neural network from capacitive self-sensing values of composite ankle springs for exo-robots
Hyunsoo Hong, Muhammad Salman Sarfraz, Mingoo Jeong, et al.
Composite Structures (2022) Vol. 301, pp. 116233-116233
Closed Access | Times Cited: 10
Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults
Salvatore Tedesco, Martina Andrulli, Markus Larsson, et al.
International Journal of Environmental Research and Public Health (2021) Vol. 18, Iss. 23, pp. 12806-12806
Open Access | Times Cited: 12
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: 8
Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study
Shui Kan Lam, Ivan Vujaklija
Sensors (2021) Vol. 21, Iss. 19, pp. 6597-6597
Open Access | Times Cited: 10
Artificial Neural Networks
Markus Tilp
Springer eBooks (2024), pp. 169-176
Closed Access | Times Cited: 1
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