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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:

Tibial Acceleration-Based Prediction of Maximal Vertical Loading Rate During Overground Running: A Machine Learning Approach
Rud Derie, Pieter Robberechts, Pieter Van den Berghe, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Wearables for Running Gait Analysis: A Systematic Review
Rachel Mason, Liam T. Pearson, Gill Barry, et al.
Sports Medicine (2022) Vol. 53, Iss. 1, pp. 241-268
Open Access | Times Cited: 70
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: 45
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: 18
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
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 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
Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective
Chi Wu, Yanan Xu, Jianguang Fang, et al.
Archives of Computational Methods in Engineering (2024)
Open Access | Times Cited: 6
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
Accurate Impact Loading Rate Estimation During Running via a Subject-Independent Convolutional Neural Network Model and Optimal IMU Placement
Tian Tan, Zachary A. Strout, Peter B. Shull
IEEE Journal of Biomedical and Health Informatics (2020) Vol. 25, Iss. 4, pp. 1215-1222
Closed Access | Times Cited: 30
The Use of Wearable Sensor Technology to Detect Shock Impacts in Sports and Occupational Settings: A Scoping Review
Ingrid Eitzen, Julie Renberg, Hilde Færevik
Sensors (2021) Vol. 21, Iss. 15, pp. 4962-4962
Open Access | Times Cited: 24
Machine Learning role in clinical decision-making: Neuro-rehabilitation video game
Shabnam Sadeghi Esfahlani, Hassan Shirvani, J.B. Butt, et al.
Expert Systems with Applications (2022) Vol. 201, pp. 117165-117165
Open Access | Times Cited: 19
The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syndrome
Taylor Miners, Jeremy Witchalls, Jaquelin A. Bousie, et al.
Biomechanics (2025) Vol. 5, Iss. 2, pp. 22-22
Open Access
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
TSFuse: automated feature construction for multiple time series data
Arne De Brabandere, Tim Op De Beéck, Kilian Hendrickx, et al.
Machine Learning (2022) Vol. 113, Iss. 8, pp. 5001-5056
Open Access | Times Cited: 11
Comparison of different machine learning models to enhance sacral acceleration-based estimations of running stride temporal variables and peak vertical ground reaction force
Aurélien Patoz, Thibault Lussiana, Bastiaan Breine, et al.
Sports Biomechanics (2023), pp. 1-17
Open Access | Times Cited: 6
Determining jumping performance from a single body-worn accelerometer using machine learning
Mark White, Neil E. Bezodis, Jonathon Neville, et al.
PLoS ONE (2022) Vol. 17, Iss. 2, pp. e0263846-e0263846
Open Access | Times Cited: 8
Estimation of Normal Ground Reaction Forces in Multiple Treadmill Skiing Movements Using IMU Sensors with Optimized Locations
Yijia Zhang, Qing Fei, Zhen Chen, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 16, pp. 25972-25985
Closed Access | Times Cited: 1
Acceleration-Based Estimation of Vertical Ground Reaction Forces during Running: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns
Dovin Kiernan, Brandon Ng, David Hawkins
Sensors (2023) Vol. 23, Iss. 21, pp. 8719-8719
Open Access | Times Cited: 3
Prediction of instantaneous perceived effort during outdoor running using accelerometry and machine learning
Cristina-Ioana Pîrșcoveanu, Anderson Souza Oliveira
European Journal of Applied Physiology (2023) Vol. 124, Iss. 3, pp. 963-973
Open Access | Times Cited: 2
Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach
Hannah L. Dimmick, Cody R. van Rassel, Martin J. MacInnis, et al.
Frontiers in Sports and Active Living (2023) Vol. 5
Open Access | Times Cited: 1
Can machine learning help reveal the competitive advantage of elite beach volleyball players?
Ola Thorsen, Emmanuel Esema, Said Hemaz, et al.
Linköping electronic conference proceedings (2024) Vol. 208, pp. 57-66
Open Access
Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running and Sports Movements
Carlo Dindorf, Fabian Horst, Djordje Slijepčević, et al.
Springer optimization and its applications (2024), pp. 91-148
Closed Access
Sacral acceleration can predict whole-body kinetics and stride kinematics across running speeds
Ryan S. Alcantara, Evan M. Day, Michael G. Hahn, et al.
(2021)
Open Access | Times Cited: 3
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.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 2
Generalisable FPCA-based Models for Predicting Peak Power in Vertical Jumping using Accelerometer Data
Mark White
(2021)
Open Access | Times Cited: 1
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