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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:

Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning
Georgios Giarmatzis, Evangelia I. Zacharaki, Κωνσταντίνος Μουστάκας
Sensors (2020) Vol. 20, Iss. 23, pp. 6933-6933
Open Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors
Shima Mohammadi Moghadam, Ted Yeung, Julie Choisne
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 43
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
Comparing lab and field agility kinematics in young talented female football players: Implications for ACL injury prevention
Stefano Di Paolo, Eline M. Nijmeijer, Laura Bragonzoni, et al.
European Journal of Sport Science (2022) Vol. 23, Iss. 5, pp. 859-868
Open Access | Times Cited: 34
Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis
Ahmad Zahid Rao, Saba Shahid Siddique, Muhammad Danish Mujib, et al.
IEEE Access (2024) Vol. 12, pp. 41676-41687
Open Access | Times Cited: 7
Finding the Goldilocks Zone of Mechanical Loading: A Comprehensive Review of Mechanical Loading in the Prevention and Treatment of Knee Osteoarthritis
Jacob Jahn, Quinn T. Ehlen, Chun‐Yuh Huang
Bioengineering (2024) Vol. 11, Iss. 2, pp. 110-110
Open Access | Times Cited: 6
Differences in running technique between runners with better and poorer running economy and lower and higher milage: An artificial neural network approach
Bas Van Hooren, Rebecca Lennartz, Maartje Cox, et al.
Scandinavian Journal of Medicine and Science in Sports (2024) Vol. 34, Iss. 3
Open Access | Times Cited: 5
Human motion capture, reconstruction, and musculoskeletal analysis in real time
Urbano Lugrís, Manuel Pérez-Soto, Florian Michaud, et al.
Multibody System Dynamics (2023) Vol. 60, Iss. 1, pp. 3-25
Open Access | Times Cited: 12
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: 12
A Leap Forward in Biomechanics: Predicting Ground Reaction Forces with Dual-Branch GRNN
Saeid Soraghi, Mehdi Gheitasi
(2025)
Closed Access
Machine learning-based prediction of joint moments based on kinematics in patients with cerebral palsy
Mustafa Erkam Özateş, Derya Karabulut, Firooz Salami, et al.
Journal of Biomechanics (2023) Vol. 155, pp. 111668-111668
Closed Access | Times Cited: 9
Glenohumeral joint force prediction with deep learning
Pezhman Eghbali, Fabio Becce, Patrick Goetti, et al.
Journal of Biomechanics (2024) Vol. 163, pp. 111952-111952
Open Access | Times Cited: 3
On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods
Felipe Arruda Moura, Alexandre Roberto Marcondes Pelegrinelli, Danilo S. Catelli, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Gait Deviation and Neurological Diseases: A Comparative Study of Quantitative Measures
Lorenzo Hermez, Nesma Houmani, Sonia Garcia-Salicetti, et al.
IFMBE proceedings (2024), pp. 498-507
Open Access | Times Cited: 3
Machine Learning for Musculoskeletal Modeling of Upper Extremity
Rahul Sharma, Abhishek Dasgupta, Runbei Cheng, et al.
IEEE Sensors Journal (2022) Vol. 22, Iss. 19, pp. 18684-18697
Closed Access | Times Cited: 12
Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks
Jere Lavikainen, Lauri Stenroth, Tine Alkjær, et al.
Annals of Biomedical Engineering (2023) Vol. 51, Iss. 11, pp. 2479-2489
Open Access | Times Cited: 7
Machine learning-based prediction of hip joint moment in healthy subjects, patients and post-operative subjects
Mattia Perrone, Steven P. Mell, John T. Martin, et al.
Computer Methods in Biomechanics & Biomedical Engineering (2024), pp. 1-5
Open Access | Times Cited: 2
3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units
Shima Mohammadi Moghadam, Pablo Ortega Auriol, Ted Yeung, et al.
Frontiers in Bioengineering and Biotechnology (2024) Vol. 12
Open Access | Times Cited: 2
Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities
Guanlin Ding, Andrew Plummer, Ioannis Georgilas
Frontiers in Bioengineering and Biotechnology (2022) Vol. 10
Open Access | Times Cited: 11
Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks
Bernard X. W. Liew, David Rügamer, Qichang Mei, et al.
Frontiers in Bioengineering and Biotechnology (2023) Vol. 11
Open Access | Times Cited: 6
Robot-Aided Motion Analysis in Neurorehabilitation: Benefits and Challenges
Mirjam Bonanno, Rocco Salvatore Calabrò
Diagnostics (2023) Vol. 13, Iss. 23, pp. 3561-3561
Open Access | Times Cited: 6
Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I
Anne Benjaminse, Eline M. Nijmeijer, Alli Gokeler, et al.
Sensors (2024) Vol. 24, Iss. 11, pp. 3652-3652
Open Access | Times Cited: 2
How Artificial Intelligence and Machine Learning Is Assisting Us to Extract Meaning from Data on Bone Mechanics?
Saeed Mouloodi, Hadi Rahmanpanah, Colin Burvill, et al.
Advances in experimental medicine and biology (2022), pp. 195-221
Closed Access | Times Cited: 9
Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data
Abhishek Dasgupta, Rahul Sharma, Challenger Mishra, et al.
Bioengineering (2023) Vol. 10, Iss. 5, pp. 510-510
Open Access | Times Cited: 5
Towards a comprehensive biomechanical assessment of the elderly combining in vivo data and in silico methods
Giorgio Davico, Luciana Labanca, Irene Gennarelli, et al.
Frontiers in Bioengineering and Biotechnology (2024) Vol. 12
Open Access | Times Cited: 1
Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only
Pankwon Kim, Jin‐Kyu Lee, Choongsoo S. Shin
Sensors (2021) Vol. 21, Iss. 12, pp. 4204-4204
Open Access | Times Cited: 9
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