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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:

Determinants of gait stability while walking on a treadmill: A machine learning approach
Fabienne Reynard, Philippe Terrier
Journal of Biomechanics (2017) Vol. 65, pp. 212-215
Open Access | Times Cited: 10

Showing 10 citing articles:

Deep Learning for Fall Risk Assessment With Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters
Can Tunca, Gülüstü Salur, Cem Ersoy
IEEE Journal of Biomedical and Health Informatics (2019) Vol. 24, Iss. 7, pp. 1994-2005
Closed Access | Times Cited: 90
Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions
Nizam Uddin Ahamed, Dylan Kobsar, Lauren C. Benson, et al.
PLoS ONE (2018) Vol. 13, Iss. 9, pp. e0203839-e0203839
Open Access | Times Cited: 55
Postural control in healthy adults: Determinants of trunk sway assessed with a chest-worn accelerometer in 12 quiet standing tasks
Fabienne Reynard, David Christe, Philippe Terrier
PLoS ONE (2019) Vol. 14, Iss. 1, pp. e0211051-e0211051
Open Access | Times Cited: 45
Subject-specific and group-based running pattern classification using a single wearable sensor
Nizam Uddin Ahamed, Dylan Kobsar, Lauren C. Benson, et al.
Journal of Biomechanics (2019) Vol. 84, pp. 227-233
Closed Access | Times Cited: 41
The Maximum Lyapunov Exponent During Walking and Running: Reliability Assessment of Different Marker-Sets
Antonis Ekizos, Alessandro Santuz, Arno Schroll, et al.
Frontiers in Physiology (2018) Vol. 9
Open Access | Times Cited: 35
A Random Forest Approach for Quantifying Gait Ataxia With Truncal and Peripheral Measurements Using Multiple Wearable Sensors
Dũng H. Phan, Nhan Nguyen, Pubudu N. Pathirana, et al.
IEEE Sensors Journal (2019) Vol. 20, Iss. 2, pp. 723-734
Closed Access | Times Cited: 32
Severity Classification of Parkinson’s Disease Based on Permutation-Variable Importance and Persistent Entropy
Jigang Tong, Jiachen Zhang, Enzeng Dong, et al.
Applied Sciences (2021) Vol. 11, Iss. 4, pp. 1834-1834
Open Access | Times Cited: 18
Pathological Gait Detection Based on Multiple Regression Models Using Unobtrusive Sensing Technology
Saikat Chakraborty, Shaili Jain, Anup Nandy, et al.
Journal of Signal Processing Systems (2020) Vol. 93, Iss. 1, pp. 1-10
Closed Access | Times Cited: 18
Classification of Ataxic Gait
Oldřich Vyšata, Ondřej Ťupa, Aleš Procházka, et al.
Sensors (2021) Vol. 21, Iss. 16, pp. 5576-5576
Open Access | Times Cited: 10
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
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