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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:

Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework
Eric Rapp, Soyong Shin, W Thomsen, et al.
Journal of Biomechanics (2021) Vol. 116, pp. 110229-110229
Open Access | Times Cited: 57

Showing 1-25 of 57 citing articles:

OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations
Mazen Al Borno, Johanna O’Day, Vanessa Ibarra, et al.
Journal of NeuroEngineering and Rehabilitation (2022) Vol. 19, Iss. 1
Open Access | Times Cited: 101
A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units
Marion Mundt, William R. Johnson, Wolfgang Potthast, et al.
Sensors (2021) Vol. 21, Iss. 13, pp. 4535-4535
Open Access | Times Cited: 66
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: 65
Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models
Jay-Shian Tan, Sawitchaya Tippaya, Tara Binnie, et al.
Sensors (2022) Vol. 22, Iss. 2, pp. 446-446
Open Access | Times Cited: 38
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
Validity and reliability of inertial measurement units used to measure motion of the lumbar spine: A systematic review of individuals with and without low back pain
F. A. McClintock, Andrew Callaway, Carol Clark, et al.
Medical Engineering & Physics (2024) Vol. 126, pp. 104146-104146
Open Access | Times Cited: 8
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: 43
DeepBBWAE-Net: A CNN-RNN Based Deep SuperLearner for Estimating Lower Extremity Sagittal Plane Joint Kinematics Using Shoe-Mounted IMU Sensors in Daily Living
Md Sanzid Bin Hossain, Joseph Dranetz, Hwan Choi, et al.
IEEE Journal of Biomedical and Health Informatics (2022) Vol. 26, Iss. 8, pp. 3906-3917
Open Access | Times Cited: 29
The effect of time normalization and biomechanical signal processing techniques of ground reaction force curves on deep-learning model performance
Oussama Jlassi, Philippe C Dixon
Journal of Biomechanics (2024) Vol. 168, pp. 112116-112116
Open Access | Times Cited: 5
Technology for measuring freezing of gait: Current state of the art and recommendations
Martina Mancini, J. Lucas McKay, Helena Cockx, et al.
Journal of Parkinson s Disease (2025)
Open Access
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: 25
Feasibility of Markerless Motion Capture for Three-Dimensional Gait Assessment in Community Settings
Theresa E. McGuirk, Elliott S. Perry, Wandasun B. Sihanath, et al.
Frontiers in Human Neuroscience (2022) Vol. 16
Open Access | Times Cited: 24
Population and Age-Based Cardiorespiratory Fitness Level Investigation and Automatic Prediction
Liangliang Xiang, Kaili Deng, Qichang Mei, et al.
Frontiers in Cardiovascular Medicine (2022) Vol. 8
Open Access | Times Cited: 23
Fusion of video and inertial sensing data via dynamic optimization of a biomechanical model
Owen Pearl, Soyong Shin, Ashwin Godura, et al.
Journal of Biomechanics (2023) Vol. 155, pp. 111617-111617
Open Access | Times Cited: 13
Hip contact forces can be predicted with a neural network using only synthesised key points and electromyography in people with hip osteoarthritis
Bradley M. Cornish, Claudio Pizzolato, David J. Saxby, et al.
Osteoarthritis and Cartilage (2024) Vol. 32, Iss. 6, pp. 730-739
Open Access | Times Cited: 4
Learning based lower limb joint kinematic estimation using open source IMU data
Benjamin Hur, Seung Gyun Baek, Inseung Kang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions
Eva Dorschky, Marlies Nitschke, Matthias Mayer, et al.
Frontiers in Bioengineering and Biotechnology (2025) Vol. 13
Open Access
Research on Geomagnetic Sensor Error Correction Method for Rotary-Wing UAV
Wei Xu, Ying Zhou, Yongjun Wang, et al.
Lecture notes in electrical engineering (2025), pp. 130-140
Closed Access
Optimal control simulations tracking wearable sensor signals provide comparable running gait kinematics to marker-based motion capture
Grace McConnochie, Aaron Fox, Clint R. Bellenger, et al.
PeerJ (2025) Vol. 13, pp. e19035-e19035
Open Access
Inertial Sensors for Human Motion Analysis: A Comprehensive Review
Sara García-de-Villa, David Casillas-Pérez, Ana Jiménez-Martín, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-39
Open Access | Times Cited: 12
Real-Time Ground Reaction Force and Knee Extension Moment Estimation During Drop Landings Via Modular LSTM Modeling and Wearable IMUs
Tao Sun, Dongxuan Li, Bingfei Fan, et al.
IEEE Journal of Biomedical and Health Informatics (2023) Vol. 27, Iss. 7, pp. 3222-3233
Closed Access | Times Cited: 10
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
Validity and Sensitivity of an Inertial Measurement Unit-Driven Biomechanical Model of Motor Variability for Gait
Christopher A. Bailey, Thomas K. Uchida, Julie Nantel, et al.
Sensors (2021) Vol. 21, Iss. 22, pp. 7690-7690
Open Access | Times Cited: 25
Markerless Motion Tracking With Noisy Video and IMU Data
Soyong Shin, LI Zhi-xiong, Eni Halilaj
IEEE Transactions on Biomedical Engineering (2023) Vol. 70, Iss. 11, pp. 3082-3092
Closed Access | Times Cited: 9
Deep Learning for Quantified Gait Analysis: A Systematic Literature Review
Adil Khan, Omar Galarraga, Sonia Garcia-Salicetti, et al.
IEEE Access (2024) Vol. 12, pp. 138932-138957
Open Access | Times Cited: 3
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