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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:

Automatic recognition of gait patterns in human motor disorders using machine learning: A review
Joana Figueiredo, Cristina P. Santos, Juan C. Moreno
Medical Engineering & Physics (2018) Vol. 53, pp. 1-12
Open Access | Times Cited: 137

Showing 1-25 of 137 citing articles:

Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects
Md. Milon Islam, Sheikh Nooruddin, Fakhri Karray, et al.
Computers in Biology and Medicine (2022) Vol. 149, pp. 106060-106060
Open Access | Times Cited: 126
Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
Alexandra-Maria Tăuƫan, Bogdan Ionescu, Emiliano Santarnecchi
Artificial Intelligence in Medicine (2021) Vol. 117, pp. 102081-102081
Closed Access | Times Cited: 105
Explaining the differences of gait patterns between high and low-mileage runners with machine learning
Datao Xu, Wenjing Quan, Huiyu Zhou, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 98
Machine Learning Approach to Support the Detection of Parkinson’s Disease in IMU-Based Gait Analysis
Dante Trabassi, Mariano Serrao, Tiwana Varrecchia, et al.
Sensors (2022) Vol. 22, Iss. 10, pp. 3700-3700
Open Access | Times Cited: 69
A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis
Datao Xu, Huiyu Zhou, Wenjing Quan, et al.
Gait & Posture (2023) Vol. 107, pp. 293-305
Closed Access | Times Cited: 57
Explaining the unique nature of individual gait patterns with deep learning
Fabian Horst, Sebastian Lapuschkin, Wojciech Samek, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 142
Application of supervised machine learning algorithms in the classification of sagittal gait patterns of cerebral palsy children with spastic diplegia
Yanxin Zhang, Ye Ma
Computers in Biology and Medicine (2019) Vol. 106, pp. 33-39
Closed Access | Times Cited: 99
Wearable Solutions for Patients with Parkinson’s Disease and Neurocognitive Disorder: A Systematic Review
Asma Channa, Nirvana Popescu, Vlad Ciobanu
Sensors (2020) Vol. 20, Iss. 9, pp. 2713-2713
Open Access | Times Cited: 97
Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors?
Ríona Mc Ardle, Silvia Del Din, Brook Galna, et al.
Gait & Posture (2019) Vol. 76, pp. 372-376
Closed Access | Times Cited: 90
Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease
E. Balaji, D. Brindha, Balakrishnan Ramasamy
Applied Soft Computing (2020) Vol. 94, pp. 106494-106494
Closed Access | Times Cited: 90
Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation
Gundala Jhansi Rani, Mohammad Farukh Hashmi, Aditya Gupta
IEEE Access (2023) Vol. 11, pp. 105140-105169
Open Access | Times Cited: 25
Towards an Inertial Sensor-Based Wearable Feedback System for Patients after Total Hip Arthroplasty: Validity and Applicability for Gait Classification with Gait Kinematics-Based Features
Wolfgang Teufl, Bertram Taetz, Markus Miezal, et al.
Sensors (2019) Vol. 19, Iss. 22, pp. 5006-5006
Open Access | Times Cited: 68
Time series classification using a modified LSTM approach from accelerometer-based data: A comparative study for gait cycle detection
Hui Xing Tan, Nway Nway Aung, Jing Tian, et al.
Gait & Posture (2019) Vol. 74, pp. 128-134
Closed Access | Times Cited: 59
The detection of age groups by dynamic gait outcomes using machine learning approaches
Yuhan Zhou, Robbin Romijnders, Clint Hansen, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 57
Data-driven gait analysis for diagnosis and severity rating of Parkinson’s disease
E Balaji, D. Brindha, Vinodh Kumar Elumalai, et al.
Medical Engineering & Physics (2021) Vol. 91, pp. 54-64
Closed Access | Times Cited: 49
Explaining Machine Learning Models for Clinical Gait Analysis
Djordje Slijepčević, Fabian Horst, Sebastian Lapuschkin, et al.
ACM Transactions on Computing for Healthcare (2021) Vol. 3, Iss. 2, pp. 1-27
Open Access | Times Cited: 44
Smart insoles review (2008-2021): Applications, potentials, and future
Ibrahim Almuteb, Rui Hua, Ya Wang
Smart Health (2022) Vol. 25, pp. 100301-100301
Open Access | Times Cited: 28
An Efficient Machine Learning Approach for Diagnosing Parkinson’s Disease by Utilizing Voice Features
Arti Rana, Ankur Dumka, Rajesh Singh, et al.
Electronics (2022) Vol. 11, Iss. 22, pp. 3782-3782
Open Access | Times Cited: 28
Halloysite nanotube/black phosphorene nanohybrid modified screen-printed carbon electrode as an ultra-portable electrochemical sensing platform for smartphone-capable detection of maleic hydrazide with machine learning assistance
Yu Ge, Minghui Li, Ying Zhong, et al.
Food Chemistry (2022) Vol. 406, pp. 134967-134967
Closed Access | Times Cited: 27
Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
Pranav Madhav Kuber, Hrushikesh Godbole, Ehsan Rashedi
Applied Sciences (2024) Vol. 14, Iss. 9, pp. 3563-3563
Open Access | Times Cited: 7
Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations
Julien Lebleu, Thierry Gosseye, Christine Detrembleur, et al.
Sensors (2020) Vol. 20, Iss. 3, pp. 715-715
Open Access | Times Cited: 49
Prediction of Freezing of Gait in Patients With Parkinson’s Disease by Identifying Impaired Gait Patterns
Yuqian Zhang, Weiwu Yan, Yifei Yao, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2020) Vol. 28, Iss. 3, pp. 591-600
Closed Access | Times Cited: 47
Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning
Johannes Burdack, Fabian Horst, Sven Giesselbach, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 46
Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders
Rana Zia Ur Rehman, Yuhan Zhou, Silvia Del Din, et al.
Sensors (2020) Vol. 20, Iss. 23, pp. 6992-6992
Open Access | Times Cited: 43
Perceptual-motor styles
Pierre‐Paul Vidal, Francesco Lacquaniti
Experimental Brain Research (2021) Vol. 239, Iss. 5, pp. 1359-1380
Open Access | Times Cited: 32
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