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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:

Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach
Rana Zia Ur Rehman, Silvia Del Din, Yu Guan, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 117

Showing 1-25 of 117 citing articles:

Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson’s disease motor symptoms
Anirudha S. Chandrabhatla, I. Jonathan Pomeraniec, Alexander Ksendzovsky
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 78
A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis
Shriniket Dixit, Khitij Bohre, Yashbir Singh, et al.
Electronics (2023) Vol. 12, Iss. 4, pp. 783-783
Open Access | Times Cited: 48
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
Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations
Arti Rana, Ankur Dumka, Rajesh Singh, et al.
Diagnostics (2022) Vol. 12, Iss. 8, pp. 2003-2003
Open Access | Times Cited: 65
Gait speed in clinical and daily living assessments in Parkinson’s disease patients: performance versus capacity
Arash Atrsaei, Marta Francisca Corrà, Farzin Dadashi, et al.
npj Parkinson s Disease (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 62
Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning
Anat Mirelman, Mor Ben Or Frank, Michal L. Melamed, et al.
Movement Disorders (2021) Vol. 36, Iss. 9, pp. 2144-2155
Closed Access | Times Cited: 60
Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters
Marta Isabel A.S.N Ferreira, Fábio Augusto Barbieri, Vinícius Christianini Moreno, et al.
Gait & Posture (2022) Vol. 98, pp. 49-55
Open Access | Times Cited: 46
Detection and assessment of Parkinson's disease based on gait analysis: A survey
Yao Guo, Jianxin Yang, Yuxuan Liu, et al.
Frontiers in Aging Neuroscience (2022) Vol. 14
Open Access | Times Cited: 41
Do the gait domains change in PD patients with freezing of gait during their ‘interictal’ period?
Jiahao Zhao, Chen Liu, Ying Wan, et al.
BMC Geriatrics (2025) Vol. 25, Iss. 1
Open Access | Times Cited: 1
A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson’s Disease
Samuel Rupprechter, Gareth Morinan, Yuwei Peng, et al.
Sensors (2021) Vol. 21, Iss. 16, pp. 5437-5437
Open Access | Times Cited: 55
Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review
Aishwarya Balakrishnan, Jeevan Medikonda, Pramod K. Namboothiri, et al.
Engineered Science (2022)
Open Access | Times Cited: 37
Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts?
Rana Zia Ur Rehman, Christopher Buckley, M. Encarna Micó-Amigo, et al.
IEEE Open Journal of Engineering in Medicine and Biology (2020) Vol. 1, pp. 65-73
Open Access | Times Cited: 49
An artificial neural network approach to detect presence and severity of Parkinson’s disease via gait parameters
Tiwana Varrecchia, Stefano Filippo Castiglia, Alberto Ranavolo, et al.
PLoS ONE (2021) Vol. 16, Iss. 2, pp. e0244396-e0244396
Open Access | Times Cited: 40
Recent use of deep learning techniques in clinical applications based on gait: a survey
Yume Matsushita, Dinh Tuan Tran, Hirotake Yamazoe, et al.
Journal of Computational Design and Engineering (2021) Vol. 8, Iss. 6, pp. 1499-1532
Open Access | Times Cited: 33
Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review
Huan Zhao, Junyi Cao, Junxiao Xie, et al.
Digital Health (2023) Vol. 9
Open Access | Times Cited: 15
Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review
Milad Yousefi, Masih Akhbari, Zhina Mohamadi, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access | Times Cited: 5
Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease
Rana Zia Ur Rehman, Silvia Del Din, Jian Qing Shi, et al.
Sensors (2019) Vol. 19, Iss. 24, pp. 5363-5363
Open Access | Times Cited: 41
Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease
Rana Zia Ur Rehman, Philipp Klocke, Sofia Hryniv, et al.
Sensors (2020) Vol. 20, Iss. 18, pp. 5377-5377
Open Access | Times Cited: 33
High-accuracy wearable detection of freezing of gait in Parkinson's disease based on pseudo-multimodal features
Yuzhu Guo, Debin Huang, Wei Zhang, et al.
Computers in Biology and Medicine (2022) Vol. 146, pp. 105629-105629
Closed Access | Times Cited: 19
Automatic Assessments of Parkinsonian Gait with Wearable Sensors for Human Assistive Systems
Yi Han, Xiangzhi Liu, Ning Zhang, et al.
Sensors (2023) Vol. 23, Iss. 4, pp. 2104-2104
Open Access | Times Cited: 11
Specific Distribution of Digital Gait Biomarkers in Parkinson’s Disease Using Body-Worn Sensors and Machine Learning
Guoen Cai, Weikun Shi, Ying-Qing Wang, et al.
The Journals of Gerontology Series A (2023) Vol. 78, Iss. 8, pp. 1348-1354
Closed Access | Times Cited: 11
On the inter-dataset generalization of machine learning approaches to Parkinson's disease detection from voice
Máté Hireš, Peter Drotár, Nemuel Daniel Pah, et al.
International Journal of Medical Informatics (2023) Vol. 179, pp. 105237-105237
Closed Access | Times Cited: 11
Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning
Navita, Pooja Mittal, Yogesh Kumar Sharma, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Neurological Disease Classification Based on Gait Analysis Through Transformation-Based Multiple Linear Regression Normalization
Jhonathan Barrios, Bárbara Araújo, Miguel Gago, et al.
Springer proceedings in mathematics & statistics (2025), pp. 381-392
Closed Access
Assessment of temporospatial and kinematic gait parameters using human pose estimation in patients with Parkinson’s disease: A comparison between near-frontal and lateral views
J.S. Kim, Ryul Kim, Kyeongho Byun, et al.
PLoS ONE (2025) Vol. 20, Iss. 1, pp. e0317933-e0317933
Open Access
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