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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:

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

Showing 1-25 of 41 citing articles:

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
Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization
Luca Palmerini, Luca Reggi, Tecla Bonci, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 22
Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality
Silvia Cavedoni, Alice Chirico, Elisa Pedroli, et al.
Frontiers in Human Neuroscience (2020) Vol. 14
Open Access | Times Cited: 64
A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson’s Disease Using Wearable Based Gait Signals
Satyabrata Aich, Jinyoung Youn, Sabyasachi Chakraborty, et al.
Diagnostics (2020) Vol. 10, Iss. 6, pp. 421-421
Open Access | Times Cited: 52
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: 36
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
Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole‐brain white matter
Zhenyu Shu, Sijia Cui, Xiao Wu, et al.
Magnetic Resonance in Medicine (2020) Vol. 85, Iss. 3, pp. 1611-1624
Closed Access | Times Cited: 47
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
Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis
Aditi Site, Jari Nurmi, Elena Simona Lohan
IEEE Access (2021) Vol. 9, pp. 112221-112235
Open Access | Times Cited: 36
Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson’s disease
Kaiwen Deng, Yueming Li, Hanrui Zhang, et al.
Communications Biology (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 24
IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring
Fraser Young, Rachel Mason, Rosie Morris, et al.
Sensors (2023) Vol. 23, Iss. 8, pp. 4100-4100
Open Access | Times Cited: 14
A survey of artificial intelligence in gait-based neurodegenerative disease diagnosis
Haocong Rao, Minlin Zeng, Xuejiao Zhao, et al.
Neurocomputing (2025), pp. 129533-129533
Closed Access
A framework to standardize gait study protocols in Parkinson's disease
Martina Mancini, Jeffrey M. Hausdorff, Elisa Pelosin, et al.
Journal of Parkinson s Disease (2025)
Open Access
Towards remote healthcare monitoring using accessible IoT technology: state-of-the-art, insights and experimental design
Graham Coulby, Alison Clear, Oliver Jones, et al.
BioMedical Engineering OnLine (2020) Vol. 19, Iss. 1
Open Access | Times Cited: 35
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
The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature
Ashley Cha Yin Lim, Pragadesh Natarajan, R. Dineth Fonseka, et al.
Digital Health (2022) Vol. 8, pp. 205520762210741-205520762210741
Open Access | Times Cited: 20
Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson’s Disease
Rana M. Khalil, Lisa Shulman, Ann L. Gruber‐Baldini, et al.
Sensors (2024) Vol. 24, Iss. 15, pp. 4983-4983
Open Access | Times Cited: 4
Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson’s Disease Classification Using Machine Learning
Rana Zia Ur Rehman, Yu Guan, Jian Qing Shi, et al.
Frontiers in Aging Neuroscience (2022) Vol. 14
Open Access | Times Cited: 18
The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review
Nader Salari, Mohsen Kazeminia, Hesam Sagha, et al.
Current Psychology (2022) Vol. 42, Iss. 20, pp. 16637-16660
Closed Access | Times Cited: 17
A Classification Model Using Personal Biometric Characteristics to Identify Individuals Vulnerable to an Extremely Hot Environment
Yujin Choi, Seungwon Seo, Taehoon Hong, et al.
Journal of Management in Engineering (2024) Vol. 40, Iss. 2
Closed Access | Times Cited: 3
The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review
Sylvain Jung, Mona Michaud, Laurent Oudre, et al.
Sensors (2020) Vol. 20, Iss. 19, pp. 5625-5625
Open Access | Times Cited: 25
Gait classification for early detection and severity rating of Parkinson’s disease based on hybrid signal processing and machine learning methods
Qinghui Wang, Wei Zeng, Xiangkun Dai
Cognitive Neurodynamics (2022) Vol. 18, Iss. 1, pp. 109-132
Closed Access | Times Cited: 15
Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID-19
Nader Salari, Shamarina Shohaimi, Aliakbar Kiaei, et al.
Biology Methods and Protocols (2023) Vol. 8, Iss. 1
Open Access | Times Cited: 8
Deep learning-based detection of affected body parts in Parkinson’s disease and freezing of gait using time-series imaging
Hwayoung Park, Sungtae Shin, Changhong Youm, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review
William Tam, Mohannad Alajlani, Alaa Abd‐Alrazaq
Journal of Medical Internet Research (2023) Vol. 25, pp. e42950-e42950
Open Access | Times Cited: 6
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