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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:

A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson’s Disease Characteristics
Yi Xia, Zhiming Yao, Qiang Ye, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2019) Vol. 28, Iss. 1, pp. 42-51
Closed Access | Times Cited: 67

Showing 1-25 of 67 citing articles:

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature
Jie Mei, Christian Desrosiers, Johannes Frasnelli
Frontiers in Aging Neuroscience (2021) Vol. 13
Open Access | Times Cited: 268
Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances
Protima Khan, Md. Fazlul Kader, S. M. Riazul Islam, et al.
IEEE Access (2021) Vol. 9, pp. 37622-37655
Open Access | Times Cited: 160
Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review
Κωνσταντίνα-Μαρία Γιαννακοπούλου, Ioanna Roussaki, Konstantinos Demestichas
Sensors (2022) Vol. 22, Iss. 5, pp. 1799-1799
Open Access | Times Cited: 69
Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review
Luis Sigcha, Luigi Borzì, Federica Amato, et al.
Expert Systems with Applications (2023) Vol. 229, pp. 120541-120541
Open Access | Times Cited: 53
Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)
Hui Wen Loh, Wanrong Hong, Chui Ping Ooi, et al.
Sensors (2021) Vol. 21, Iss. 21, pp. 7034-7034
Open Access | Times Cited: 80
A deep learning approach for parkinson’s disease severity assessment
Tunç Aşuroğlu, Hasan Oğul
Health and Technology (2022) Vol. 12, Iss. 5, pp. 943-953
Open Access | Times Cited: 41
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: 38
Parkinson’s disease diagnosis using deep learning: A bibliometric analysis and literature review
Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Sarminah Samad, et al.
Ageing Research Reviews (2024) Vol. 96, pp. 102285-102285
Closed Access | Times Cited: 11
A hybrid deep transfer learning-based approach for Parkinson's disease classification in surface electromyography signals
Khosro Rezaee, Somayeh Savarkar, Xiaofeng Yu, et al.
Biomedical Signal Processing and Control (2021) Vol. 71, pp. 103161-103161
Closed Access | Times Cited: 55
Machine Learning Algorithm for Gait Analysis and Classification on Early Detection of Parkinson
Rami Alkhatib, Mohamad O. Diab, Christophe Corbier, et al.
IEEE Sensors Letters (2020) Vol. 4, Iss. 6, pp. 1-4
Closed Access | Times Cited: 51
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: 50
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
A survey of deep learning techniques based Parkinson’s disease recognition methods employing clinical data
Amin Ul Haq, Jianping Li, Bless Lord Y. Agbley, et al.
Expert Systems with Applications (2022) Vol. 208, pp. 118045-118045
Closed Access | Times Cited: 34
The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson’s disease
Peng Wu, Biwei Cao, Zhendong Liang, et al.
Frontiers in Aging Neuroscience (2023) Vol. 15
Open Access | Times Cited: 17
A comparative study: prediction of parkinson’s disease using machine learning, deep learning and nature inspired algorithm
Pankaj Kumar Keserwani, Suman R. Das, Nairita Sarkar
Multimedia Tools and Applications (2024) Vol. 83, Iss. 27, pp. 69393-69441
Closed Access | Times Cited: 7
Hybridized Hierarchical Deep Convolutional Neural Network for Sports Rehabilitation Exercises
Dapeng Tang
IEEE Access (2020) Vol. 8, pp. 118969-118977
Open Access | Times Cited: 42
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
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
Psychological and physiological computing based on multi-dimensional foot information
Shengyang Li, Huilin Yao, Ruotian Peng, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 5
Open Access
Smart insole-based abnormal gait identification: Deep sequential networks and feature ablation study
Beomjoon Park, Minhye Kim, Dawoon Jung, et al.
Digital Health (2025) Vol. 11
Open Access
A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson’s Disease Using Complex and Large Vocal Features
Rahul Nijhawan, M. Senthil Kumar, Sahitya Arya, et al.
Biomimetics (2023) Vol. 8, Iss. 4, pp. 351-351
Open Access | Times Cited: 11
Classification of Parkinson’s disease severity using gait stance signals in a spatiotemporal deep learning classifier
Brenda G. Muñoz-Mata, Guadalupe Dorantes-Méndez, Omar Piña-Ramírez
Medical & Biological Engineering & Computing (2024)
Closed Access | Times Cited: 4
A new lightweight deep learning model optimized with pruning and dynamic quantization to detect freezing gait on wearable devices
Myung-Kyu Yi, Seong Oun Hwang
Computers in Biology and Medicine (2025) Vol. 191, pp. 110138-110138
Closed Access
Transformers for 1D signals in Parkinson’s disease detection from gait
Duc Minh Dimitri Nguyen, Mehdi Miah, Guillaume-Alexandre Bilodeau, et al.
2022 26th International Conference on Pattern Recognition (ICPR) (2022), pp. 5089-5095
Closed Access | Times Cited: 16
Video-Based Quantification of Gait Impairments in Parkinson’s Disease Using Skeleton-Silhouette Fusion Convolution Network
Qingyi Zeng, Peipei Liu, Ningbo Yu, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Vol. 31, pp. 2912-2922
Open Access | Times Cited: 10
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