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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:

Decision Support Framework for Parkinson’s Disease Based on Novel Handwriting Markers
Peter Drotár, Jiří Mekyska, Irena Rektorová, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2014) Vol. 23, Iss. 3, pp. 508-516
Closed Access | Times Cited: 162

Showing 1-25 of 162 citing articles:

Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities
Eni Halilaj, Apoorva Rajagopal, Madalina Fiterau, et al.
Journal of Biomechanics (2018) Vol. 81, pp. 1-11
Open Access | Times Cited: 339
Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease
Peter Drotár, Jiří Mekyska, Irena Rektorová, et al.
Artificial Intelligence in Medicine (2016) Vol. 67, pp. 39-46
Closed Access | Times Cited: 276
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
Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning
Wu Wang, Junho Lee, Fouzi Harrou, et al.
IEEE Access (2020) Vol. 8, pp. 147635-147646
Open Access | Times Cited: 230
Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification
Clayton R. Pereira, Danilo Rodrigues Pereira, Gustavo Henrique de Rosa, et al.
Artificial Intelligence in Medicine (2018) Vol. 87, pp. 67-77
Open Access | Times Cited: 168
Machine learning-based classification of simple drawing movements in Parkinson's disease
C. Kotsavasiloglou, Nicholas Kostikis, Dimitrios Hristu‐Varsakelis, et al.
Biomedical Signal Processing and Control (2016) Vol. 31, pp. 174-180
Closed Access | Times Cited: 152
Dynamic Handwriting Analysis for the Assessment of Neurodegenerative Diseases: A Pattern Recognition Perspective
Donato Impedovo, Giuseppe Pirlo
IEEE Reviews in Biomedical Engineering (2018) Vol. 12, pp. 209-220
Open Access | Times Cited: 136
Handwriting Analysis in Parkinson's Disease: Current Status and Future Directions
Mathew Thomas, Abhishek Lenka, Pramod Kumar Pal
Movement Disorders Clinical Practice (2017) Vol. 4, Iss. 6, pp. 806-818
Open Access | Times Cited: 121
Automated human-level diagnosis of dysgraphia using a consumer tablet
Thibault Asselborn, Thomas Gargot, Łukasz Kidziński, et al.
npj Digital Medicine (2018) Vol. 1, Iss. 1
Open Access | Times Cited: 117
Identification and Rating of Developmental Dysgraphia by Handwriting Analysis
Jiří Mekyska, Marcos Faúndez-Zanuy, Zdenek Mzourek, et al.
IEEE Transactions on Human-Machine Systems (2016) Vol. 47, Iss. 2, pp. 235-248
Closed Access | Times Cited: 99
Assessing visual attributes of handwriting for prediction of neurological disorders—A case study on Parkinson’s disease
Momina Moetesum, Imran Siddiqi, Nicole Vincent, et al.
Pattern Recognition Letters (2018) Vol. 121, pp. 19-27
Closed Access | Times Cited: 98
Handwriting dynamics assessment using deep neural network for early identification of Parkinson’s disease
Iqra Kamran, Saeeda Naz, Imran Razzak, et al.
Future Generation Computer Systems (2020) Vol. 117, pp. 234-244
Closed Access | Times Cited: 97
Dynamically enhanced static handwriting representation for Parkinson’s disease detection
Moises Díaz, Miguel A. Ferrer, Donato Impedovo, et al.
Pattern Recognition Letters (2019) Vol. 128, pp. 204-210
Open Access | Times Cited: 95
Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review
Gennaro Vessio
Applied Sciences (2019) Vol. 9, Iss. 21, pp. 4666-4666
Open Access | Times Cited: 75
Dysgraphia detection through machine learning
Peter Drotár, Marek Dobeš
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 71
Extending the Spectrum of Dysgraphia: A Data Driven Strategy to Estimate Handwriting Quality
Thibault Asselborn, Mateo Chapatte, Pierre Dillenbourg
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 70
Sequence-based dynamic handwriting analysis for Parkinson’s disease detection with one-dimensional convolutions and BiGRUs
Moises Díaz, Momina Moetesum, Imran Siddiqi, et al.
Expert Systems with Applications (2020) Vol. 168, pp. 114405-114405
Open Access | Times Cited: 70
Multiple-Fine-Tuned Convolutional Neural Networks for Parkinson’s Disease Diagnosis From Offline Handwriting
Matej Gazda, Máté Hireš, Peter Drotár
IEEE Transactions on Systems Man and Cybernetics Systems (2021) Vol. 52, Iss. 1, pp. 78-89
Closed Access | Times Cited: 60
A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s Disease
S. Saravanan, K. Ramkumar, K. Adalarasu, et al.
Archives of Computational Methods in Engineering (2022) Vol. 29, Iss. 6, pp. 3639-3653
Closed Access | Times Cited: 50
Automated methods for diagnosis of Parkinson’s disease and predicting severity level
Zainab Ayaz, Saeeda Naz, Naila Habib Khan, et al.
Neural Computing and Applications (2022)
Closed Access | Times Cited: 45
LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis
Xuechao Wang, Junqing Huang, Marianna Chatzakou, et al.
Computer Methods and Programs in Biomedicine (2024) Vol. 247, pp. 108066-108066
Open Access | Times Cited: 10
Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis
Donato Impedovo, Giuseppe Pirlo, Gennaro Vessio
Information (2018) Vol. 9, Iss. 10, pp. 247-247
Open Access | Times Cited: 71
A Handwriting-Based Protocol for Assessing Neurodegenerative Dementia
Donato Impedovo, Giuseppe Pirlo, Gennaro Vessio, et al.
Cognitive Computation (2019) Vol. 11, Iss. 4, pp. 576-586
Closed Access | Times Cited: 61
Velocity-Based Signal Features for the Assessment of Parkinsonian Handwriting
Donato Impedovo
IEEE Signal Processing Letters (2019) Vol. 26, Iss. 4, pp. 632-636
Open Access | Times Cited: 58
Explainable Artificial Intelligence (EXAI) Models for Early Prediction of Parkinson’s Disease Based on Spiral and Wave Drawings
S. Saravanan, K. Ramkumar, K. Narasimhan, et al.
IEEE Access (2023) Vol. 11, pp. 68366-68378
Open Access | Times Cited: 20
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