Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Positive impact of short-term gait rehabilitation in Parkinson patients: a combined approach based on statistics and machine learning
Leandro Donisi, Giuseppe Cesarelli, Pietro Balbi, et al.
Mathematical Biosciences & Engineering (2021) Vol. 18, Iss. 5, pp. 6995-7009
Open Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression
Natalia Shusharina, Denis Yukhnenko, Stepan Botman, et al.
Diagnostics (2023) Vol. 13, Iss. 3, pp. 573-573
Open Access | Times Cited: 55
Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature
Leandro Donisi, Giuseppe Cesarelli, Noemi Pisani, et al.
Diagnostics (2022) Vol. 12, Iss. 12, pp. 3048-3048
Open Access | Times Cited: 38
Artificial intelligence in physical rehabilitation: A systematic review
Jennifer Sumner, Hui Wen Lim, Lin Siew Chong, et al.
Artificial Intelligence in Medicine (2023) Vol. 146, pp. 102693-102693
Open Access | Times Cited: 31
Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture
Carlo Ricciardi, Alfonso Maria Ponsiglione, Arianna Scala, et al.
Bioengineering (2022) Vol. 9, Iss. 4, pp. 172-172
Open Access | Times Cited: 28
Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson’s Disease
Michela Russo, Marianna Amboni, Paolo Barone, et al.
Sensors (2023) Vol. 23, Iss. 4, pp. 1985-1985
Open Access | Times Cited: 18
Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography
Mario Sansone, Roberta Fusco, Francesca Grassi, et al.
Current Oncology (2023) Vol. 30, Iss. 1, pp. 839-853
Open Access | Times Cited: 16
Machine Learning Methods in Physical Therapy: A Scoping Review of applications in clinical context.
Felipe José Jandré dos Reis, Matheus Bartholazzi Lugão de Carvalho, Gabriela de Assis Neves, et al.
Musculoskeletal Science and Practice (2024) Vol. 74, pp. 103184-103184
Closed Access | Times Cited: 6
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: 14
Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson’s Disease by Means of Machine Learning Models
Giuseppe Cesarelli, Leandro Donisi, Francesco Amato, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Vol. 31, pp. 1056-1063
Open Access | Times Cited: 13
The role of machine learning in discovering biomarkers and predicting treatment strategies for neurodegenerative diseases: A narrative review
Abdullahi Tunde Aborode, Ogunware Adedayo Emmanuel, Isreal Ayobami Onifade, et al.
NeuroMarkers. (2025) Vol. 2, Iss. 1, pp. 100034-100034
Open Access
Comparative Study: Using Machine Learning Models Based Rehabilitation Therapy to Classify Diabetic Frozen Shoulder Exercises
Zaid Ahmed, Mohamed Sherif, Maha Abdelmohsen, et al.
Lecture notes in networks and systems (2025), pp. 684-698
Closed Access
A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks
Leandro Donisi, Giuseppe Cesarelli, E Capodaglio, et al.
Diagnostics (2022) Vol. 12, Iss. 11, pp. 2624-2624
Open Access | Times Cited: 15
Feasibility of Tree-based Machine Learning algorithms fed with surface electromyographic features to discriminate risk classes according to NIOSH
Leandro Donisi, E Capodaglio, Gaetano Pagano, et al.
2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (2022), pp. 1-6
Closed Access | Times Cited: 11
Detection of Suspicious Cardiotocographic Recordings by Means of a Machine Learning Classifier
Carlo Ricciardi, Francesco Amato, Annarita Tedesco, et al.
Bioengineering (2023) Vol. 10, Iss. 2, pp. 252-252
Open Access | Times Cited: 5
Machine learning-based detection of cervical spondylotic myelopathy using multiple gait parameters
Xinyu Ji, Wei Zeng, Qihang Dai, et al.
Biomimetic Intelligence and Robotics (2023) Vol. 3, Iss. 2, pp. 100103-100103
Open Access | Times Cited: 5
Impact of feature reduction techniques on classification accuracy of machine learning techniques in leg rehabilitation
Ayat Naji Hussain, Sahar Adil Abboud, Basim Abdul baki Jumaa, et al.
Measurement Sensors (2022) Vol. 25, pp. 100544-100544
Open Access | Times Cited: 8
Breast Density Analysis on Mammograms: Application of Machine Learning with Textural Features
Francesca Angelone, Carlo Ricciardi, Gianluca Gatta, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2022), pp. 295-300
Closed Access | Times Cited: 7
Application of Bayesian Decision Tree in Hematology Research: Differential Diagnosis of β-Thalassemia Trait from Iron Deficiency Anemia
Mina Jahangiri, Fakher Rahim, Najmaldin Saki, et al.
Computational and Mathematical Methods in Medicine (2021) Vol. 2021, pp. 1-10
Open Access | Times Cited: 9
Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review
Daniele Bibbo, Cristiano De Marchis, Maurizio Schmid, et al.
Physiological Measurement (2023) Vol. 44, Iss. 12, pp. 12TR01-12TR01
Open Access | Times Cited: 3
Biomechanical risk classification according to NIOSH in workers affected by occupational pathologies
Leandro Donisi, Giuseppe Cesarelli, E Capodaglio, et al.
2022 E-Health and Bioengineering Conference (EHB) (2022), pp. 1-4
Closed Access | Times Cited: 5
Biomechanical modelling for quantitative assessment of gait kinematics in drop foot patients with ankle foot orthosis
Armando Coccia, Federica Amitrano, Gaetano Pagano, et al.
2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (2022), pp. 1-6
Closed Access | Times Cited: 4
Combining simulation and machine learning for the management of healthcare systems
Carlo Ricciardi, Giuseppe Cesarelli, Alfonso Maria Ponsiglione, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2022) Vol. 14, pp. 335-339
Closed Access | Times Cited: 4
Machine Learning and Biosignals are able to discriminate biomechanical risk classes according to the Revised NIOSH Lifting Equation
Leandro Donisi, Giuseppe Cesarelli, E Capodaglio, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2022)
Closed Access | Times Cited: 4
The Impact of Ankle-Foot Orthoses on Spatio-Temporal Gait Parameters in Drop-Foot Patients
Federica Amitrano, Armando Coccia, Gaetano Pagano, et al.
2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (2022), pp. 1-6
Closed Access | Times Cited: 3
The impact of ankle-foot orthosis on walking features of drop foot patients
Federica Amitrano, Armando Coccia, Giuseppe Cesarelli, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2022), pp. 87-92
Closed Access | Times Cited: 3
Page 1 - Next Page

Scroll to top