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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:

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning
Leandro Donisi, Giuseppe Cesarelli, Armando Coccia, et al.
Sensors (2021) Vol. 21, Iss. 8, pp. 2593-2593
Open Access | Times Cited: 51

Showing 1-25 of 51 citing articles:

The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review
Victor C.H. Chan, Gwyneth B. Ross, Allison L. Clouthier, et al.
Applied Ergonomics (2021) Vol. 98, pp. 103574-103574
Open Access | Times Cited: 79
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: 37
Application of wearable technology for the ergonomic risk assessment of healthcare professionals: A systematic literature review
Inês Sabino, Maria do Carmo Fernandes, Cátia Cepeda, et al.
International Journal of Industrial Ergonomics (2024) Vol. 100, pp. 103570-103570
Open Access | Times Cited: 7
Exposures to select risk factors can be estimated from a continuous stream of inertial sensor measurements during a variety of lifting-lowering tasks
Sol Lim
Ergonomics (2024) Vol. 67, Iss. 11, pp. 1596-1611
Closed Access | Times Cited: 6
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
Manufacturing Operator Ergonomics: A Conceptual Digital Twin Approach to Detect Biomechanical Fatigue
Abhimanyu Sharotry, Jesus A. Jimenez, Francis A. Méndez Mediavilla, et al.
IEEE Access (2022) Vol. 10, pp. 12774-12791
Open Access | Times Cited: 23
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
Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors
G. Prisco, Maria Romano, Fabrizio Esposito, et al.
Diagnostics (2024) Vol. 14, Iss. 6, pp. 576-576
Open Access | Times Cited: 4
Combining Postural Sway Parameters and Machine Learning to Assess Biomechanical Risk Associated with Load-Lifting Activities
G. Prisco, Maria Agnese Pirozzi, Antonella Santone, et al.
Diagnostics (2025) Vol. 15, Iss. 1, pp. 105-105
Open Access
Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning
Fatemeh Davoudi Kakhki, Hardik Vora, Armin Moghadam
Biosensors (2025) Vol. 15, Iss. 2, pp. 84-84
Open Access
A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset
Leandro Donisi, Giuseppe Cesarelli, Anna Castaldo, et al.
Journal of Imaging (2021) Vol. 7, Iss. 10, pp. 215-215
Open Access | Times Cited: 26
Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes
Giovanni D’Addio, Leandro Donisi, Giuseppe Cesarelli, et al.
Bioengineering (2021) Vol. 8, Iss. 10, pp. 138-138
Open Access | Times Cited: 20
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
sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings
Leandro Donisi, Deborah Jacob, Lorena Guerrini, et al.
Bioengineering (2023) Vol. 10, Iss. 9, pp. 1103-1103
Open Access | Times Cited: 8
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
Machine Learning for Detection and Risk Assessment of Lifting Action
Brennan Thomas, Ming‐Lun Lu, Rashmi Jha, et al.
IEEE Transactions on Human-Machine Systems (2022) Vol. 52, Iss. 6, pp. 1196-1204
Open Access | Times Cited: 11
Visual analysis of machine learning methods in the field of ergonomics — Based on Cite Space V
Mengjiao Zhang, Hongxia Li, Shuicheng Tian
International Journal of Industrial Ergonomics (2022) Vol. 93, pp. 103395-103395
Closed Access | Times Cited: 10
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
Exposures to Several Risk Factors can be Estimated from a Continuous Stream of Inertial Sensor Measurements during a Variety of Lifting-Lowering Tasks
Sol Lim
SSRN Electronic Journal (2024)
Closed Access | Times Cited: 1
A Case Study of Ergonomic Risk Assessment in Slovakia with Respect to EU Standard
Daniela Onofrejová, Miriam Andrejiová, Denisa Porubčanová, et al.
International Journal of Environmental Research and Public Health (2024) Vol. 21, Iss. 6, pp. 666-666
Open Access | Times Cited: 1
Binary Risk vs No-Risk Classification of Load Lifting Activities Using Features Extracted from sEMG Trapezius Muscle
G. Prisco, Leandro Donisi, Deborah Jacob, et al.
IFMBE proceedings (2024), pp. 283-291
Closed Access | Times Cited: 1
Modeling the Impact of Ergonomic Interventions and Occupational Factors on Work-Related Musculoskeletal Disorders in the Neck of Office Workers with Machine Learning Methods
Mohammad Sadegh Sohrabi, Hassan Khotanlou, Rashid Heidarimoghadam, et al.
Journal of Research in Health Sciences (2024) Vol. 24, Iss. 3, pp. e00623-e00623
Open Access | Times Cited: 1
Comparing Optical and Custom IoT Inertial Motion Capture Systems for Manual Material Handling Risk Assessment Using the NIOSH Lifting Index
Manuel Gutiérrez, Britam Gómez, Gustavo Retamal, et al.
Technologies (2024) Vol. 12, Iss. 10, pp. 180-180
Open Access | Times Cited: 1
Classifying hazardous movements and loads during manual materials handling using accelerometers and instrumented insoles
Mitja Trkov, Duncan T. Stevenson, Andrew Merryweather
Applied Ergonomics (2022) Vol. 101, pp. 103693-103693
Open Access | Times Cited: 6
Tactile Gloves Predict Load Weight During Lifting With Deep Neural Networks
Guoyang Zhou, Ming‐Lun Lu, Denny Yu
IEEE Sensors Journal (2023) Vol. 23, Iss. 16, pp. 18798-18809
Closed Access | Times Cited: 3
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