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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:

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach
Ilaria Conforti, Ilaria Mileti, Zaccaria Del Prete, et al.
Sensors (2020) Vol. 20, Iss. 6, pp. 1557-1557
Open Access | Times Cited: 72

Showing 26-50 of 72 citing articles:

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
Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review
M. E. Stephens, Christen M. O’Neal, Alison M. Westrup, et al.
Neurosurgical Review (2021) Vol. 45, Iss. 2, pp. 965-978
Closed Access | Times Cited: 19
Working Posture and the Center of Mass Assessment While Starting a Chainsaw: A Case Study among Forestry Workers in Croatia
Matija Landekić, Marin Bačić, Matija Bakarić, et al.
Forests (2023) Vol. 14, Iss. 2, pp. 395-395
Open Access | Times Cited: 7
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
A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities
Bryan Rivera, Consuelo Cano, Israel Luis, et al.
Sensors (2022) Vol. 22, Iss. 3, pp. 763-763
Open Access | Times Cited: 9
Validation of a novel wearable solution for measuring L5/S1 load during manual material handling tasks
Ilaria Conforti, Ilaria Mileti, Dario Panariello, et al.
(2020), pp. 501-506
Closed Access | Times Cited: 13
Learning from Acceleration Data to Differentiate the Posture, Dynamic and Static Work of the Back: An Experimental Setup
Elena-Camelia Mușat, Stelian Alexandru Borz
Healthcare (2022) Vol. 10, Iss. 5, pp. 916-916
Open Access | Times Cited: 8
An attention-based adaptive spatial–temporal graph convolutional network for long-video ergonomic risk assessment
Chengju Zhou, Jiayu Zeng, Lina Qiu, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107780-107780
Open Access | Times Cited: 1
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
Estimation of lower back muscle force in a lifting task using wearable IMUs
Maryam Shakourisalim, Karla Beltran Martinez, Alireza Golabchi, et al.
Journal of Biomechanics (2024) Vol. 167, pp. 112077-112077
Closed 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
Ergonomic Assessment of Key Biomechanical Factors in Patient Lifting: Results from a Cross-Sectional Study
Karolis Senvaitis, Aušra Adomavičienė, Alina Tomaševič, et al.
Applied Sciences (2024) Vol. 14, Iss. 17, pp. 8076-8076
Open Access | Times Cited: 1
Detecting subject-specific fatigue-related changes in lifting kinematics using a machine learning approach
Sheldon J. Hawley, Andrew Hamilton-Wright, Steven L. Fischer
Ergonomics (2022) Vol. 66, Iss. 1, pp. 113-124
Open Access | Times Cited: 7
Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System
Seungheon Chae, Ahnryul Choi, Hyunwoo Jung, et al.
Applied Sciences (2021) Vol. 11, Iss. 24, pp. 11735-11735
Open Access | Times Cited: 9
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
Automatic Improper Loading Posture Detection and Correction Utilizing Electrical Muscle Stimulation
Ravi Kiran Kattoju, Ryan Ghamandi, Eugene M. Taranta, et al.
(2023), pp. 1-18
Closed Access | Times Cited: 3
Physical Exertion Recognition Using Surface Electromyography and Inertial Measurements for Occupational Ergonomics
Elsa Concha-Pérez, Hugo G. González-Hernández, Jorge A. Reyes-Avendaño
Sensors (2023) Vol. 23, Iss. 22, pp. 9100-9100
Open Access | Times Cited: 3
Feasibility of Tree-Based Machine Learning Models to Discriminate Safe and Unsafe Posture During Weight Lifting
G. Prisco, Maria Romano, Fabrizio Esposito, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2023), pp. 870-875
Closed Access | Times Cited: 3
Smart Manufacturing Technologies in Industry-4.0
M. Maheswari, N.C. Brintha
2021 Sixth International Conference on Image Information Processing (ICIIP) (2021)
Closed Access | Times Cited: 8
Activity recognition from trunk muscle activations for wearable and non-wearable robot conditions
Nihar Gonsalves, Omobolanle Ogunseiju, Abiola Akanmu
Smart and Sustainable Built Environment (2022) Vol. 13, Iss. 6, pp. 1370-1385
Closed Access | Times Cited: 5
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
A Computational Framework Towards the Tele-Rehabilitation of Balance Control Skills
Kübra Akbaş, Carlotta Mummolo
Frontiers in Robotics and AI (2021) Vol. 8
Open Access | Times Cited: 6
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
Automated Classification of the Phases Relevant to Work-Related Musculoskeletal Injury Risks in Residential Roof Shingle Installation Operations Using Machine Learning
Amrita Dutta, Scott P. Breloff, Dilruba Mahmud, et al.
Buildings (2023) Vol. 13, Iss. 6, pp. 1552-1552
Open Access | Times Cited: 2

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