EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 532 (2024) E3S Web Conf., 532 (2024) 02001 References
Open Access
Issue
E3S Web Conf.
Volume 532, 2024
Second International Conference of Applied Industrial Engineering: Intelligent Production Automation and its Sustainable Development (CIIA 2024)
Article Number 02001
Number of page(s) 14
Section Applied Technological Innovations for Sustainable Industrial Environments
DOI https://doi.org/10.1051/e3sconf/202453202001
Published online 06 June 2024
  1. T.K. Gill, M.M. Mittinty, L.M. March, J.D. Steinmetz, G.T. Culbreth, M. Cross, J.A. Kopec, A.D. Woolf, L.M. Haile, H. Hagins et al., Global, regional, and national burden of other musculoskeletal disorders, 1990–2020, and projections to 2050: a systematic analysis of the global burden of disease study 2021, The Lancet Rheumatology 5, e670 (2023). [CrossRef] [PubMed] [Google Scholar]
  2. Fathallah, Musculoskeletal disorders in labor-intensive agriculture, Elsevier 41, 738 (2010). https://doi.org/10.1016 [Google Scholar]
  3. B.C. Hamilton, M.I. Dairywala, A. Highet, T.C. Nguyen, P. O’Sullivan, H. Chern, I.S. Soriano, Artificial intelligence based real-time video ergonomic assessment and training improves resident ergonomics, The American Journal of Surgery 226, 741 (2023). [CrossRef] [Google Scholar]
  4. R. Jain, M.L. Meena, G.S. Dangayach, A.K. Bhardwaj, Risk factors for musculoskeletal disorders in manual harvesting farmers of rajasthan, Industrial health 56, 241 (2018). [CrossRef] [PubMed] [Google Scholar]
  5. J.W. Kim, J.Y. Choi, E.J. Ha, J.H. Choi, Human pose estimation using mediapipe pose and optimization method based on a humanoid model, Applied Sciences 13, 2700 (2023). [CrossRef] [Google Scholar]
  6. M. McMillan, C. Trask, J. Dosman, L. Hagel, W. Pickett, S.F.I.C.S. Team et al., Prevalence of musculoskeletal disorders among saskatchewan farmers, Journal of agromedicine 20, 292 (2015). [CrossRef] [PubMed] [Google Scholar]
  7. M. Biazus, C.F. Moretto, A. Pasqualotti, Relationship between musculoskeletal pain complaints and family agriculture work, Revista Dor 18, 232 (2017). [CrossRef] [Google Scholar]
  8. E. Sombatsawat, T. Luangwilai, P. Ong-artborirak, W. Siriwong, Musculoskeletal disorders among rice farmers in phimai district, nakhon ratchasima province, thailand, Journal of Health Research 33, 494 (2019). [CrossRef] [Google Scholar]
  9. M. Sharifirad, A. Poursaeed, F. Lashgarara, S.M. Mirdamadi, Risk factors for musculoskeletal problems in paddy field workers in northern iran: A community-based study, Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences 27 (2022). [Google Scholar]
  10. O’Neill, Ergonomics in industrially developing countries: does its application differ from that in industrially advanced countries?, Applied Ergonomics 3, 631 (2000). https://doi.org/10.1016/S0003-6870(00)00033-8 [Google Scholar]
  11. H.I. Castellucci, C. Viviani, P. Hernández, G. Bravo, M. Martínez, J. Ibacache, Á. Bartsch, Developing countries and the use of iso standard 11228-3 for risk management of work-related musculoskeletal disorders of the upper limbs (wrmsds-uls): The case of chile, Applied Ergonomics 96, 103483 (2021). [CrossRef] [PubMed] [Google Scholar]
  12. T.S. Ogedengbe, O.A. Abiola, O.M. Ikumapayi, S.A. Afolalu, A.I. Musa, A.O. Ajayeoba, T.A. Adeyi, Ergonomics postural risk assessment and observational techniques in the 21st century, Procedia Computer Science 217, 1335 (2023). [CrossRef] [Google Scholar]
  13. A. Kongtawelert, B. Buchholz, D. Sujitrarath, W. Laohaudomchok, P. Kongtip, S. Woskie, Prevalence and factors associated with musculoskeletal disorders among thai burley tobacco farmers, International Journal of Environmental Research and Public Health 19, 6779 (2022). [CrossRef] [PubMed] [Google Scholar]
  14. M. Rodríguez Espitia, Ph.D. thesis, Corporación Universitaria Minuto de Dios (2018) [Google Scholar]
  15. P.A. Rojas Nieto, A.L. Sierra Rubiano, L.D. Gallego Garcia, Ph.D. thesis, Corporación Universitaria Minuto de Dios (2019) [Google Scholar]
  16. I. Dianat, D. Afshari, N. Sarmasti, M.S. Sangdeh, R. Azaddel, Work posture, working conditions and musculoskeletal outcomes in agricultural workers, International Journal of Industrial Ergonomics 77, 102941 (2020). [CrossRef] [Google Scholar]
  17. P.K. Mahto, B.B. Gautam, Prevalence of work-related musculoskeletal disorders in agricultural farmers of bhaktapur district, nepal, International Journal of Occupational Safety and Health 8, 3 (2018). [CrossRef] [Google Scholar]
  18. G.K. Nayak, E. Kim, Development of a fully automated rula assessment system based on computer vision, International Journal of Industrial Ergonomics 86, 103218 (2021). [CrossRef] [Google Scholar]
  19. M. MassirisFernández, J.Á. Fernández, J.M. Bajo, C.A. Delrieux, Ergonomic risk assessment based on computer vision and machine learning, Computers & Industrial Engineering 149, 106816 (2020). [CrossRef] [Google Scholar]
  20. X.X. Li Li, Tara Martin, A novel vision-based real-time method for evaluating postural risk factors associated with musculoskeletal disorders, Applied Ergonomics 87 (2020). https://doi.org/10.1016/j.apergo.2020.103138. [Google Scholar]
  21. B. Qiu, Y. Zhang, H. Shen, J. Zhou, L. Chu, Ergonomic researches in agricultural machinery-a systematic review using the prisma method, International Journal of Industrial Ergonomics 95, 103446 (2023). [CrossRef] [Google Scholar]
  22. A.K. Singh, V.A. Kumbhare, K. Arthi, Real-time human pose detection and recognition using mediapipe, in International Conference on Soft Computing and Signal Processing (Springer, 2021), pp. 145–154 [Google Scholar]
  23. D. Battini, A. Persona, F. Sgarbossa, Innovative real-time system to integrate ergonomic evaluations into warehouse design and management, Computers & Industrial Engineering 77, 1 (2014). https://doi.org/10.1016/j.cie.2014.08.018 [CrossRef] [Google Scholar]
  24. P. Plantard, H.P.H. Shum, A.S. Le Pierres, F. Multon, Validation of an ergonomic assessment method using kinect data in real workplace conditions, Appl. Ergon. 65, 562 (2017). [CrossRef] [Google Scholar]
  25. A.M. Coruzzolo, F. Loll, N. Amicosante, H. Kuma, P. Thupaki, S. Agarwal, Comparing semiautomatic rapid upper limb assessments (rula): Azure kinect versus rgb-based machine vision algorithm, Physical Ergonomics and Human Factors 63, 54 (2022). [Google Scholar]
  26. A.A. Klein, A.L.C. Legey, A.A. Motter, E.S. Castro, M.L.L.R. Okimoto, Comparative study of rula evaluations using kinebot software, DAT Journal 7, 161 (2022). [CrossRef] [Google Scholar]
  27. P.C. Lin, Y.J. Chen, W.S. Chen, Y.J. Lee, Automatic real-time occupational posture evaluation and select corresponding ergonomic assessments, Scientific Reports 12, 2139 (2022). [CrossRef] [PubMed] [Google Scholar]
  28. D. Kee, Participatory ergonomic interventions for improving agricultural work environment: A case study in a farming organization of korea, Applied Sciences 12, 2263 (2022). [CrossRef] [Google Scholar]
  29. T. Chatzis, D. Konstantinidis, K. Dimitropoulos, Automatic ergonomic risk assessment using a variational deep network architecture, Sensors 22, 6051 (2022). [CrossRef] [PubMed] [Google Scholar]
  30. L. Singh, P. Kumar, S.K. Lohan, Development of a real-time work-related postural risk assessment system of farm workers using a sensor-based artificial intelligence approach, Journal of Field Robotics (2023). [Google Scholar]
  31. R. Arízaga, Agente inteligente para analizar los desordenes musculo esqueléticos y la evaluación ergonómica a trabajadores home office mediante redes neuronales, Journal of Interesting Articles (2020). [Google Scholar]
  32. Sánchez, Evaluación de la carga física postural: ¿owas, rula o reba?, Journal of Interesting Articles (2017). [Google Scholar]
  33. R. Ricardo, Enfermedades ergonómicas., Journal of Interesting Articles (2020). [Google Scholar]
  34. Acuña, Desarrollo de un sistema autónomo con inteligencia artificial para el monitoreo continuo de riesgos posturales en tiempo real en minería., Journal of Interesting Articles (2019). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.