Integration of artificial intelligence and traditional methods for ergonomic risk assessment

Felipe Larez; Yéssika Maribao

doi:10.61286/e-rms.v3i.287

Autores/as

Felipe Larez Carlisle Construction Materials, Carlisle, EE. UU. https://orcid.org/0009-0003-5037-9987
Yéssika Maribao Anglo Latin Culture Research Center, 369 Florida, EE. UU. https://orcid.org/0000-0002-1390-0943

DOI:

https://doi.org/10.61286/e-rms.v3i.287

Palabras clave:

artificial intelligence, applied ergonomics, ergonomic risks, classical methods, legal framework.

Resumen

These approaches are susceptible to variations based on the evaluator's experience and criteria. The adoption of artificial intelligence (AI), through computer vision, inertial sensors, and motion capture systems, offers a tool capable of enhancing accuracy, consistency, and real-time continuous monitoring. This study examines the benefits and drawbacks of both methods in assessing Musculoskeletal Disorders (MSDs), proposing a hybrid model that combines AI with validated techniques like REBA, RULA, and NIOSH, ensuring scientific rigor and practical relevance in actual work settings. From a regulatory standpoint, the global context and the latest U.S. legal framework are reviewed. The European Union leads with Regulation (EU) 2024/1689, which sets binding requirements for high-risk systems, including human oversight, traceability, and data protection. In Asia, ASEAN and Singapore are progressing through ethical guidelines and non-binding national strategies aimed at responsible AI governance. In the U.S., the October 2023 Executive Order was revoked on January 20, 2025, and replaced by Executive Order 14179 – Removing Barriers to American Leadership in Artificial Intelligence, emphasizing innovation and instructing federal agencies to review existing regulations. However, there is currently no comprehensive federal AI law. The order warns that unregulated use of automated tools, without professional validation, can pose legal and technical risks to workers and employers. In conclusion, a hybrid approach remains the most efficient, scientifically sound, and legally justifiable option, as it couples the speed of AI with professional oversight, methodological traceability, and adherence to international standards.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Aaron, K. A., Vaughan, J., Gupta, R., Ali, N. E., Beth, A. H., Moore, J. M., Ma, Y., Ahmad, I., Jackler, R. K., & Vaisbuch, Y. (2021). The risk of ergonomic injury across surgical specialties. PLoS One, 16(2), e0244868.

https://doi.org/10.1371/journal.pone.0244868

ASEAN. (2024). ASEAN guide on AI governance and ethics. ASEAN Secretariat.

https://asean.org/wp-content/uploads/2024/02/ASEAN-Guide-on-AI-Governance-and-Ethics_beautified_201223_v2.pdf

Ayvaz, Ö., Özyıldırım, B. A., İşsever, H., Öztan, G., Atak, M., & Özel, S. (2023). Ergonomic risk assessment of working postures of nurses working in a medical faculty hospital with REBA and RULA methods. Science Progress, 106(4), 368504231216540. https://doi.org/10.1177/00368504231216540

Bazaluk, O., Tsopa, V., Cheberiachko, S., Deryugin, O., Radchuk, D., Borovytskyi, O., & Lozynskyi, V. (2023). Ergonomic risk management process for safety and health at work. Frontiers in Public Health, 11, 1253141. https://doi.org/10.3389/fpubh.2023.1253141

Camargo Salinas, M. A., Miranda Arandia, N. Y., & Suárez Pérez, J. F. (2024). State of the art in evaluation of automated ergonomic risk detection methods in industrial work environments. Occupational Safety and Health Management, 6(2), 25–37. https://doi.org/10.15765/jzdrd646

Chatzis, T., Konstantinidis, D., & Dimitropoulos, K. (2022). Automatic ergonomic risk assessment using a variational deep network architecture. Sensors (Basel), 22(16), 6051. https://doi.org/10.3390/s22166051

Danylak, S., Walsh, L. J., & Zafar, S. (2024). Measuring ergonomic interventions and prevention programs for reducing musculoskeletal injury risk in the dental workforce: A systematic review. Journal of Dental Education, 88(2), 128–141. https://doi.org/10.1002/jdd.13403

Dixon, F., Vitish-Sharma, P., Khanna, A., Keeler, B. D., & VOLCANO Trial Group. (2024). Robotic assisted surgery reduces ergonomic risk during minimally invasive colorectal resection: The VOLCANO randomised controlled trial. Langenbeck's Archives of Surgery, 409(1), 142. https://doi.org/10.1007/s00423-024-03322-y

European Commission. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 on artificial intelligence. Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj

Fan, L. J., Liu, S., Jin, T., Gan, J. G., Wang, F. Y., Wang, H. T., & Lin, T. (2022). Ergonomic risk factors and work-related musculoskeletal disorders in clinical physiotherapy. Frontiers in Public Health, 10, 1083609. https://doi.org/10.3389/fpubh.2022.1083609

Holzgreve, F., Fraeulin, L., Betz, W., Erbe, C., Wanke, E. M., Brüggmann, D., Nienhaus, A., Groneberg, D. A., Maurer-Grubinger, C., & Ohlendorf, D. (2022). A RULA-based comparison of the ergonomic risk of typical working procedures for dentists and dental assistants. Sensors (Basel), 22(3), 805. https://doi.org/10.3390/s22030805

Hulshof, C. T. J., Pega, F., Neupane, S., Colosio, C., Daams, J. G., Kc, P., Kuijer, P. P. F. M., Mandic-Rajcevic, S., Masci, F., van der Molen, H. F., Nygård, C. H., Oakman, J., Proper, K. I., & Frings-Dresen, M. H. W. (2021). The effect of occupational exposure to ergonomic risk factors on osteoarthritis of hip or knee and selected other musculoskeletal diseases. Environmental International, 150, 106349. https://doi.org/10.1016/j.envint.2020.106349

Monfared, S., Athanasiadis, D. I., Umana, L., Hernandez, E., Asadi, H., Colgate, C. L., Yu, D., & Stefanidis, D. (2022). A comparison of laparoscopic and robotic ergonomic risk. Surgical Endoscopy, 36(11), 8397–8402. https://doi.org/10.1007/s00464-022-09105-0

Nygaard, N. B., Thomsen, G. F., Rasmussen, J., Skadhauge, L. R., & Gram, B. (2022). Ergonomic and individual risk factors for musculoskeletal pain in the ageing workforce. BMC Public Health, 22(1), 1975. https://doi.org/10.1186/s12889-022-14386-0

Paskarini, I., Dwiyanti, E., Mahmudah, M., Widarjanto, W., Nugroho, S. A., & Syaiful, D. A. (2025). The interplay of ergonomic risk factor and lifestyle factors on Potter's well-being and work fatigue in Magelang's tourism village. BMC Public Health, 25(1), 1550. https://doi.org/10.1186/s12889-025-22780-7

Pejčić, N., Petrović, V., Đurić-Jovičić, M., Medojević, N., & Nikodijević-Latinović, A. (2021). Analysis and prevention of ergonomic risk factors among dental students. European Journal of Dental Education, 25(3), 460–479. https://doi.org/10.1111/eje.12621

Raghavan, R., Panicker, V. V., & Emmatty, F. J. (2022). Ergonomic risk and physiological assessment of plogging activity. Work, 72(4), 1337–1348. https://doi.org/10.3233/WOR-205210

Scataglini, S., Fontinovo, E., Khafaga, N., Khan, M. U., Khan, M. F., & Truijen, S. (2025). A systematic review of the accuracy, validity, and reliability of markerless versus marker camera-based 3D motion capture for industrial ergonomic risk analysis. Sensors (Basel), 25(17), 5513. https://doi.org/10.3390/s25175513

Smart Nation and Digital Government Office. (2023). National AI Strategy 2.0. Government of Singapore. https://www.smartnation.gov.sg/initiatives/national-ai-strategy

The White House. (2025, January 23). Executive Order 14179 – Removing barriers to American leadership in artificial intelligence. https://www.federalregister.gov/documents/2025/01/31/2025-02172/removing-barriers-to-american-leadership-in-artificial-intelligence

Yunus, M. N. H., Jaafar, M. H., Mohamed, A. S. A., Azraai, N. Z., & Hossain, M. S. (2021). Implementation of kinetic and kinematic variables in ergonomic risk assessment using motion capture simulation: A review. International Journal of Environmental Research and Public Health, 18(16), 8342. https://doi.org/10.3390/ijerph18168342