Use of artificial intelligence in workers safety: a systematic literature review
DOI:
https://doi.org/10.14195/1647-7723_32-1_5Keywords:
Artificial intelligence,, work safety,, prevention,, technology.Abstract
Work accidents represent a problem not only in Brazil, but also worldwide. The International Labour Organization estimates that 2 million people die worldwide each year from work-related causes. This research presents a systematic literature review, with the purpose of identifying the main international publications, types and applications of AI in work safety, with a focus on preventing worker accidents. After drafting the research protocol and searching the Emerald Insight, IEEE Xplore, Science Direct, Scopus, and Web of Science databases, 2,369 articles were found from which, after applying the exclusion criteria, 31 articles directly related to the theme were selected. The countries with the most searches were China, the US, and South Korea, with around 50% of the total. The main applications identified were monitoring workers and detecting objects in work environments. It became clear that AI applied to worker safety is still little explored, with a significant increase from 2022.
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References
Anuário Estatístico da Previdência Social (AEPS). (2019). Recuperado de: https://www.gov.br/trabalho-e-previdencia/pt-br/assuntos/previdencia-social/saude-e-seguranca-do-trabalhador/dados-de-acidentes-do-trabalho. Acesso em 30 de dezembro 2022.
Aoki, K., Nishikawa, H., Makihara, Y., Muramatsu, D., Takemura, N., & Yagi, Y. (2021). Physical Fatigue Detection From Gait Cycles via a Multi-Task Recurrent Neural Network. IEEE Access, 9, 127565-127575.
Balakreshnan, B., Richards, G., Nanda, G., Mao, H., Athinarayanan, R., & Zaccaria, J. (2020). PPE compliance detection using artificial intelligence in learning factories. Procedia Manufacturing, 45, 277-282.
Bento, C. R., Cusioli, L. F., Rezende, D., & Mantovani, D. Quantitativo de acidentes de trabalho na construção civil de 2015-2019 no Brasil: Uma revisão. A engenharia de segurança do trabalho e suas diversidades aplicadas na prática Volume, 25.
Bonifazi, G., Corradini, E., Ursino, D., Virgili, L., Anceschi, E., & De Donato, M. C. (2022). A machine learning based sentient multimedia framework to increase safety at work. Multimedia tools and applications, 81(1), 141-169.
Cai, J., Zhang, Y., Yang, L., Cai, H., & Li, S. (2020). A context-augmented deep learning approach for worker trajectory prediction on unstructured and dynamic construction sites. Advanced Engineering Informatics, 46, 101173.
Campero-Jurado, I., Márquez-Sánchez, S., Quintanar-Gómez, J., Rodríguez, S., & Corchado, J. M. (2020). Smart helmet 5.0 for industrial internet of things using artificial intelligence. Sensors, 20(21), 6241.
Chang, C. H., Chuang, M. L., Tan, J. C., Hsieh, C. C., & Chou, C. C. (2022). Indoor safety monitoring for falls or restricted areas using wi-fi channel state information and deep learning methods in mega building construction projects. Sustainability, 14(22), 15034.
Cho, H., Lee, K., Choi, N., Kim, S., Lee, J., & Yang, S. (2022). Online Safety Zone Estimation and Violation Detection for Nonstationary Objects in Workplaces. IEEE Access, 10, 39769-39781.
Chuerubim, M. L., & Silva, I. D. (2019). Possibilidades em inteligência artificial na detecção de padrões e previsão de acidentes em rodovias.
Clifford, C. (2018), “Google CEO: a.I. is more important than fire or electricity”. Recuperado de: www.cnbc.com/2018/02/01/google-ceo-sundar-pichai-ai-is-more-important-than-fire-electricity.html.
Costanzo, M., De Maria, G., Lettera, G., & Natale, C. (2021). A multimodal approach to human safety in collaborative robotic workcells. IEEE Transactions on Automation Science and Engineering, 19(2), 1202-1216.
de Camargo Fiorini, P., & Jabbour, C. J. C. (2017). Information systems and sustainable supply chain management towards a more sustainable society: Where we are and where we are going. International Journal of Information Management, 37(4), 241-249.
de Oliveira, C. S., Sanin, C., & Szczerbicki, E. (2018). Contextual knowledge to enhance workplace hazard recognition and interpretation in a cognitive vision platform. Procedia Computer Science, 126, 1837-1846.
Deng, H., Ou, Z., & Deng, Y. (2021). Multi-angle fusion-based safety status analysis of construction workers. International Journal of Environmental Research and Public Health, 18(22), 11815.
Ensslin, L., Gonçalves, A., Dutra, A., & Ensslin, S. R. Gestão de Riscos de Acidentes de Trabalho na Indústria da Construção Civil: Revisão Bibliográfica. Gestão e Desenvolvimento em Revista, 9(1), 59-80.
Fang, W., Love, P. E., Luo, H., & Xu, S. (2022). A deep learning fusion approach to retrieve images of People’s unsafe behavior from construction sites. Developments in the Built Environment, 12, 100085.
Golcarenarenji, G., Martinez-Alpiste, I., Wang, Q., & Alcaraz-Calero, J. M. (2022). Machine-learning-based top-view safety monitoring of ground workforce on complex industrial sites. Neural Computing and Applications, 1-14.
Gnoni, M. G., Bragatto, P. A., Milazzo, M. F., & Setola, R. (2020). Integrating IoT technologies for an “intelligent” safety management in the process industry. Procedia manufacturing, 42, 511-515.
Gong, F., Ji, X., Gong, W., Yuan, X., & Gong, C. (2021). Deep learning based protective equipment detection on offshore drilling platform. Symmetry, 13(6), 954.
Gradolewski, D., Maslowski, D., Dziak, D., Jachimczyk, B., Mundlamuri, S. T., Prakash, C. G., & Kulesza, W. J. (2020). A distributed computing real-time safety system of collaborative robot. Elektronika ir Elektrotechnika, 26(2), 4-14.
Groenner, L. C., de Faria, L. I. L., Perissini, R. C., & de Souza Gracioso, L. (2022). Um estudo bibliométrico sobre a pesquisa em inteligência artificial no Brasil. Brazilian Journal of Information Science, 16(1), 7.
Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). Machine learning and deep learning approaches for cybersecuriy: A review. IEEE Access.
Jabbour, C. J. C. (2013). Environmental training in organisations: From a literature review to a framework for future research. Resources, Conservation and Recycling, 74, 144-155.
Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2022). A Review of Yolo algorithm developments. Procedia Computer Science, 199, 1066-1073.
Junior, J. A. G., Busso, C. M., Gobbo, S. C. O., & Carreão, H. (2018). Making the links among environmental protection, process safety, and industry 4.0. Process safety and environmental protection, 117, 372-382.
Kim, J., Hwang, J., Chi, S., & Seo, J. (2020). Towards database-free vision-based monitoring on construction sites: A deep active learning approach. Automation in Construction, 120, 1033.
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering–a systematic literature review. Information and software technology, 51(1), 7-15.
Li, Y., Wei, H., Han, Z., Huang, J., & Wang, W. (2020). Deep learning-based safety helmet detection in engineering management based on convolutional neural networks. Advances in Civil Engineering, 2020, 1-10.
Liang, H., & Seo, S. (2022). Automatic detection of construction workers’ helmet wear based on lightweight deep learning. Applied Sciences, 12(20), 10369.
Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. applied sciences, 9(20), 4396.
Magarey, J. M. (2001). Elements of a systematic review. International Journal of Nursing Practice, 7(6), 376-382.
Makris, S., & Aivaliotis, P. (2022). AI-based vision system for collision detection in HRC applications. Procedia CIRP, 106, 156-161.
Maman, Z. S., Chen, Y. J., Baghdadi, A., Lombardo, S., Cavuoto, L. A., & Megahed, F. M. (2020). A data analytic framework for physical fatigue management using wearable sensors. Expert Systems with Applications, 155, 113405.
Márquez-Sánchez, S., Campero-Jurado, I., Robles-Camarillo, D., Rodríguez, S., & Corchado-Rodríguez, J. M. (2021). Besafe b2. 0 smart multisensory platform for safety in workplaces. Sensors, 21(10), 3372.
Márquez-Sánchez, S., Campero-Jurado, I., Herrera-Santos, J., Rodríguez, S., & Corchado, J. M. (2021). Intelligent platform based on smart PPE for safety in workplaces. Sensors, 21(14), 4652.
McCarthy, J. (1959). Programs with common sense.
Moore, P. (2020). Inteligencia artificial en el entorno laboral. Desafíos para los trabajadores. Universidad de Leicester. Recuperado de: https://www.bbvaopenmind.com/articulos/inteligencia-artificial-en-entorno-laboral-desafios-para-trabajadores
Mulrow, C. D., Cook, D. J., & Davidoff, F. (1997). Systematic reviews: critical links in the great chain of evidence. Annals of internal medicine, 126(5), 389-391.
Organisation for Economic Co-operation and Development (OECD). Framework for Classifying AI systems, (2022). Recuperado de: http://oecd.ai/classification
Organização Internacional do Trabalho (OIT). (2021) Recuperado de: https://www.ilo.org/brasilia/noticias/WCMS_820318/lang--pt/index.htm
Organização Internacional do Trabalho (OIT). Recuperado de: https://www.ilo.org/brasilia/noticias/WCMS_848148/lang--pt/index.htm
Otgonbold, M. E., Gochoo, M., Alnajjar, F., Ali, L., Tan, T. H., Hsieh, J. W., & Chen, P. Y. (2022). SHEL5K: An extended dataset and benchmarking for safety helmet detection. Sensors, 22(6), 2315.
Pan, L., Yan, C., Zheng, Y., Fu, Q., Zhang, Y., Lu, Z., ... & Tian, J. (2023). Fatigue detection method for UAV remote pilot based on multi feature fusion. Electronic Research Archive, 31(1), 442-466.
Park, C., Lee, D., & Khan, N. (2020, July). An analysis on safety risk judgment patterns towards computer vision based construction safety management. In Creative Construction e-Conference 2020 (pp. 31-38). Budapest University of Technology and Economics.
Park, J., Lee, H., & Kim, H. Y. (2022). Risk factor recognition for automatic safety management in construction sites using fast deep convolutional neural networks. Applied Sciences, 12(2), 694.
PROGRAMA DAS NAÇÕES UNIDAS PARA O DESENVOLVIMENTO - PNUD. (2019. Recuperado de: https://hdr.undp.org/system/files/documents/hdr2019ptpdf.pdf. Acesso em 06 de março 2023.
Sága, M., Bartoš, M., Bulej, V., Stanček, J., & Wiecek, D. (2021). Development of an automated diagnostic and inspection system based on artificial intelligence designed to eliminate risks in transport and industrial companies. Transportation Research Procedia, 55, 805-813.
Saliba, T. M., & de Freitas Lanza, M. B. (2018). Curso básico de segurança e higiene ocupacional. LTr Editora Ltda.
SOCIEDAD ESPAÑOLA DE SALUD Y SEGURIDAD EN EL TRABAJO (ESST) (2018), El Uso de Sistemas de Inteligencia Artificial Para Reducir la Siniestralidad en el Trabajo. Recuperado de: https://www.sesst.org/e-prevenir-a-predecirel-uso-de-sistemas-de-inteligencia-artificial-para-reducir-lasiniestralidad-en-el-trabajo/
Shadroo, S., & Rahmani, A. M. (2018). Systematic survey of big data and data mining in internet of things. Computer Networks, 139, 19-47.
Tan, Y. H., Hitesh, A., & Li, K. H. H. (2021). Application of machine learning algorithm on mems-based sensors for determination of helmet wearing for workplace safety. Micromachines, 12(4), 449.
Teixeira, R. L. P., Teixeira, C. H. S. B., de Araujo Brito, M. L., & Silva, P. C. D. (2019). Os discursos acerca dos desafios da siderurgia na indústria 4.0 no Brasil. Brazilian Journal of Development, 5(12), 28290-28309.
Thomas, B., Lu, M. L., Jha, R., & Bertrand, J. (2022). Machine Learning for Detection and Risk Assessment of Lifting Action. IEEE Transactions on Human-Machine Systems, 52(6), 1196-1204.
Ustundag, A., & Cevikcan, E. (2018). Industry 4.0: managing the digital transformation. by Springer Nature.
Villalobos, M. (2019). Modelo Predictivo de Factores de Riesgos Laborales con uso de Inteligencia Artificial. Fundación Científica y Tecnologica (ACHS). Asociación Chilena de Seguridad. Santiago, Chile. Informe Final Proyecto, 31.
Vukicevic, A. M., Djapan, M., Isailovic, V., Milasinovic, D., Savkovic, M., & Milosevic, P. (2022). Generic compliance of industrial PPE by using deep learning techniques. Safety science, 148, 105646.
Xiong, R., & Tang, P. (2021). Pose guided anchoring for detecting proper use of personal protective equipment. Automation in Construction, 130, 103828.
Yu, Y., Li, H., Yang, X., Kong, L., Luo, X., & Wong, A. Y. (2019). An automatic and non-invasive physical fatigue assessment method for construction workers. Automation in construction, 103, 1-12.
Zhang, J., Yin, H., Zhang, J., Yang, G., Qin, J., & He, L. (2022). Real-time mental stress detection using multimodality expressions with a deep learning framework. Frontiers in Neuroscience, 16.
Zhu, C., Zhu, J., Bu, T., & Gao, X. (2022). Monitoring and Identification of Road Construction Safety Factors via UAV. Sensors, 22(22), 8797.
Zimbelman, E. G., & Keefe, R. F. (2021). Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations. PLoS One, 16(5), e0250624.
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