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Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

Author

Listed:
  • Nafiseh Mollaei

    (LIBPhys, Physics Department, Faculty of Sciences and Technology, Nova University of Lisbon, 2825-149 Caparica, Portugal)

  • Carlos Fujao

    (Volkswagen Autoeuropa, Industrial Engineering and Lean Management, Quinta da Marquesa, 2954-024 Quinta do Anjo, Portugal)

  • Luis Silva

    (LIBPhys, Physics Department, Faculty of Sciences and Technology, Nova University of Lisbon, 2825-149 Caparica, Portugal)

  • Joao Rodrigues

    (LIBPhys, Physics Department, Faculty of Sciences and Technology, Nova University of Lisbon, 2825-149 Caparica, Portugal)

  • Catia Cepeda

    (LIBPhys, Physics Department, Faculty of Sciences and Technology, Nova University of Lisbon, 2825-149 Caparica, Portugal)

  • Hugo Gamboa

    (LIBPhys, Physics Department, Faculty of Sciences and Technology, Nova University of Lisbon, 2825-149 Caparica, Portugal)

Abstract

In automotive and industrial settings, occupational physicians are responsible for monitoring workers’ health protection profiles. Workers’ Functional Work Ability (FWA) status is used to create Occupational Health Protection Profiles (OHPP). This is a novel longitudinal study in comparison with previous research that has predominantly relied on the causality and explainability of human-understandable models for industrial technical teams like ergonomists. The application of artificial intelligence can support the decision-making to go from a worker’s Functional Work Ability to explanations by integrating explainability into medical (restriction) and support in contexts of individual, work-related, and organizational risk conditions. A sample of 7857 for the prognosis part of OHPP based on Functional Work Ability in the Portuguese language in the automotive industry was taken from 2019 to 2021. The most suitable regression models to predict the next medical appointment for the workers’ body parts protection were the models based on CatBoost regression, with an RMSLE of 0.84 and 1.23 weeks (mean error), respectively. CatBoost algorithm is also used to predict the next body part severity of OHPP. This information can help our understanding of potential risk factors for OHPP and identify warning signs of the early stages of musculoskeletal symptoms and work-related absenteeism.

Suggested Citation

  • Nafiseh Mollaei & Carlos Fujao & Luis Silva & Joao Rodrigues & Catia Cepeda & Hugo Gamboa, 2022. "Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms," IJERPH, MDPI, vol. 19(15), pages 1-27, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9552-:d:879712
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    References listed on IDEAS

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    1. Galizzi, Monica & Boden, Leslie I., 2003. "The return to work of injured workers: evidence from matched unemployment insurance and workers' compensation data," Labour Economics, Elsevier, vol. 10(3), pages 311-337, June.
    2. Ana Assunção & Vera Moniz-Pereira & Carlos Fujão & Sarah Bernardes & António P. Veloso & Filomena Carnide, 2021. "Predictive Factors of Short-Term Related Musculoskeletal Pain in the Automotive Industry," IJERPH, MDPI, vol. 18(24), pages 1-12, December.
    3. Syed Atif Ali Shah & Irfan Uddin & Furqan Aziz & Shafiq Ahmad & Mahmoud Ahmad Al-Khasawneh & Mohamed Sharaf, 2020. "An Enhanced Deep Neural Network for Predicting Workplace Absenteeism," Complexity, Hindawi, vol. 2020, pages 1-12, February.
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