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

Int J Environ Res Public Health. 2022 Aug 3;19(15):9552. doi: 10.3390/ijerph19159552.


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.

Keywords: explainable AI (XAI); functional work ability; musculoskeletal symptoms; natural language processing; occupational health protection profiles.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Absenteeism
  • Artificial Intelligence
  • Humans
  • Longitudinal Studies
  • Occupational Health*
  • Occupations

Grant support

This work was partly supported by science and technology foundation (FCT), under projects OPERATOR (ref. 04/SI/2019) and PREVOCUPAI (DSAIPA/AI/0105/2019), and Ph.D. grants PD/BDE/142816/2018 and PD/BDE/142973/2018.