Musculoskeletal disorders (MSD) are a highly prevalent work-related health problem. Biomechanical exposure to hazardous postures during work is a risk factor for the development of MSD. This study focused on developing an inertial sensor-based approach to evaluate posture in industrial contexts, particularly in automotive assembly lines. The analysis was divided into two stages: 1) a comparative study of joint angles calculated during movements of the upper body segments using the proposed motion tracking framework and the ones provided by a state-of-the-art inertial motion capture system and 2) a work-related posture risk evaluation of operators working in an automative assembly line. For the comparative study, we selected data collected in laboratory (N = 8 participants) and assembly line settings (N = 9 participants), while for the work-related posture risk evaluation, we only considered data acquired within the automotive assembly line. The results revealed that the proposed framework could be applied to track industrial tasks movements performed on the sagittal plane, and the posture evaluation uncovered posture risk differences among different operators that are not considered in traditional posture risk assessment instruments.

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.

To determine the short-term associations between biomechanical risk factors and musculoskeletal symptoms in the upper limbs and low back in an automotive company, a longitudinal study with a follow-up of 4 days was conducted in a sample of 228 workers of the assembly and paint areas. Data were analyzed using generalized estimating equations, calculating the crude and adjusted model for age, sex, seniority, and intensity of pain at baseline. The interactions found were the same for both models. Workers were divided in low-risk and high-risk group for posture, force, exposure, percentage of cycle time with the arm at/above shoulder level, and with the trunk flexed or/and strongly flexed. The predictive factors showed by time × group effect were found between pain intensity on the left shoulder for posture (β = 0.221, p < 0.001), percentage of time with the trunk flexed (β = 0.136, p = 0.030) and overall exposure (β = 0.140, p = 0.013). A time × group interactions were observed, namely between neck pain and posture (β = 0.218, p = 0.005) and right wrist and force (β = 0.107, p = 0.044). Workers in the high-risk group were more prone to report unfavorable effects on their self-reported musculoskeletal pain, across a workweek when exposed to specific risk factor, being posture important to neck, right wrist and left shoulder pain.

Proper working conditions must be one of the employers’ main concerns in any type of company but particularly in work locations where the employees are chronically exposed to hazardous compounds, like factories and production lines. Regarding this challenge, the present research addresses the mapping of a car factory painting line to possibly toxic volatile organic compounds emitted by all the coatings and chemicals used during the work shifts for the future evaluation of employees’ exposure. For the first time, a Gas Chromatography–Ion Mobility Spectrometry device was employed for the in situ detection of volatile organic compounds in an automotive factory. A total of 26 analytes were detected at nine different locations, of which 15 VOCs were accurately identified. Pure chemical-grade substances were used for the development of the VOC database. Although quantitative analysis was not the goal of this study, a calibration model was presented to one analyte for exemplificative purposes. Relative intensity profiles were plotted for all locations, revealing that some indoor VOCs can reach intensity levels up to 60 times higher than in outdoor air samples. The achieved results proved that the painting line has an abundant number of VOCs emitted from different sources and may lead to serious health risks for the employees. Additional studies shall be developed in the painting line for quantitative evaluation of the existing VOCs and their influence on the employees’ health conditions.

Occupational Health Protection (OHP) is mandatory by law and can be accomplished by considering the participation of others besides occupational physicians. The data shared can originate knowledge that might influence other processes related to occupational risk prevention. In this study, we used Artificial Intelligence (AI) methods to extract patterns among records shared under these circumstances over two years in the automotive industry. Records featuring OHP data against physical working conditions were selected, and a database of 383 profiles was designed. As Occupational Health Protection profiles under study are associated with work functional ability reduction, the body part(s) (n = 14) where it occurred were identified. Association Rules (ARs) coupled with Natural Language Processing techniques were applied to find meaningful hidden relationships and to identify the occurrence of protection profiles being assigned to at least two body parts simultaneously. After filtering ARs using three metrics (support, confidence, and lift), 54 ARs were found. The distribution of simultaneous body parts is presented as being higher in Special projects (n = 5). The results can use in: (i) design a multi-site body parts functional work ability (loss) model; (ii) model the capacity of organizations to retain workers in their working settings and (iii) prevent work-related musculoskeletal symptoms.

The active workforce is increasingly aging. However workload, as well as working time and intensity, sometimes remains unchanged. This can be an even more critical situation in older people, since occupational exposure associated with aging, will further reduce the muscle's ability to generate energy, which in turn facilitates the development of these age-related syndromes. This study aims to identify the normative values of handgrip strength for Portuguese workers in the automotive industry. About 1225 employees were invited to participate in the study. The final sample consisted of 656 employees in the assembly area. The handgrip strength was measured in kilograms (kg) using the Jamar digital dynamometer. Sex-specific profiles of handgrip strength were designed by the Ordinary Least Square regression (OLS) analysis, where height, age, age squared, and height squared are entered into the models as determining factors of the maximum grip strength in both female and male groups. The peak mean values of handgrip strength in the group of women was 34 kg in the age group of 35–39 years, and in the group of men the peak mean was 52 kg in the age group of 25–34 years. The most pronounced decline in the female group appears in the age of 30–34 years of about 4 kg and the male group the decline occurs at 2kg below the peak force, in the age group of 40–57 year-olds. This study used a cut-off at 2 SD below by the sex-specific peak mean. Normative values can help delineate the career path of workers because they portray risk values according to age, height, and gender. The normative values assist health and engineering professionals and ergonomists in adjusting task demands to the morphological and strength characteristics of the workers.