Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention.

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.

Internet usage has grown dramatically since the early years of its inception. The rich field of data provided by internet users in interaction with digital media content can provide insight into web-based navigation behaviour and underlying psychological dimensions. Human-Computer Interaction in the web is an underutilized source of data for understanding human online behaviour. While researchers and usability testing services do use these sources to analyse human behaviour and user experience, access to the diverse range of other potentially useful data available during web-based interaction for research is limited. In this paper, we propose a novel tool in the form of a web browser extension, referred to as Latent, which can be used to simultaneously capture information from different sources while users interact with digital content. The data acquisition capabilities of Latent makes it suitable for various research purposes, ranging from studies of usability to decision-making and personality. A particular advantage of Latent is that the method and control of data acquisition is completely transparent to the user. We present the architecture of the web browser extension, describe the data that can be acquired, and report on the residual impact of the tool on the user’s computer processing resources.

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.