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Projeto de investigação
Occupational Exposure Logging - Mechanisms for visualization, search and inference on individual and collective ergonomic information
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Human-Centered Explainable Artificial Intelligence
Publication . Mollaei, Nafiseh; Fujão, Carlos; Silva, Luís; Rodrigues, João; Cepeda, Cátia; Gamboa, Hugo; DF – Departamento de Física; LIBPhys-UNL; Molecular Diversity Preservation International (MDPI)
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.
A genetic algorithm approach to design job rotation schedules ensuring homogeneity and diversity of exposure in the automotive industry
Publication . Assunção, Ana; Mollaei, Nafiseh; Rodrigues, João; Fujão, Carlos; Osório, Daniel; Veloso, António P.; Gamboa, Hugo; Carnide, Filomena; LIBPhys-UNL; Elsevier
Job rotation is a work organization strategy with increasing popularity, given its benefits for workers and companies, especially those working with manufacturing. This study proposes a formulation to help the team leader in an assembly line of the automotive industry to achieve job rotation schedules based on three major criteria: improve diversity, ensure homogeneity, and thus reduce exposure level. The formulation relied on a genetic algorithm, that took into consideration the biomechanical risk factors (EAWS), workers’ qualifications, and the organizational aspects of the assembly line. Moreover, the job rotation plan formulated by the genetic algorithm formulation was compared with the solution provided by the team leader in a real life-environment. The formulation proved to be a reliable solution to design job rotation plans for increasing diversity, decreasing exposure, and balancing homogeneity within workers, achieving better results in all of the outcomes when compared with the job rotation schedules created by the team leader. Additionally, this solution was less time-consuming for the team leader than a manual implementation. This study provides a much-needed solution to the job rotation issue in the manufacturing industry, with the genetic algorithm taking less time and showing better results than the job rotations created by the team leaders.
Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
Publication . Gamboa, Patrícia; Varandas, Rui; Rodrigues, João; Cepeda, Cátia; Quaresma, Cláudia; Gamboa, Hugo; LIBPhys-UNL; MDPI - Multidisciplinary Digital Publishing Institute
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.
Classification of Volatile Compounds with Morphological Analysis of e-nose Response
Publication . Alves, Rita; Rodrigues, João; Ramou, Efthymia; Palma, Susana; Roque, Ana; Gamboa, Hugo; LIBPhys-UNL; DQ - Departamento de Química; UCIBIO - Applied Molecular Biosciences Unit
Electronic noses (e-noses) mimic human olfaction, by identifying Volatile Organic Compounds (VOCs). This work presents a novel approach that successfully classifies 11 known VOCs using the signals generated by sensing gels in an in-house developed e-nose. The proposed signals' analysis methodology is based on the generated signals' morphology for each VOC since different sensing gels produce signals with different shapes when exposed to the same VOC. For this study, two different gel formulations were considered, and an average f1-score of 84% and 71% was obtained, respectively. Moreover, a standard method in time series classification was used to compare the performances. Even though this comparison reveals that the morphological approach is not as good as the 1-nearest neighbour with euclidean distance, it shows the possibility of using descriptive sentences with text mining techniques to perform VOC classification.
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Fundação para a Ciência e a Tecnologia
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PD/BDE/142816/2018