Quantifying the ergonomic risk and biomechanical exposure in automotive assembly lines

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiênciasAs Lesões Músculo-esqueléticas Relacionadas com o Trabalho (LMERTs) representam 15% do número total de anos de vida perdidos por danos físicos ou doenças com a sua génese no trabalho. De entre os fatores de risco para as LMERTs, no presente estudo, destacam-se as posturas corporais relacionadas com o trabalho. A exposição biomecânica a posturas consideradas prejudiciais tem um impacto negativo na saúde dos trabalhadores, na economia das empresas e na sociedade. A fim de aperceber a prática recorrente de posturas prejudiciais no local de trabalho, têm sido invocados métodos de autoavaliação ergonómica, nos quais o risco é percecionado pelo próprio trabalhador; observacionais, conduzidos por peritos em ergonomia; e de medição direta, que recorrem ao emprego de soluções tecnológicas para a recolha e monitorização objetiva de variáveis pertinentes para a avaliação ergonómica. Porém, frequentemente e em contexto industrial, são apenas aplicados métodos de autoavaliação e observacionais, apesar da medição direta constituir uma solução mais notável. O advento da Internet das Coisas vem revelar a oportunidade da utilização de wearables para uma recolha de dados omnipresente, amplificando a quantidade de dados disponível com o fim de uma avaliação ergonómica mais individual e imparcial. Deste modo, estudos relativos à avaliação ergonómica no local de trabalho têm primado pelo uso de wearables com vista a monitorização do movimento humano. A presente dissertação respeita ao desenvolvimento de uma abordagem automática para a avaliação ergonómica em contexto industrial. As contribuições principais são o desenvolvimento de (1) uma rotina de captura de movimento, através da utilização de um sistema wearable com sensores inerciais; (2) uma framework computacional para a monitorização do movimento da parte superior do corpo humano, em termos dos ângulos relativos às articulações entre os segmentos anatómicos, estimados com recurso à cinemática inversa; e (3) implementações computacionais de especificações estabelecidas e relativas aos fatores de risco de postura para a quantificação da exposição biomecânica e consequente risco ergonómico em âmbito ocupacional. Subsequentemente, as implementações das especificações foram aplicadas por forma a prover constatações acerca de um caso de estudo das linhas de montagem de automóveis da Volkswagen Autoeuropa. O estudo delineado foi dividido em dois cenários: validação e avaliação. A validação consistiu em comparar os dados provisionados por um sistema inercial de referência e determinados através dos métodos desenvolvidos. Para tal, usaram-se dados de sensores inerciais recolhidos em laboratório (N = 8 participantes) e nas linhas de montagem de automóveis (N = 9 participantes). A avaliação consistiu em quantificar a exposição biomecânica e consequente risco ergonómico respeitantes ao caso de estudo, empregando as estimativas angulares calculadas pela framework desenvolvida, e a partir dos dados recolhidos com o nosso sistema nas linhas de montagem de automóveis. Os resultados revelaram que a framework proposta tem o potencial para ser aplicada na monitorização de tarefas industriais. A avaliação ergonómica é mais lata através da medição direta, desvendando diferenças de exposição biomecânica e consequente risco ergonómico entre operadores.Work-related musculoskeletal disorders (WRMSDs) represent 15% of the total number of life-years lost due to work-related injuries and illness. Among WRMSDs’ risk factors, work-related postures are underlined in this research. Biomechanical exposure to hazardous postures negatively impacts workers’ health, enterprises’ economy, and society. Toward the apperception about the recurrent practice of hazardous postures in the workplace, self-reported, observational, and directly measured ergonomic assessment methods have been established. However, only self-reported and observational approaches are enforced on a more frequent basis, besides directly measured is a more compelling choice. The advent of the Internet of Things poses the opportunity of using wearables in the direction of ubiquitous data collection, increasing the amount of available data for a more personal and non-biased ergonomic evaluation. As follows, over workplace ergonomics research, wearables have been used to monitor human motion. The dissertation developed an automatic approach to ergonomic evaluation in industrial contexts. Its main contributions are the development of (1) a motion capture routine using inertial sensors; (2) a computational framework to monitor human upper body motion, in terms of joints’ angles, through inverse kinematics; and (3) computational implementations of posture risk factors specifications to quantify the biomechanical exposure and consequent ergonomic risk in occupational settings. Subsequently, specifications implementations were applied to provide insights in consideration of a case study from Volkswagen Autoeuropa automotive assembly lines. The research was divided into two scenarios: validation and evaluation. Validation consisted of comparing data provided by a ground truth inertial motion capture system and computed throughout the developed methods. Hence, inertial sensors’ data, collected in the laboratory (N = 8 participants) and automotive assembly lines (N = 9 participants) settings, were used. The evaluation consisted of quantifying the biomechanical exposure and consequent ergonomic risk concerning the case study, using angular estimates computed through the developed framework and about data collected in automotive assembly lines. The results revealed that the proposed framework has the potential to be applied to monitor industrial tasks. The ergonomic evaluation is more comprehensive through direct measures, uncovering differences about biomechanical exposure and consequent ergonomic risk among operators

A Deep Learning approach to prevent problematic movements of industrial workers based on inertial sensors

Nowadays, manufacturing industries still face difficulties applying traditional Work-related MusculoSkeletal Disorders (WMSDs) risk assessment methods due to the high effort required by a continuous data collection when using observational methods. An interesting solution is to adopt Inertial Measurement Units (IMUs) to automate the data collection, thus supporting occupational health professionals. In this paper, we propose a deep learning approach to predict human motion based on IMU data with the goal of preventing industrial worker problematic movements that can arise during repetitive actions. The proposed system includes an initial Madgwick filter to merge the raw inertial tri-axis sensor data into a single angle orientation time series. Then, a Machine Learning (ML) algorithm is trained with the obtained time series, allowing to build a forecasting model. The effectiveness of the developed system was validated by using an open-source dataset composed of different motions for the upper body collected in a laboratory environment, aiming to monitor the abduction/adduction angle of the arm. Firstly, distinct ML algorithms were compared for a single angle orientation time series prediction, including: three Long Short-Term Memory (LSTM) methods - a one layer, a stacked layer and a Sequence to Sequence (Seq2Seq) model; and three non deep learning methods - a Multiple Linear Regression, a Random Forest and a Support Vector Machine. The best results were provided by the Seq2Seq LSTM model, which was further evaluated for WMSD prevention by considering 11 human subject datasets and two evaluation procedures (single person and multiple person training and testing). Overall, interesting results were achieved, particularly for multiple person evaluation, where the proposed Seq2Seq LSTM has shown an excellent capability to anticipate problematic movements.This article is a result of the project STVgoDigital - Digitalization of the T&C sector (POCI-01-0247-FEDER-046086), supported by COMPETE 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional De velopment Fund (ERDF)

Posture Risk Assessment in an Automotive Assembly Line using Inertial Sensors

Publisher Copyright: AuthorMusculoskeletal 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.publishersversionepub_ahead_of_prin

Psychological Distress in Men during the COVID-19 Pandemic in Brazil: The Role of the Sociodemographic Variables, Uncertainty, and Social Support

Objective: To analyze the relationships between sociodemographic variables, intolerance to uncertainty (INT), social support, and psychological distress (i.e., indicators of Common Mental Disorders (CMDs) and perceived stress (PS)) in Brazilian men during the COVID-19 pandemic. Methods: A cross-sectional study with national coverage, of the web survey type, and conducted with 1006 Brazilian men during the period of social circulation restriction imposed by the health authorities in Brazil for suppression of the coronavirus and control of the pandemic. Structural equation modeling analysis was performed. Results: Statistically significant direct effects of race/skin color ( = 0.268; p-value < 0.001), socioeconomic status (SES) ( = 0.306; p-value < 0.001), household composition( = 0.281; p-value < 0.001), PS ( = 0.513; p-value < 0.001), and INT ( = 0.421; p-value < 0.001) were evidenced in the occurrence of CMDs. Black-skinned men with higher SES, living alone, and with higher PS and INT levels presented higher prevalence values of CMDs. Conclusions: High levels of PS and INT were the factors that presented the strongest associations with the occurrence of CMDs among the men. It is necessary to implement actions to reduce the stress-generating sources as well as to promote an increase in resilience and the development of intrinsic reinforcements to deal with uncertain threats.info:eu-repo/semantics/publishedVersio

A ansiedade dos pais influencia o comportamento das crianças no atendimento odontológico? Uma scoping review

Introdução: Alguns estudos se propuseram a avaliar a relação entre ansiedade materna e o comportamento do paciente infantil no consultório odontológico. Entretanto, não há consenso seansiedade dos pais se relaciona ao comportamento da criança no atendimento odontológico. Objetivo: Resumir sistematicamente as evidências disponíveis sobre a relação entre ansiedade dos pais e comportamento dos filhos no tratamento odontológico. Materiais e Métodos: Foi realizada uma pesquisa na base de dados Pubmed/Medline para busca de artigos publicados de 1969 até maio de 2023. Para isso, foram utilizados descritores relacionados ao tema. Estudos clínicos foram selecionados, tabulados e analisados descritivamente. Resultados e discussão: De 477 estudos potencialmente elegíveis identificados, 5 artigos foram incluídos. Cada um desses estudos foi realizado em países diferentes: Bulgária, Arábia Saudita, Índia, Grécia e Brasil.A escala Corah Dental AnxietyScale foi a mais utilizada nos estudos para avaliação da ansiedade dos pais frente ao atendimento odontológico. Do mesmo modo, a escala de medo infantil Children´s Fear Survey Schedule foi a mais empregada para medir o medo das crianças no consultório. Observou-se que 3 estudos evidenciaram relação direta entre a ansiedade dentária nos pais e o comportamento das crianças no tratamento odontológico. Os outros dois estudos mostraram resultados de não associação. Contudo, as metodologias dos estudos diferem entre si, o que dificulta uma análise única dos resultados encontrados por cada estudo. Conclusão: Concluiu-se que, embora alguns artigos relatem que a ansiedade dos pais influencia o comportamento dos filhos durante o atendimento odontológico, ainda não há evidências suficientes para confirmar essa correlação

A systematic review

Funding Information: This work was supported by Project OPERATOR (NORTE01-0247-FEDER-045910), cofinanced by the ERDF—European Regional Development Fund through the North Portugal Regional Operational Program and Lisbon Regional Operational Program and by the Portuguese Foundation for Science and Technology , under the MIT Portugal Program (2019 Open Call for Flagship projects). M.D was supported by the Doctoral Grant SFRH/BD/151375/2021 financed by the Portuguese Foundation for Science and Technology (FCT) , and with funds from the State Budget, under the MIT Portugal Program . Publisher Copyright: © 2023 The AuthorsCardiovascular disease (CVD) is the leading cause of death worldwide. Health and safety hazards and risk factors in the workplace are associated with occupational CVD, though inconsistent evidence of causal associations represents a knowledge gap. The assessment of physical load on the cardiovascular system in relation to work different risk factors and occupational groups is necessary, if preventative measures for occupational CVD are to be better tailored to workers’ needs. The pertinent literature reports the use of different objective and subjective metrics to evaluate the cardiovascular load (CVL). We aimed to identify how cardiovascular stress is assessed in the workplace and to bring together related evidence-based recommendations for preventative measures. Hence, we systematically searched the Google Scholar database for corresponding publications to a) gather metrics used to assess CVL, b) summarize the related risk factors investigated, c) report the occupational groups and activities targeted in these studies, and d) compile recommendations resulting from these studies. The majority of studies reported objective measures, mostly Relative Heart Rate. The identified risk factors included work environment factors, general job features (such as the number of working hours), task-related factors and individual characteristics of the worker. Most studies focused on the industrial sector, namely, the manufacturing industry and construction were the two most frequent occupational groups, due to high exposure to risk factors. Few evidence-based recommendations were identified, though guidelines to promote safety and productivity were proposed. Our results encourage further research on CVL, occupational risk and CVD.publishersversionpublishe

Time Series Subsequence Search Library

Subsequence search and distance measures are crucial tools in time series data mining. This paper presents our Python package entitled TSSEARCH, which provides a comprehensive set of methods for subsequence search and similarity measurement in time series. These methods are user-customizable for more flexibility and efficient integration into real deployment scenarios. TSSEARCH enables fast exploratory time series data analysis and was validated in the context of human activity recognition and indoor localization.publishersversionpublishe