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Applying generalised feedforward neural networks to classifying industrial jobs in terms of risk of low back disorders

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Applying generalised feedforward neural networks to classifying industrial jobs in terms of risk of low back disorders

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dc.contributor.author Asensio Cuesta, Sabina es_ES
dc.contributor.author Diego Más, José Antonio es_ES
dc.contributor.author Alcaide Marzal, Jorge es_ES
dc.date.accessioned 2017-12-21T08:03:10Z
dc.date.available 2017-12-21T08:03:10Z
dc.date.issued 2010 es_ES
dc.identifier.issn 0169-8141 es_ES
dc.identifier.uri http://hdl.handle.net/10251/93223
dc.description.abstract [EN] This paper describes a new approach for the development of artificial neural networks applied to classifying the risk of low back disorders (LBDs) presented by certain lifting jobs. The development process of neural networks for classification problems and the influence of network architecture on its prediction and generalisation capabilities are analysed. The phenomenon of overfitting and its relationship to the number of network connections, and the size of the training data set are discussed. The new approach uses complex architecture networks and early stopping of training procedures to avoid overfitting. It is thus compared with previous studies and its results and advantages over them are assessed. Such comparison shows that this approach allows for the development of neural networks for the classification of industrial jobs according to their risk of causing LBDs in a way faster than previous procedures. The neural network obtained is able to correctly classify 81.6% of highly repetitive lifting industrial jobs as posing low or high-risk for LBDs, from five independent variables representing biomechanical risk factors. The neural network obtained has been implemented in a software application which focuses on risk analysis and prevention of the injuries caused by tasks involving manual lifting in the industrial environment. Relevance to industry The approach described in this paper allows neural networks to be developed for the prediction of musculoskeletal disorders in a way that is faster and easier than with previous procedures. It can also be applied to risk prediction in other areas of ergonomics. The neural network obtained can be used by the ergonomist as a diagnostic system, enabling jobs to be classified into two categories (low-risk and high-risk) according to the associated likelihood of causing low back disorders. This system provides a higher proportion of correct classifications than other previous models. es_ES
dc.description.sponsorship We would like to thank the Universidad Politécnica de Valencia for its assistance to carry out this research through its Support Programme for Research and Development and its Funding Projects PAID-06-09/2902 and PAID-05-09/4215.
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation UPV/PAID-06-09/2902
dc.relation UPV/PAID-05-09/4215
dc.relation.ispartof International Journal of Industrial Ergonomics es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Artificial neural networks es_ES
dc.subject Low back disorders es_ES
dc.subject Assessment of lifting jobs es_ES
dc.subject.classification PROYECTOS DE INGENIERIA es_ES
dc.title Applying generalised feedforward neural networks to classifying industrial jobs in terms of risk of low back disorders es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.ergon.2010.04.007 es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Proyectos de Ingeniería - Departament de Projectes d'Enginyeria es_ES
dc.description.bibliographicCitation Asensio Cuesta, S.; Diego Más, JA.; Alcaide Marzal, J. (2010). Applying generalised feedforward neural networks to classifying industrial jobs in terms of risk of low back disorders. International Journal of Industrial Ergonomics. 40(6):629-635. doi:10.1016/j.ergon.2010.04.007 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.ergon.2010.04.007 es_ES
dc.description.upvformatpinicio 629 es_ES
dc.description.upvformatpfin 635 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 40 es_ES
dc.description.issue 6 es_ES
dc.relation.pasarela S\40245 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES


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