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“La Fabbrica si Misura”: An Anthropometric Study of Workers at FCA Italian Plants

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Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) (IEA 2018)

Abstract

Nowadays, the use of updated and reliable anthropometric database is of great importance. Anthropometric data play a key role in workstation design, safe use of machineries and protective equipment supplying. The variability of anthropometric data between populations of different ethnic groups, or even different countries or geographical areas of origin, cannot be neglected. The ISO 7250 technical standard reports a collection of statistical data of anthropometric measurements for different nations. As far as the Italian adult population is concerned, the anthropometric survey, referred in the standard, was carried out nearly 30 years ago. The interest and value of a more recent anthropometric database, that could be representative of the Italian working population, was the driver for the project “La Fabbrica si Misura” (The Factory Measures itself).

The present work describes the methodology used to acquire the data on selected FCA plants in different Italian regions. Data collection was aimed in particular at the body measurements that are crucial for the creation of virtual manikins to be used in Digital Human Models, and are not always available in the standard database. A minimum of 3000 volunteer subjects per gender, aged between 18 and 65, participated in the initiative. The paper presents a preliminary statistical analysis on the data for the male sample. An initial comparison with the reference database of the ISO international technical standard is also reported.

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Correspondence to Raffaele Castellone .

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Spada, S., Castellone, R., Cavatorta, M.P. (2019). “La Fabbrica si Misura”: An Anthropometric Study of Workers at FCA Italian Plants. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 826. Springer, Cham. https://doi.org/10.1007/978-3-319-96065-4_44

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