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Genetic Algorithm and VR for Assessing the Level of Expertise of Maintenance Operator

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Extended Reality (XR Salento 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15027))

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Abstract

The study aims to find the features for assessing the level of a maintenance operator. A genetic algorithm is used to identify the most relevant features and reduce their size. Based on 30 different features entered, we demonstrate that only three operator-level evaluation features provide a good classification. Virtual reality was used to simulate maintenance operations, collect data, and validate our method for identifying the most relevant features.

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Acknowledgements

This work was supported by French government funding managed by the National Research Agency in the framework of the project VIMACO - ANR-21-CE10-0009. This work was supported by French government funding managed by the National Research Agency under the Investments for the Future program (PIA) grant ANR-21- ESRE-0030 (CONTINUUM).

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Authors and Affiliations

  1. Arts et Métiers Institute of Technology, LISPEN, 71100, Chalon-Sur-Saône, France

    Axel Foltyn, Florence Danglade & Frédéric Merienne

  2. Université de Bourgogne, LISPEN, 71100, Chalon-Sur-Saône, France

    Christophe Guillet

Authors
  1. Axel Foltyn
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  2. Christophe Guillet
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  3. Florence Danglade
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  4. Frédéric Merienne
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Corresponding author

Correspondence to Axel Foltyn .

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Editors and Affiliations

  1. University of Salento, Lecce, Italy

    Lucio Tommaso De Paolis

  2. University of Naples Federico II, Naples, Italy

    Pasquale Arpaia

  3. CNR-STIIMA, Lecco, Italy

    Marco Sacco

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Foltyn, A., Guillet, C., Danglade, F., Merienne, F. (2024). Genetic Algorithm and VR for Assessing the Level of Expertise of Maintenance Operator. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2024. Lecture Notes in Computer Science, vol 15027. Springer, Cham. https://doi.org/10.1007/978-3-031-71707-9_31

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  • DOI: https://doi.org/10.1007/978-3-031-71707-9_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-71706-2

  • Online ISBN: 978-3-031-71707-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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