Body shape and size modelling using regression analysis and neural network prediction

N. Vaughan, V.N. Dubey, M.Y.K. Wee, R. Isaacs

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The aim of this research is to build a patient-specific virtual body shape model for patients of various Body Mass Index (BMI) and body shape. This will enable simulated epidural procedure on patients of various body characteristics, to increase trainee skill, reduce injuries and litigation costs. Regression analysis (RA) and artificial neural networks (ANN) were implemented to accurately calculate body shape in a data-driven approach. Epidural simulator software was developed containing a screen to enter patient characteristics. When the patient BMI is adjusted, the modelled body shape and tissue layer thickness updates allowing patient specific simulation. The model uses anthropometric measurements as input: body mass, height, age, gender and body shape. The developed model enables a virtual representation of any actual patient to be built based on their measured parameters for epidural rehearsal prior to in-vivo procedure. Copyright © 2014 by ASME.
    Original languageEnglish
    Title of host publicationProceedings of the ASME Design Engineering Technical Conference
    PublisherAmerican Society of Mechanical Engineers(ASME)
    ISBN (Electronic)978-079184634-6
    DOIs
    Publication statusPublished - 2014
    EventASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - Buffalo, United States
    Duration: 17 Aug 201420 Aug 2014

    Conference

    ConferenceASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
    Abbreviated titleIDETC/CIE
    CountryUnited States
    CityBuffalo
    Period17/08/1420/08/14

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  • Cite this

    Vaughan, N., Dubey, V. N., Wee, M. Y. K., & Isaacs, R. (2014). Body shape and size modelling using regression analysis and neural network prediction. In Proceedings of the ASME Design Engineering Technical Conference American Society of Mechanical Engineers(ASME). https://doi.org/10.1115/DETC2014-35707