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Body Shape and Size Modelling Using Regression Analysis and Neural Network Prediction

[+] Author Affiliations
Neil Vaughan, Venketesh N. Dubey

Bournemouth University, Poole, Dorset, UK

Michael Y. K. Wee, Richard Isaacs

Poole Hospital NHS Foundation Trust, Poole, Dorset, UK

Paper No. DETC2014-35707, pp. V003T12A011; 2 pages
doi:10.1115/DETC2014-35707
From:
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 16th International Conference on Advanced Vehicle Technologies; 11th International Conference on Design Education; 7th Frontiers in Biomedical Devices
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4634-6
  • Copyright © 2014 by ASME

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

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