Body shape and size modelling using regression analysis and neural network prediction
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.
Journal: Proceedings of the ASME Design Engineering Technical Conference
Volume: 3
ISBN: 9780791846346
DOI: 10.1115/DETC2014-35707
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.
Source: Scopus
BODY SHAPE AND SIZE MODELLING USING REGRESSION ANALYSIS AND NEURAL NETWORK PREDICTION
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K., Isaacs, R. and ASME
Journal: PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2014, VOL 3
ISBN: 978-0-7918-4634-6
Source: Web of Science (Lite)
Body Shape and Size Modelling Using Regression Analysis and Neural Network Prediction
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.
Conference: ASME 2014 International Design, Engineering Technical Conferences and the Computers and Information in Engineering Conference, IDETC/CIE
Dates: 17-20 August 2014
Journal: asmedigitalcollection.asme.org
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 offer estimates for patients from known data and learnt patient measurements. The developed model takes standard anthropometric patient data of body mass, height, age, gender and body shape as inputs to produce a novel anatomical model of the body including spinal ligaments in the back for epidural training. 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.
Source: Manual
Preferred by: Venky Dubey