Paper 15.085

W. De Keyzer et al., "Anthropometric baseD Estimation of adiPoSity - The ADEPS Project", in Proc. of 6th Int. Conf. on 3D Body Scanning Technologies, Lugano, Switzerland, 2015, pp. 85-91,


Anthropometric baseD Estimation of adiPoSity - The ADEPS Project


Willem DE KEYZER 1, Frank DERUYCK 2, Benjamin VAN DER SMISSEN 3, Simona VASILE 4, Joris COOLS 4, Alexandra DE RAEVE 4, Stefaan DE HENAUW 5,1, Peter VAN RANSBEECK 3

1 Bio- and food sciences, University College Ghent, Ghent, Belgium;
2 Exact sciences, University College Ghent, Ghent, Belgium;
3 Mechatronics, University College Ghent, Ghent, Belgium;
4 Fashion, textile and wood technology, University College Ghent, Ghent, Belgium;
5 Public health, Ghent University, Ghent, Belgium


Worldwide, the prevalence of obesity has increased dramatically. Obesity is a condition associated with an increased amount of adipose tissue in the body and is linked to increased morbidity and mortality. In clinical practice and research, determination of body fat percentage (BF) is not always possible due to limitations in available resources (time, equipment, budget, etc.). Therefore, weight indexes like the body mass index (BMI; body weight (kg)/body height2 (m)) offer a major advantage because they are quick and inexpensive to use. Although the BMI is extensively used, it does not take into account fat or muscle distribution in the body and is unable to differentiate adipose tissue from lean body mass. Hence, it has been suggested that future research in body composition measurement should focus more on body shape and volume rather than body mass. With the advent of 3D body scanning technology, it is possible to obtain accurate and reliable anthropometric measures of an individual within a few minutes. Also, 3D body scans provide information on an individual's body volume and body shape. From this data, BF can be calculated using a two component model of the human body based on known densities of fat and fat-free mass. In addition, a 3D digital model of the body allows for visualization of regional fat deposition and division of the total body into segments for more detailed data analysis compared to total body measurements. The ADEPS project builds on experience with 3D body scanning gained during the SMARTFIT project and is looking to merge areas of expertise in medicine, health care and technology. The principal aim of the ADEPS project is to examine the extent to which BF can be predicted using anthropometric measurements obtained from 3D body scans using a structured white light full body scanner. A comprehensive dataset of anthropometric measurements obtained by 3D body scanning is available within the research unit. From these data, samples of candidate anthropometrical measurements will be selected using a Design of Experiments approach. Regression analysis on sequentially selected datasets will yield anthropometric predictors which will be used to create a predictive model for BF as calculated from total body volume. This model will then be validated by comparing the anthropometric-based BF predictions with BF obtained from the Bod Pod air-displacement plethysmography system (reference method and gold standard for total body volume measurement). Finally, the regression equation will be converted into a nomogram for routine practical use in healthcare and research practice. The present article describes the research project and its methods and reports on the progress and intermediate results of the ADEPS project.


Full paper: 15.085.pdf
Proceedings: 3DBST 2015, 27-28 Oct. 2015, Lugano, Switzerland
Pages: 85-91
DOI: 10.15221/15.085

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