Paper 15.092

A. Giachetti et al., "Automatic Analysis of 3D Scans of Professional Athletes", in Proc. of 6th Int. Conf. on 3D Body Scanning Technologies, Lugano, Switzerland, 2015, pp. 92-97, https://doi.org/10.15221/15.092.

Title:

Automatic Analysis of 3D Scans of Professional Athletes

Authors:

Andrea GIACHETTI 1, Francesco PISCITELLI 2, Valentina CAVEDON 2, Chiara MILANESE 2, Carlo ZANCANARO 2

1 Dpt. of Computer Science, University of Verona, Verona, Italy;
2 Lab. of Anthropometry & Body Composition, Dpt. of Neurological and Movement Sciences, Verona, Italy

Abstract:

In this paper we present an analysis of body features of professional athletes performed using 3D body scanning with automatic processing and measurement of acquired 3D meshes and body composition data from dual-energy X-ray absorptiometry (DXA) acquisition. The aim of the work was to investigate whether professional male athletes practicing different sports show sport-specific features in terms of specific body dimensions and body composition. To perform the study, we collected 3D body scans and DXA scans of 211 players practicing basketball, soccer, golf, handball, rugb, volleyball as well as a control group of 38 physically active young adults.
A set of geometrical parameters were extracted automatically from the models exploiting a custom software tool based on body segmentation based curve skeleton analysis and symmetry based heuristics and previously applied with success to the analysis of body fat.
By measuring these body features from the scans, we could perform statistical analysis of their correlation with body composition parameters and also analyze differences among sports, in order to understand which features are more characterizing individual sports.
Furthermore, we checked if combinations of the selected feature measurements could possibly be characteristics of the disciplines and/or distinguish between professional athletes and physically active subjects, by visually analyzing the multidimensional feature space and testing automatic "athlete" or "discipline" labeling in a leave one out classification framework using different feature combinations and different classification methods. This allowed us to extract the most relevant features related to each different group.

Details:

Full paper: 15.092.pdf
Proceedings: 3DBST 2015, 27-28 Oct. 2015, Lugano, Switzerland
Pages: 92-97
DOI: 10.15221/15.092

Copyright notice

© Hometrica Consulting - Dr. Nicola D'Apuzzo, Switzerland, www.hometrica.ch.
Reproduction of the proceedings or any parts thereof (excluding short quotations for the use in the preparation of reviews and technical and scientific papers) may be made only after obtaining the specific approval of the publisher. The papers appearing in the proceedings reflect the author's opinions. Their inclusion in these publications does not necessary constitute endorsement by the editor or by the publisher. Authors retain all rights to individual papers.

Proceedings of 3DBODY.TECH International Conferences on 3D Body Scanning & Processing Technologies, © Hometrica Consulting, Switzerland