3DBODY.TECH 2017 - Paper 17.179

A. Tursi et al., "Automatic Detection of Body Landmarks in Human Body Scans - Lower Limb Analysis for Biomedical and Footwear Applications", in Proc. of 3DBODY.TECH 2017 - 8th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Montreal QC, Canada, 11-12 Oct. 2017, pp. 179-191, doi:10.15221/17.179.

Title:

Automatic Detection of Body Landmarks in Human Body Scans - Lower Limb Analysis for Biomedical and Footwear Applications

Authors:

Alessandra TURSI, Gregorij KURILLO, Ruzena BAJCSY

University of Berkeley, Berkeley CA, USA

Abstract:

This paper presents a pilot study of detection and deformation tracking of 3D body landmarks. In particular, the primary goal of this research was to develop methodology for automated design of individualized 3D printed ankle and foot orthoses for persons affected by Duchenne and Becker muscular dystrophies. The results of this study could have further implications on the design of not only the static orthoses but also active exoskeleton systems and general body deformation tracking.
Although body scanners are becoming increasingly popular in the recent years, due to new advances in 3D sensing, there are several outstanding issues related to the automated extraction and standardization of body measurements. Modern body scanners are able to virtually reproduce body shapes with an extreme accuracy. However, the result of a body scan is an indistinct 3D mesh, usually without any easily recognizable body landmarks that could provide consistent and robust measurements of the anthropometric parameters related to the particular body part. Although some of the body scanning software packages provide automatic or semi-automatic segmentation and extraction of different body measures, the algorithms are proprietary and do not easily compare between different systems.
In summary, the important open questions are the following: 1) how to uniquely recognize the same points on scans from different people in order to extract comparable measurements, 2) how to extract the same measures starting from two body scans of the same person, acquired and processed by different body scanning hardware. This paper first briefly describes the state of art in body scanning and body measurement extraction while focusing in particular on lower limbs. The lower limbs are one of the more challenging body parts for scanning as there are very few easily recognizable body landmarks. Majority of the landmarks described in the literature require palpation to locate them and therefore cannot be detected purely from visual data. Presented analysis is focused on first obtaining 3D scans of the foot, ankle and lower leg in different configurations (e.g. flexion/extension) using a custom-developed contraption that allows for scanning in a secure static position. In our tests, we used the most common landmarks reported in medical and design literature. The scans obtained in the different configurations are then aligned based on the landmarks while the algorithm extracts the skin surface deformation in different cross-sectional plains. Based on the change in curvature, it is possible to extrapolate this information across the surface of the lower limb to estimate how much soft tissue deformation is exhibited during the observed configuration change.
By quantifying the deformation across the surface of the lower limb, it is possible to determine the sections of the limb where the deformations are small. This information is crucial for customized design of biomedical and footwear products. For example, the areas that exhibit low deformation/movement can be put in close and more rigid contact with the orthosis, while the areas with large deformation require additional padding and softer materials to prevent skin irritation and discomfort.

Details:

Full paper: 17.179.pdf
Proceedings: 3DBODY.TECH 2017, 11-12 Oct. 2017, Montreal QC, Canada
Pages: 179-191
DOI: 10.15221/17.179

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