Robust automatic measurement of 3D scanned models for human body fat estimation.

In this paper we present an automatic tool for estimating geometrical parameters from 3D human scans independenton pose, and robustly against topological noise. It is based on an automatic segmentation of body parts exploiting curve skeleton processing and ad hoc heuristics able to removeproblems due to different acquisition poses and body types. The software is able to locate body trunk and limbs, detect their directions and compute parameters like volumes, areas, girths and lengths.Experimental results demonstrate that measurements provided by our system on 3D body scans of normal and overweight subjects acquired in different poses are highly correlated with body fat estimates obtained on the same subjects with Dual-Energy X-rays absorptiometry (DXA) scanning. In particular, maximal lengths and girths, not requiring precise localization of anatomical landmarks, demonstrate a good correlation (up to 96%) with body fat and trunk fat. Regression models based on our automatical measurements can be used to predict body fat values reasonably well.