A device-agnostic shape model for automated body composition estimates from 3D optical scans.

ABSTRACT:

Background

Many predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods.

Purpose

We introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw

SUBMITTER: Tian IY 

PROVIDER: S-EPMC9990507 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature