3DPatBody: 3D dataset of human bodies of a patagonian population and their anthropometric measurements

The study of human body shape using classical anthropometric techniques is often problematic due to several error sources. Instead, 3D models and representations provide more accurate registrations, which are stable across acquisitions, and enable more precise, systematic, and fast measuring capabilities. Thus, the same person can be scanned several times and precise differential measurements can be established in an accurate manner. Here we present 3DPatBody, a dataset including 3D body scans, with their corresponding 3D point clouds and anthropometric measurements, from a sample of a Patagonian population (female=211, male=87, other=1). The sample is of scientific interest since it is representative of a phenotype characterized by both its biomedical meaning as a descriptor of overweight and obesity, and its population-specific nature related to ancestry and/or local environmental factors. The acquired 3D models were used to compare shape variables against classical anthropometric data. The shape indicators proved to be accurate predictors of classical indices, also adding geometric characteristics that reflect more properly the shape of the body under study.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2024-12-18
DOI
https://doi.org/10.1038/s41597-024-04189-w
Citations
2
Primary Topic
3D Shape Modeling and Analysis
Type
article
Field-Weighted Citation Impact
0.75

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

3DPatBody: 3D dataset of human bodies of a patagonian population and their anthropometric measurements

Rolando González‐José, Bruno Pazos, Carolina Paschetta, Tamara Teodoroff et al.
2 citations
Scientific Data
3D Shape Modeling and Analysis
0.75
article

3DPatBody: 3D dataset of human bodies of a patagonian population and their anthropometric measurements

Rolando González‐José, Bruno Pazos, Carolina Paschetta, Tamara Teodoroff, Magda Alexandra Trujillo‐Jiménez, Claudio Augusto Delrieux, Leonardo Morales, Anahí Ruderman, Virgínia Ramallo, L. Perez, Soledad de Azevedo
article en
2 citations

Abstract

The study of human body shape using classical anthropometric techniques is often problematic due to several error sources. Instead, 3D models and representations provide more accurate registrations, which are stable across acquisitions, and enable more precise, systematic, and fast measuring capabilities. Thus, the same person can be scanned several times and precise differential measurements can be established in an accurate manner. Here we present 3DPatBody, a dataset including 3D body scans, with their corresponding 3D point clouds and anthropometric measurements, from a sample of a Patagonian population (female=211, male=87, other=1). The sample is of scientific interest since it is representative of a phenotype characterized by both its biomedical meaning as a descriptor of overweight and obesity, and its population-specific nature related to ancestry and/or local environmental factors. The acquired 3D models were used to compare shape variables against classical anthropometric data. The shape indicators proved to be accurate predictors of classical indices, also adding geometric characteristics that reflect more properly the shape of the body under study.

Scientific DataVol. 11(1)
Ministerio de Ciencia y Tecnología (AR), Centro Científico Tecnológico - Bahía Blanca (AR), Centro Científico Tecnológico Patagónico (AR), Universidad Nacional del Sur (AR)
Consejo Nacional de Investigaciones Científicas y Técnicas
Good health and well-being
Openalex Percentile: Top 31%
3D Shape Modeling and Analysis
0.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.