ArticlePloS one2016
Novel Anthropometry Based on 3D-Bodyscans Applied to a Large Population Based Cohort.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Smartphone and tablet-based 3D scanner for anthropometric assessments in adults: a systematic review of reliability, validity, and accuracy.Biomedical engineering online · 2025Pooled it
- 3D body shape-based phenotyping for precision risk stratification of metabolic syndrome.Lipids in health and disease · 2026Article
- Patient perceptions of three-dimensional (3D) surface imaging technology and traditional methods used to assess anthropometry.Obesity pillars · 2024Article
- Translating digital anthropometry measurements obtained from different 3D body image scanners.European journal of clinical nutrition · 2023Review
- Laser-Based 3D Body Scanning Reveals a Higher Prevalence of Abdominal Obesity than Tape Measurements: Results from a Population-Based Sample.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning-based obesity classification considering 3D body scanner measurements.Scientific reports · 2023Article
- Cohort Profile: The LIFE-Adult-Study.International journal of epidemiology · 2023Article
- Body surface scan anthropometrics are related to cardiorespiratory fitness in the general population.Scientific reports · 2022Article
- Machine learning modeling practices to support the principles of AI and ethics in nutrition research.Nutrition & diabetes · 2022Article
- Torso Shape Improves the Prediction of Body Fat Magnitude and Distribution.International journal of environmental research and public health · 2022Article
- Article
- 3D Scanning of the Forearm for Orthosis and HMI Applications.Frontiers in robotics and AI · 2021Article
- How shape-based anthropometry can complement traditional anthropometric techniques: a cross-sectional study.Scientific reports · 2020Article
- Machine learning prediction of combat basic training injury from 3D body shape images.PloS one · 2020Article
- A data-driven classification of 3D foot types by archetypal shapes based on landmarks.PloS one · 2020Article
- The aging human body shape.NPJ aging and mechanisms of disease · 2020Article
- Multiple measures derived from 3D photonic body scans improve predictions of fat and muscle mass in young Swiss men.PloS one · 2020Article
- A Novel Hybrid Model for Visceral Adipose Tissue Prediction using Shape Descriptors.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
- Determinants of leptin in human breast milk: results of the Ulm SPATZ Health Study.International journal of obesity (2005) · 2019Article
- A collaborative, mixed-methods evaluation of a low-cost, handheld 3D imaging system for child anthropometry.Maternal & child nutrition · 2019Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional (3D) whole body scanners are increasingly used as precise measuring tools for the rapid quantification of anthropometric measures in epidemiological studies. We analyzed 3D whole body scanning data of nearly 10,000 participants of a cohort collected from the adult population of Leipzig, one of the largest cities in Eastern Germany. We present a novel approach for the systematic analysis of this data which aims at identifying distinguishable clusters of body shapes called body types. In the first step, our method aggregates body measures provided by the scanner into meta-measures, each representing one relevant dimension of the body shape. In a next step, we stratified the cohort into body types and assessed their stability and dependence on the size of the underlying cohort. Using self-organizing maps (SOM) we identified thirteen robust meta-measures and fifteen body types comprising between 1 and 18 percent of the total cohort size. Thirteen of them are virtually gender specific (six for women and seven for men) and thus reflect most abundant body shapes of women and men. Two body types include both women and men, and describe androgynous body shapes that lack typical gender specific features. The body types disentangle a large variability of body shapes enabling distinctions which go beyond the traditional indices such as body mass index, the waist-to-height ratio, the waist-to-hip ratio and the mortality-hazard ABSI-index. In a next step, we will link the identified body types with disease predispositions to study how size and shape of the human body impact health and disease.
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Registered trials
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