Body height and waist circumference of young Swiss men as assessed by 3D laser-based photonic scans and by manual anthropometric measurements

Beckmann, Claudia; Aldakak, Lafi; Eppenberger, Patrick; Rühli, Frank J; Staub, Kaspar; Bender, Nicole

doi:10.7717/peerj.8095

Cited by 9 publications

(18 citation statements)

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“…2016-01625), and all participants were required to sign a detailed form to ensure informed consent to later scientific evaluation of their data. Participation was voluntary, without further selection criteria such as origin, demographic factors, or socioeconomic status [6]. A semimobile 3D photonic full-body scanner (Anthroscan VITUSbodyscan, Human Solution, Kaiserslautern, Germany) was used for body surface data acquisition, providing spatial resolution of <1 mm, with a point density of 300 data points per cm 3 .…”

Section: Methodsmentioning

confidence: 99%

“…3D models used for our study were taken from a prior study in which the correlation of height and WC measured by BS and manual anthropometric measurements in young Swiss men was investigated [ 6 , 17 ]. This prior study included a cross-sectional part and a re-examination 4 months later.…”

Section: Methodsmentioning

confidence: 99%

“…Height and weight were measured with a standard stadiometer (Seca 274, Seca AG, Reinach, Switzerland). Further details of the measuring protocol are given in prior studies [ 6 , 17 , 21 ]. Raw scan datasets—in a proprietary point cloud format—were later triangulated and closed using the standard scanner software (Anthroscan, Version 3.5.3, Human Solutions, Kaiserslautern, Germany) and exported to the .obj file format.…”

Section: Methodsmentioning

confidence: 99%

“…The use of 3D photonic body scans (BS) to assess body shape in epidemiologic studies and for daily fitness tracking is on the rise, thanks to developments in equipment and measurement methods that provide fast, reproducible, and increasingly accurate results [1][2][3][4][5][6][7]. However, an essential limitation of many currently available approaches is the lack of a precise approximation of adipose tissue distribution, particularly in more obese individuals, despite its relevance for assessing the risk for metabolic and cardiovascular diseases [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Associations between relative body fat and areal body surface roughness characteristics in 3D photonic body scans—a proof of feasibility

2021

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Introduction A reliable and accurate estimate of the percentage and distribution of adipose tissue in the human body is essential for evaluating the risk of developing chronic and noncommunicable diseases. A precise and differentiated method, which at the same time is fast, noninvasive, and straightforward to perform, would, therefore, be desirable. We sought a new approach to this research area by linking a person’s relative body fat with their body surface’s areal roughness characteristics. Materials and methods For this feasibility study, we compared areal surface roughness characteristics, assessed from 3D photonic full-body scans of 76 Swiss young men, and compared the results with body impedance-based estimates of relative body fat. We developed an innovative method for characterizing the areal surface roughness distribution of a person’s entire body, in a similar approach as it is currently used in geoscience or material science applications. We then performed a statistical analysis using different linear and stepwise regression models. Results In a stepwise regression analysis of areal surface roughness frequency tables, a combination of standard deviation, interquartile range, and mode showed the best association with relative body fat (R2 = 0.55, p < 0.0001). The best results were achieved by calculating the arithmetic mean height, capable of explaining up to three-quarters of the variance in relative body fat (R2 = 0.74, p < 0.001). Discussion and conclusion This study shows that areal surface roughness characteristics assessed from 3D photonic whole-body scans associate well with relative body fat, therefore representing a viable new approach to improve current 3D scanner-based methods for determining body composition and obesity-associated health risks. Further investigations may validate our method with other data or provide a more detailed understanding of the relation between the body’s areal surface characteristics and adipose tissue distribution by including larger and more diverse populations or focusing on particular body segments.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Associations between relative body fat and areal body surface roughness characteristics in 3D photonic body scans—a proof of feasibility

2021

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“…This study was part of a larger project in which we examined 104 recruits at the beginning of their Armed Forces basic training [52]. The cross-sectional baseline examinations involved young males (age range 18.8-24.4 years, mean 20.5 years, SD = 1.1 years) recruited by the Swiss Armed Forces and were conducted in Kloten (Canton of Zurich) from 21 March to 24 March 2017.…”

Section: Methodsmentioning

confidence: 99%

Multiple measures derived from 3D photonic body scans improve predictions of fat and muscle mass in young Swiss men

et al. 2020

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Introduction Digital tools like 3D laser-based photonic scanners, which can assess external anthropometric measurements for population based studies, and predict body composition, are gaining in importance. Here we focus on a) systematic deviation between manually determined and scanned standard measurements, b) differences regarding the strength of association between these standard measurements and body composition, and c) improving these predictions of body composition by considering additional scan measurements. Methods We analysed 104 men aged 19-23. Bioelectrical Impedance Analysis was used to estimate whole body fat mass, visceral fat mass and skeletal muscle mass (SMM). For the 3D body scans, an Anthroscan VITUSbodyscan was used to automatically obtain 90 body shape measurements. Manual anthropometric measurements (height, weight, waist circumference) were also taken. Results Scanned and manually measured height, waist circumference, waist-to-height-ratio, and BMI were strongly correlated (Spearman Rho>0.96), however we also found systematic differences. When these variables were used to predict body fat or muscle mass, explained variation and prediction standard errors were similar between scanned and manual measurements. The univariable predictions performed well for both visceral fat (r 2 up to 0.92) and absolute fat mass (AFM, r 2 up to 0.87) but not for SMM (r 2 up to 0.54). Of the 90 body scanner measures used in the multivariable prediction models, belly circumference and middle hip circumference were the most important predictors of body fat content. Stepwise forward model selection using the AIC criterion showed that the best predictive power (r 2 up to 0.99) was achieved with models including 49 scanner measurements.

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Detection of body shape changes in obesity monitoring using image processing techniques

Ergün,

Aktepe,

Koca

2024

Sci Rep

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Body measurements are primarily made with a tape measure. In measurements taken with a tape measure, the inability to take measurements from the same part of the body each time, incorrect positioning of the tape measure, the occurrence of incorrect measurements, and the need for a person to take the measurements are significant problems in the traditional measurement method. Due to the social distancing rule that must be followed during the Covid-19 pandemic, the close contact between the person to be measured and the person taking the measurement became the starting point of this study. This study focuses on the detecting body shape changes using image processing techniques with 2D imaging. The novelty of the work is that non-contact body measurements are taken more accurately and reliably using the cosine theorem. Regular monitoring of obese patients is important in combating obesity, which is also the source of many health problems. In the monitoring of obese patients, it is necessary to determine the rate of slimming in areas where fat accumulation is intense. The error margin between the real measurements of human models and the calculated measurements was calculated as an average of ± 5.16% for waistline and an average of ± 4.58% for hip size. The cosine theorem was used instead of the ellipse formula used in the literature, and it was observed that the cosine theorem obtained results closer to reality. It is also thought that the developed system will be beneficial not only for extracting body measurements but also for extracting body measurements contactless in the textile sector. The study demonstrates the feasibility of image processing for non-contact body anthropometry and shape tracking.

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Body height and waist circumference of young Swiss men as assessed by 3D laser-based photonic scans and by manual anthropometric measurements

Cited by 9 publications

References 30 publications

Associations between relative body fat and areal body surface roughness characteristics in 3D photonic body scans—a proof of feasibility

Associations between relative body fat and areal body surface roughness characteristics in 3D photonic body scans—a proof of feasibility

Multiple measures derived from 3D photonic body scans improve predictions of fat and muscle mass in young Swiss men

Detection of body shape changes in obesity monitoring using image processing techniques

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