Comparison of body fat estimates using 3D digital laser scans, direct manual anthropometry, and DXA in men

Garlie, Todd N.; Obusek, John P.; Corner, Brian D.; Zambraski, Edward J.

doi:10.1002/ajhb.21069

Cited by 19 publications

(28 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Body fatness predicted with 3D imaging was significantly correlated with HD reference values with a wide distribution of the values (SEE = 7.95%). Garlie et al [18] also reported no differences in body fat values among military personnel measured with 3D optical imaging, DXA, and military-specific anthropometric models. They noted, however, a higher concordance correlation coefficient for 3D imaging compared to anthropometry rather than to 3D imaging compared to DXA (0.96 vs. 0.74).…”

Section: Discussionmentioning

confidence: 99%

“…Although widely publicized and used as fat analyzers, BIA devices are limited in accuracy in terms of assessing body fat largely due to several faulty assumptions such as a steady hydration of the lean tissues [13,14,15]. Alternatively, whole-body optical scanning devices to estimate body volume, size, and regional body circumferences are emerging [16,17,18,19]. They yield reasonably accurate measurements but require relatively costly and cumbersome equipment, large space requirements and lighting that makes them impractical for routine personal use [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Smartphone Application for Personal Assessments of Body Composition and Phenotyping

Farina

Spataro

Lorenzo

et al. 2016

Sensors

View full text Add to dashboard Cite

Personal assessments of body phenotype can enhance success in weight management but are limited by the lack of availability of practical methods. We describe a novel smart phone application of digital photography (DP) and determine its validity to estimate fat mass (FM). This approach utilizes the percent (%) occupancy of an individual lateral whole-body digital image and regions indicative of adipose accumulation associated with increased risk of cardio-metabolic disease. We measured 117 healthy adults (63 females and 54 males aged 19 to 65 years) with DP and dual X-ray absorptiometry (DXA) and report here the development and validation of this application. Inter-observer variability of the determination of % occupancy was 0.02%. Predicted and reference FM values were significantly related in females (R2 = 0.949, SEE = 2.83) and males (R2 = 0.907, SEE = 2.71). Differences between predicted and measured FM values were small (0.02 kg, p = 0.96 and 0.07 kg, p = 0.96) for females and males, respectively. No significant bias was found; limits of agreement ranged from 5.6 to −5.4 kg for females and from 5.6 to −5.7 kg for males. These promising results indicate that DP is a practical and valid method for personal body composition assessments.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Smartphone Application for Personal Assessments of Body Composition and Phenotyping

Farina

Spataro

Lorenzo

et al. 2016

Sensors

View full text Add to dashboard Cite

show abstract

“…However, application of such a protocol requires subjects to breathe out fully whilst being scanned, which might be limiting for some individuals as it is for UWW. A study of military personnel found good agreement between % fat derived from 3D scanning (Cyberware) with in-house software and DXA (GE Lunar Prodigy) [55] , although the strategy for assessing the lung volume to subtract from scan volume before calculation of % fat was not stated. In this respect, 3D and DXA scanning share the commonality of "undisclosed algorithms" for arriving at fatness and thereby, unquantified error, which future research must address.…”

Section: Ultrasound (Us)mentioning

confidence: 99%

Current Status of Body Composition Assessment in Sport

et al. 2012

View full text Add to dashboard Cite

Dette er siste tekst-versjon av artikkelen, og den kan inneholde små forskjeller fra forlagets pdf-versjon. Forlagets pdf-versjon finner du på link.springer.com: http://dx.doi.org/10.2165/11597140-000000000-00000 This is the final text version of the article, and it may contain minor differences from the journal's pdf version. The original publication is available at link.springer.com: http://dx.doi.org/10.2165/11597140-000000000-00000 . Figure 3. Semi-automatic image evaluation: The edge detection algorithm for subcutaneous adipose tissue (SAT) thickness determination enables selecting areas of interest, distances (d US ) measurement series, color-coding of distance values, and statistical evaluations [48] . In this example of a SAT-layer above the triceps muscle, with the transducer held parallel to the humerus, 119 d US values ranging from 2.3 mm to 4.3 mm were automatically detected by the algorithm; the median was 3.4 mm (c = 1470 m/s). Layers and interfaces: A: gel, B: gel-epidermis, C: dermis, D: dermis-SAT, E: SAT, F: SAT-fascia of muscle, G: muscle. [77] . Abbreviations: mid dist = middle distance track runners; long dist = long distance runners; Scot long dist = Scottish long distance runners; SASI mid dist = South Australian Sports Institute middle distance track runners; SASI sprint = sprint runners; SASI jump = jumpers. Figure 6. Selected skinfold ratios in extremely lean male and female endurance athletes, and mean values from 106 male and 33 female athletes [80] . 4 ABSTRACTQuantifying human body composition has played an important role in monitoring all athlete performance and training regimens, but especially so in gravitational, weight class and aesthetic sports wherein the tissue composition of the body profoundly affects performance or adjudication. Over the past century, a myriad of techniques and equations have been proposed, but all have some inherent problems, whether in measurement methodology or in the assumptions they make. To date, there is no universally applicable criterion or "gold standard" methodology for body composition assessment.Having considered issues of accuracy, repeatability and utility, the multi-component model might be employed as a performance or selection criterion, provided the selected model accounts for variability in the density of FFM in its computation. However, when profiling change in interventions, single methods whose raw data are surrogates for body composition (with the notable exception of the BMI) remain useful.

show abstract

“…The use of 3-D body scanners for studying obesity is not new; several scanner manufacturers provide software tools to collect sets of measurements that can be used to analyze the body shape, and these anthropometric data have been recently correlated with metabolic parameters in validation studies [11], [12], [13], [14]. Methods used to extract measurements in these studies are, however, not standardized, usually require standard posture and compute girths at landmarks derived by markers or manually corrected in case of failure of automatic detection.…”

Section: Related Workmentioning

confidence: 99%

Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

Giachetti

Lovato

Piscitelli

et al. 2015

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

In this paper, we present an automatic tool for estimating geometrical parameters from 3-D human scans independent on pose and robustly against the topological noise. It is based on an automatic segmentation of body parts exploiting curve skeleton processing and ad hoc heuristics able to remove problems 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 3-D body scans of normal and overweight subjects acquired in different poses are highly correlated with the 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 the body fat and trunk fat. Regression models based on our automatic measurements can be used to predict body fat values reasonably well.

show abstract

Comparison of body fat estimates using 3D digital laser scans, direct manual anthropometry, and DXA in men

Cited by 19 publications

References 41 publications

A Smartphone Application for Personal Assessments of Body Composition and Phenotyping

A Smartphone Application for Personal Assessments of Body Composition and Phenotyping

Current Status of Body Composition Assessment in Sport

Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

Contact Info

Product

Resources

About