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Industry Research

Translating digital anthropometry measurements obtained from different 3D body image scanners



Body image scanners are used in industry and research to reliably provide a wealth of anthropometric measurements within seconds. The demonstrated utility of the scanners drives the current proliferation of more commercially available devices that rely on their own reference body sites and proprietary algorithms to output anthropometric measurements. Since each scanner relies on its own algorithms, measurements obtained from different scanners cannot directly be combined or compared.


To develop mathematical models that translate anthropometric measurements between the three popular commercially available scanners.


A unique database that contained 3D scanner measurements in the same individuals from three different scanners (Styku, Human Solutions, and Fit3D) was used to develop linear regression models that translate anthropometric measurements between each scanner. A limits of agreement analysis was performed between Fit3D and Styku against Human Solutions measurements and the coefficient of determination, bias, and 95% confidence interval were calculated. The models were then applied to normalized scanner data from four different studies to compare the results of a k-means cluster analysis between studies. A scree plot was used to determine the optimal number of clusters derived from each study.


Correlations ranged between R2 = 0.63 (Styku and Human Solutions mid-thigh circumference) to R2 = 0.97 (Human Solutions and Fit3D neck circumference). In general, Fit3D had better agreement with Human Solutions compared to Styku. The widest disagreement was found in chest circumference (Fit3D (bias = 2.30, 95% CI = [−3.83, 8.43]) and Styku (bias = −5.60, 95% CI = [−10.98, −0.22]). The optimal number of body shape clusters in each of the four studies was consistently 5.


The newly developed models that translate measurements between the scanners Styku and Fit3D to predict Human Solutions measurements make it possible to standardize data between scanners allowing for data pooling and comparison.

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Fig. 1: Data preprocessing flowchart: symmetric body parts such as left and right bicep circumferences averaged.
Fig. 2: An image of a Styku scanned avatar (on right) and circumferences that are output from the Styku scan device software.
Fig. 3: Scree plots describing how much variability in body shapes were identified from the k-means cluster analysis.
Fig. 4: Heat map of defined by standard deviations ranging from −1.5 to 1 away from the mean values of each measurement by cluster.

Data availability

Data will be made available by request after review by the United States Military Academy Chief Data Officer, Paul Evangelista. The review will determine the risks to personnel for data sharing and is dependent on the type of request. Requests can be made at


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MS and DMT were funded by NIH U54TR004279.

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Authors and Affiliations



DMT, MS, MM, and NA conceived this study. JS, NG and SBH provided data and data interpretation for this study. NA performed the statistical analysis under supervision from DMT, MS, and DR. DMT and NA prepared the first draft of the manuscript. MM, BB, and BAS provided insight into US Army problems with different scanners. All authors reviewed multiple drafts of the manuscript.

Corresponding author

Correspondence to Diana M. Thomas.

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Competing interests

The US Military Academy has no affiliation with any industry and DMT, NA, DR, and MS have no conflicts of interest or financial disclosures. also have no conflicts of interest or financial disclosures. JS has an investigator initiated grant with Hologic. JS is on the scientific advisory board for Styku and Hologic. JS has some stock options for being on the boards of Styku and formerly on BodySpec (value < $10k). Steven B. Heymsfield reports his role on the Medical Advisory Boards of Tanita Corporation, Amgen, and Medifast; he is also an Amazon Scholar.

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Ashby, N., Jake LaPorte, G., Richardson, D. et al. Translating digital anthropometry measurements obtained from different 3D body image scanners. Eur J Clin Nutr 77, 872–880 (2023).

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