3DBODY.TECH 2021 - Paper 21.33

B. Smith et al., "Anthropometric Evaluation of a 3D Scanning Mobile Application", Proc. of 3DBODY.TECH 2021 - 12th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Lugano, Switzerland, 19-20 Oct. 2021, #33, https://doi.org/10.15221/21.33.


Anthropometric Evaluation of a 3D Scanning Mobile Application


Brooke SMITH, Marcelline DECHENAUD, Steven B. HEYMSFIELD

LSU Pennington Biomedical Research Center, Baton Rouge LA, USA


3D optical imaging systems have increasingly been used in clinical and research settings for evaluation of body size, shape, and composition. These devices tend to be costly, stationary, and often require some level of technical training to operate and provide maintenance for. In recent years, smartphone applications with 3D imaging capabilities have been developed in order to make these scans more accessible. The aim of the current study was to evaluate a smartphone imaging application (MeThreeSixty, SizeStream, Cary, NC) that is portable, readily available to smartphone users, free to download, and can be used by consumers in their home environment.
To evaluate the apps' accuracy in quantifying standard anthropometric dimensions, we acquired MeThreeSixty and SizeStream SS20 (a professional-grade imaging system; SizeStream, Cary, NC) digital outputs (.obj files) from a sample of healthy adults and processed them with universal software (US) developed in our laboratory. The device-agnostic US was developed to standardize and measure anthropometric dimensions (circumferences, lengths, surface areas, and volumes) from 3D body scans, using identical anatomic landmark definitions across devices.
The current study aimed to compare conventional body circumferences at standard locations to those digitally generated from US analysis of the 3D body images created by the MeThreeSixty application and the SizeStream SS20.
The MeThreeSixty app guides participants while it acquires two standing images, a frontal and lateral view. Proprietary software post-processing of the two-dimensional images allows for extraction of front and lateral silhouettes, from which major body landmarks are identified. Machine learning then allows a 3D body template to be distorted in an organized manner and fit to the scanned body shape. The large 3D body scan library available to the developer includes a range of body shapes as a reference for the distorting the template when fitting the scanned body.

Participants were 10 healthy adults ([mean+-SD] age 41.8+-22.4yrs; BMI, 27.7+-5.8 kg/m2). Conventional standardized anthropometric measurements were made with a flexible tape at the waist, hips, mid-thighs, and upper arms. Scans were performed with the MeThreeSixty app and the SizeStream SS20 in each participant. Acquired 3D .obj image files were processed using the US.

Overall, circumferences measured with the app-US approach agreed closely with those acquired with the flexible tape as shown in Table 1 (absolute errors, 0.79-1.96 cm; root-mean square errors, 1.32-3.97 cm). Results were largely equivalent to those from the SizeStream SS20 scanner (Table 2; mean absolute errors, 0.71-5.6 cm; RMSE, 2.00-11.75 cm).

This initial evaluation of the MeThreeSixty app opens up the possibility of acquiring digital standardized anthropometric measurements that can be used to derive estimates of body fat, other clinically relevant compartments, and health risk predictions at little to no cost. The app's accuracy is comparable to conventional flexible tape-anthropometry administered by a trained technician and similar to data acquired by a professional-grade 3D optical scanner. The app's accessibility makes it useful for monitoring and tracking bodymeasurements in settings outside of specialized facilities. A larger sample to follow up on these pilot evaluations is now being collected.


3D body scanning, mobile application, 3D optical


Extended abstract: 2133smith.pdf
Proceedings: 3DBODY.TECH 2021, 19-20 Oct. 2021, Lugano, Switzerland
Paper id#: 33
DOI: 10.15221/21.33
Presentation video: 3DBodyTech2021_33_Smith.mp4

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