Abstract
Background
The precision of digital anthropometry through 3-dimensional (3D) scanning has been established for relatively large, expensive, non-portable systems. The comparative performance of modern mobile applications is unclear.
Subjects/methods
Forty-six adults (age: 23.3 ± 5.3 y; BMI: 24.4 ± 4.1 kg/m2) were assessed in duplicate using: (1) a mobile phone application capturing two individual 2D images, (2) a mobile phone application capturing serial images collected during a subject’s complete rotation, (3) a traditional scanner with a time of flight infrared sensor collecting visual data from a subject being rotated on a mechanical turntable, and (4) a commercial measuring booth with structured light technology using 20 infrared depth sensors positioned in the booth. The absolute and relative technical error of measurement (TEM) and intraclass correlation coefficient (ICC) for each method were established.
Results
Averaged across circumferences, the absolute TEM, relative TEM, and ICC were (1) 0.9 cm, 1.5%, and 0.975; (2) 0.5 cm, 0.9%, and 0.986; (3) 0.8 cm, 1.5%, and 0.974; and (4) 0.6 cm, 1.1%, and 0.985. For total body volume, these values were (1) 2.2 L, 3.0%, and 0.978; (2) 0.8 L, 1.1%, and 0.997; (3) 0.7 L, 0.9%, and 0.998; and (4) 0.8 L, 1.1%, and 0.996, with segmental volumes demonstrating higher relative errors.
Conclusion
A 3D scanning mobile phone application involving full rotation of subjects in front of a smartphone camera exhibited similar reliability to larger, less portable, more expensive 3D scanners. In contrast, larger errors were observed for a mobile scanning application utilizing two 2D images, although the technical errors were acceptable for some applications.
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Introduction
Digital anthropometry through 3-dimensional (3D) scanning has received substantial interest in recent years, as evidenced by a rapid increase in research publications employing this technology and greater availability of commercially available scanning options [1, 2]. While most previous investigations focused on the use of large, relatively non-portable scanners, there is increased interest in 3D scanning using mobile devices and applications. Traditionally, mobile applications providing digital anthropometric estimations have relied on few photographic, two-dimensional (2D) images obtained of the static human body, such as a single frontal image and single lateral image, with subsequent image processing techniques producing a 3D humanoid avatar for anthropometric evaluation [3,4,5]. However, there are potential concerns with this approach as it assumes that an adequate representation of the nuances of body shape can be obtained from these limited views of the body. As such, newer mobile applications have started capturing much larger amounts of visual information. For example, some emerging mobile scanning applications prompt the subject’s complete rotation in front of the mobile device’s front-facing camera, thereby allowing images of the body to be captured from every angle. As a result of this more comprehensive data acquisition and advanced modeling techniques, a truer representation of the 3D shape of the body can be generated [6]. Although the concept of subject rotation in front of a camera or sensor has previously been employed by non-mobile scanners, these have typically involved the participant being positioned in a static A-pose on a mechanical turntable, minimizing subject movement [7]. In contrast, recent mobile technologies allow the participant to “spin” in place on any flooring, which results in complex body movements, such as rocking from side to side during each small step contributing to the complete body rotation. While these body movements could be expected to increase technical error of assessments, advances in modeling of dynamic human bodies and non-rigid avatar reconstruction have assuaged these concerns, with recent research demonstrating very low technical error of such methods [6].
Based on the continued development and implementation of 3D scanning technologies, as well as the unique data acquisition and processing procedures used by distinct commercial entities, continued investigation of the precision of anthropometric outputs obtained from these procedures is warranted. Mobile phone technologies may deserve particular attention due to the tremendous accessibility they provide relative to traditional scanning methods. Therefore, the purpose of the present study was to evaluate the reliability of repeated measurements (precision) from four distinct 3D scanning technologies, including two mobile phone applications and two traditional scanning systems. These 3D scanning technologies represent a spectrum of size, cost, complexity, and acquisition methods. The goal of this analysis was to understand the precision (i.e., reliability) of mobile phone scanning technologies, which are more accessible to many practitioners and consumers, as compared to traditional scanners with previously demonstrated reliability.
Subjects and methods
Overview
At a single research visit, adult participants were assessed using four 3D scanning technologies: (1) a mobile phone application producing a 3D avatar from two individual 2D images (frontal and lateral) from a mobile phone camera (Mobile Fit, Size Stream, Cary, NC, USA), (2) a mobile phone application producing a 3D avatar from serial images collected during a subject’s complete rotation on the flooring in front of a mobile phone camera (Prism Labs, Los Angeles, CA, USA), (3) a traditional commercially available scanner that produces a 3D avatar using data collected from a subject being rotated on a mechanical turntable in front of a separate tower containing a time of flight infrared sensor (Styku®, Los Angeles, CA, USA), and (4) a commercial measuring booth that produces a 3D avatar using 20 infrared depth sensors positioned at varying heights in the four corners of the booth, using structured light technology (SS20, Size Stream, Cary, NC, USA). For each device, duplicate assessments were performed and used for precision estimation. The present analysis includes individuals who completed duplicate assessments for all four of the 3D scanning techniques.
Participants
Individuals ≥18 years of age were recruited for participation. Prospective participants were excluded if they had a disease or medical condition that could reasonably influence body composition, had a history of major body altering surgery, had implanted electrical devices, or were currently pregnant or breastfeeding. This study was approved by the Texas Tech University Institutional Review Board (IRB #2022-610), and all participants provided written informed consent prior to participation.
Laboratory visit
Participants reported to the research laboratory after an overnight (≥8 h) fast from ingestion of foods, fluids, and other substances, as well as a ≥24-h abstention from exercise or other moderate- or vigorous-intensity physical activity. Upon arrival, each participant voided their bladder and changed into minimal, form-fitting clothing and a swim cap. Participants then completed duplicate assessments using each of the four 3D scanning technologies.
Mobile phone applications
Participants were assessed using a mobile phone application requiring the capture of two 2D images, one frontal image and one lateral image (Mobile Fit, v. 3.0.1(45), Size Stream, Cary, NC, USA). For the frontal image, the participant was positioned in an A-pose (i.e., feet spread apart, and arms straightened and raised laterally), while the lateral image required the participant to keep their arms at their sides. For the lateral image, the right side of participants was captured. Scans were performed using an 11-inch iPad Pro, 3rd generation (model number MHQT3LL/A) with iPadOS v. 16.4.1(a) (Apple, Cupertino, CA, USA). For all scans, the iPad was mounted on a tripod, with the tripod arranged on the floor to allow for participant positioning as indicated by the on-screen body outline. Scans were automatically processed through the manufacturer’s online data processing system, with results exported for analysis.
The second mobile phone application (Prism Labs, Los Angeles, CA, USA) required participants to rotate in place ~1.7 m in front of a mobile phone while serial images were captured by the camera. During the rotation, ~150 images were captured, with ~30 used for avatar reconstruction. Scans were performed using an iPhone 13 Pro Max (model number MLKR3LL/A) with iOS v. 16.5 (Apple, Cupertino, CA, USA). For all scans, the iPhone was mounted on a tripod. Each scan was processed using the manufacturer’s procedures, including machine learning for data pre-processing through binary segmentation and obtaining frame-to-frame correspondences. Avatars were produced by fully non-rigid reconstruction, and a parameterized body model was fitted to each avatar to normalize the avatar’s pose to a canonical pose and promote consistent measurement locations [6].
Traditional 3D scanners
Scans were performed using the SS20 scanner with the Size Stream Scanner 6.3 software and processed using Size Stream Studio software v. 5.2.9 (Size Stream, Cary, NC, USA). Prior to scanning, the device’s sensors were calibrated using a hanging panel with checkerboard pattern. During the scan, participants followed manufacturer instructions to stand still, facing forward, while holding handles to promote consistent arm positioning.
Additional scans were performed using the Styku® scanner with software version 4.4.0.673.0.84 (Styku®, Los Angeles, CA, USA). This device requires the participant to stand still in an A-pose on a mechanical turntable, which is rotated in front of a tower containing a Kinect sensor (Microsoft, Redmond, WA, USA). Scans were conducted following manufacturer procedures.
Statistical analysis
Sample size was informed by practical considerations, but the suitability of the achieved sample size was confirmed through estimation of the required sample for a reliability study [8, 9]. Using an expected intraclass correlation coefficient (ICC) of 0.97, from prior research [6]; a significance level of 0.05; a power of 80%; and information about the study design (e.g., duplicate assessments, no attrition), it was estimated that a minimum of 22 individuals were needed to adequately power the present study.
The precision of each 3D scanning technology was evaluated using duplicate assessments. Outcomes of interest included the circumferences of the waist, hips, chest, neck, thighs, arms, calves, and ankles, as well as the volume of the whole body, torso, legs, and arms. Body fat percentage estimated by each device was also evaluated. The absolute and relative technical error of the measurement (TEM) were calculated as follows, where V1 and V2 are the first and second values obtained from repeated measurements for a particular variable, n is the sample size, and the grand mean is the mean of V1 and V2 means:
The absolute and relative TEM are synonymous with the precision error and root-mean-square coefficient of variation, respectively. The absolute TEM is presented in the unit of measurement for each variable, while the relative TEM is presented as a percentage. For body fat percentage, only the absolute TEM was considered since this outcome is already presented in percentage units. The ICC was calculated using model 2,1 [10, 11]. Data analysis was performed using R (v. 4.3.1).
Results
Participants
Forty-six participants (22 F, 24 M) were included in the present study (Table 1). Based on self-report, this sample included 25 individuals who were non-Hispanic Caucasian, 14 who were Hispanic/Latino, 5 who were Asian, and 2 who were Black or African American.
Circumferences
Across all scanners and evaluated circumferences, the absolute TEM ranged from 0.2 to 2.0 cm, the relative TEM ranged from 0.5 to 3.6%, and the ICC ranged from 0.847 to 0.996 (Table 2). For the Mobile Fit application, the absolute TEM, relative TEM, and ICC ranged from 0.2–1.7 cm, 1.0–1.9%, and 0.946–0.987. In comparison, the error values for the Prism Labs mobile application were 0.2–0.8 cm, 0.5–1.3%, and 0.962–0.995 for the same metrics. For the traditional 3D scanners, the range of absolute TEM, relative TEM, and ICC were 0.3–2.0 cm, 0.5–3.1%, and 0.919–0.996 for Styku® and 0.3–1.0 cm, 0.5–3.6%, and 0.847–0.995 for the Size Stream SS20. When averaged across all circumferences, excluding the ankles due to their absence from the Styku® output, the mean absolute TEM, relative TEM, and ICC were as follows: Mobile Fit 0.9 cm, 1.5%, and 0.975; Prism Labs 0.5 cm, 0.9%, and 0.986; Styku® 0.8 cm, 1.5%, and 0.974; and Size Stream SS20 0.6 cm, 1.1%, and 0.985.
Volumes and body fat percentage
All scanners provided total body volume, and three of the four scanners provided segmental volumes. For total body volume estimates across scanners, the absolute TEM ranged from 0.7 to 2.2 L, the relative TEM ranged from 0.9 to 3.0%, and the ICC ranged from 0.978 to 0.998. For segmental volume estimates across all scanners, the absolute TEM ranged from 0.1 to 1.5 L, the relative TEM ranged from 1.2 to 7.2%, and the ICC ranged from 0.936 to 0.998 (Table 3). When averaged across volumes, the mean absolute TEM, relative TEM, and ICC were as follows: Mobile Fit 0.8 L, 4.3%, and 0.963; Styku® 0.3 L, 3.0%, and 0.982; and Size Stream SS20 0.3 L, 2.5%, and 0.980. Across all scanners, the TEM for body fat percentage ranged from 0.4 to 0.9%, and the ICC ranged from 0.982 to 0.996 (Table 3).
Discussion
3D scanning has been established as a suitable technology for rapid, non-invasive anthropometric assessments [1, 2]. The continued development and evolution of mobile phone scanning applications are rapidly increasing the accessibility of 3D scanning technology for health monitoring applications in clinical nutrition settings and for individual consumers. However, the critical evaluation of these emergent mobile scanning options is essential to verify the precision of measurement for key anthropometric sites. In the present study, two distinct mobile scanning applications were examined alongside two well-established non-mobile scanning options. The primary finding of this investigation was that the mobile scanning option involving full rotation of the subject in front of the smartphone camera, followed by non-rigid avatar reconstruction, performed as well as the larger, less portable, and more expensive 3D scanners. In contrast, larger errors were observed for the mobile scanning unit that utilized two 2D images to produce a 3D avatar, although the precision of circumference estimates was nearly identical to one of the traditional, less portable scanners (Styku®), and technical errors were low enough to be acceptable for some applications.
We previously reported the precision of anthropometric measurements from the two traditional 3D scanners used in the present study (Size Stream SS20 and Styku®) in a sample of 179 adults, using the exact same devices as in the present study [7]. Although these data were collected 4 years before the data used in the present investigation, the precision of the Size Stream SS20 scanner was remarkably similar. For example, the TEM (i.e., precision error) for the Size Stream scanner was 0.9 cm and 0.4 cm for waist and hip circumferences, respectively, in the previous investigation as compared to 0.8 cm and 0.5 cm in the present study. Additionally, for total body volume, the previous trial reported a TEM of 0.7 L, nearly identical to the TEM of 0.8 L in the present study, with ICC values of 0.996–0.998 across investigations. Collectively, these data provide support for the stability of this scanning technology over several years and may indicate few substantive changes in data processing for anthropometric variables during this time, despite several software updates. For the Styku® scanner, in contrast, notable improvements in precision were observed for some measurement sites as compared to the previous trial. For example, the TEM for waist circumference in the previous trial was 1.4 cm as compared to 0.7 cm in the present trial, with a corresponding improvement in the ICC from 0.982 to 0.995. In contrast, other measurements demonstrated nearly identical technical error as compared to the previous investigation (e.g., hip circumference with a TEM of 0.5 cm in both trials). As the Styku® hardware was identical in both trials, the most likely explanation for performance improvements is updated data processing procedures. Other research groups have also examined the precision of traditional, non-mobile scanners and generally report high reliability, although variation in the technical aspects of data acquisition and processing vary across technologies and impact the degree of precision [2, 12, 13].
With the strong precision of traditional, non-mobile 3D scanners established for key anthropometric sites, a key question is whether similar performance can be replicated using more accessible mobile technology. Select previous trials have examined relevant mobile applications—such as Size Stream MeThreeSixty, Amazon Halo, myBVI, and made/Spren® – although most of these have focused solely on body composition metrics —particularly body fat percentage, fat mass, and fat-free mass—or have reported validity but not reliability [3,4,5, 14,15,16]. However, Smith et al. [5], performed an investigation that is relevant to the present work. They evaluated adults using both the Size Stream SS20 (n = 56) and Size Stream MeThreeSixty (n = 54), a mobile application that is similar to the Size Stream Mobile Fit application used in the present study and that utilizes the same analysis pipeline. The reported coefficient of variation (CV) values were similar for MeThreeSixty vs. SS20 for most sites: 0.9 vs. 0.9% for waist circumference, 1.1 vs. 0.8% for hip circumference, 1.6 vs. 2.2% for right thigh circumference, and 1.8 vs. 2.6% for right arm circumference. By comparison, the relative TEM (i.e., root-mean-square CV) values in the present investigation for the Size Stream mobile application vs. SS20 were: 1.4 vs. 0.9% for waist circumference, 1.6 vs. 0.5% for hip circumference, 1.9 vs. 0.7% for right thigh circumference, and 1.6 vs. 1.8% for right arm circumference. These findings indicate that, generally, larger technical errors can be expected with this mobile application as compared to the larger booth scanner. However, a noteworthy feature of the investigation of Smith et al. [5] is that 3D object files from both the mobile application and traditional scanner were processed using the same universal software [17] rather than the standard, manufacturer-provided method. This may have contributed to the relative similarity of CV values for several sites and likely indicates that any remaining discrepancies are due solely to technical factors at the time of data acquisition. In contrast, we utilized the manufacturer-provided anthropometric values for the SS20 and the mobile application to provide generalizable results to individuals or groups who use the mobile application without modification.
The primary focus of the present work was the precision of anthropometric variables rather than body composition metrics, although the precision of body fat percentage estimates was examined, with generally strong results (TEM 0.4–0.9% and ICC 0.982–0.996 across scanners). While body composition variables can be predicted from anthropometric variables, establishing the precision of the input variables themselves is essential. Body composition prediction equations from 3D scanning techniques will typically include multiple input variables within a regression equation, meaning that examining the precision of the body composition metrics alone does not allow for meaningful information to be gathered about specific features of the humanoid avatars from which the circumference or volume estimates are obtained [7, 18]. Nonetheless, we [3, 4, 18,19,20,21,22] and others [2, 12, 23,24,25] have previously reported the precision and validity of several 3D scanning technologies for body composition assessment. Notable challenges in this research area include the potential for frequent updating of body composition prediction equations by manufacturers, rendering previous research largely inapplicable, and the proprietary nature of many body composition estimation methods from 3D scanners, limiting the ability to examine, modify, and implement optimal estimation equations. Ongoing research using device-agnostic shape models [26], pose-independent methods [27], inter-device translation procedures [28], and automated analysis pipelines [29] may hold the potential to alleviate some of these concerns, although widespread implementation has not yet been achieved and could be unattainable due to the desire for novelty and enhanced marketability of commercial 3D scanning solutions.
The primary limitations of the present study include the focus on a comparison of digital anthropometric techniques without inclusion of manual anthropometry, which prevented a consideration of the accuracy of the studied technologies; and the participants, primarily classified as normal weight or overweight based on BMI, which may indicate the potential for less generalizability of the present results to other patient populations, such as those with obesity.
The present work contributes to the growing literature describing the continual development of 3D scanning for human health monitoring. Beyond the technical examination of 3D scanning for digital anthropometry and body composition estimation, a variety of applications have been examined. These include predicting injury risk [30], evaluating body composition standards in military populations [31, 32], relating anthropometric characteristics to exercise performance [33, 34], and establishing novel body shape features associated with cardiometabolic disease risk [35], among others [36]. Optimal scanning techniques and procedures for these and other uses should be the focus of continued investigation. Regarding technical aspects of scanning, the present study supports the utility of a mobile phone application collecting serial images during complete subject rotation, followed by fully non-rigid reconstruction and parameterized body model fitting. The excellent precision of this method may contribute to increased accessibility and utility of 3D scanning for diverse applications in health and disease.
Data availability
Data may be available from the corresponding author upon reasonable request and pending relevant approvals.
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Funding
Funding for this study was provided by Greyscale Holdings, Inc. (DBA Prism Labs; Award #: A22-0305-001). Based on the research contract, the sponsor was able to review the present manuscript to ensure the technology was accurately described. Besides confirming the technical description of the scanning technology, the manufacturer played no role in the present manuscript, including the statistical analysis, writing, and decision to publish. A previous in-kind donation of the SS20 scanner (Size Stream, Cary, NC, USA) used in the present study allowed the research to proceed. This in-kind donation was unrelated to the present study, and this manufacturer had no role in any aspect of the present study.
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GMT designed the work; CR, MRS, ET, SJW, CL, AB, BD, and JR collected the data; GMT drafted the initial manuscript; all authors reviewed the manuscript for intellectual content, approved the manuscript, and agree to be accountable for the work.
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Competing interests
GMT has received in-kind support for his research laboratory, in the form of equipment loan or donation, from manufacturers of body composition assessment devices, including Size Stream LLC; Naked Labs, Inc.; Prism Labs, Inc.; RJL Systems; MuscleSound; and Biospace, Inc. None of these entities played a role in the present study, besides the financial support from Prism Labs described in the funding section.
Ethical approval
This study was approved by the Texas Tech University Institutional Review Board (IRB #2022-610), and all participants provided written informed consent prior to participation.
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Tinsley, G.M., Rodriguez, C., Siedler, M.R. et al. Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners. Eur J Clin Nutr 78, 509–514 (2024). https://doi.org/10.1038/s41430-024-01424-w
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DOI: https://doi.org/10.1038/s41430-024-01424-w
