Accuracy and reliability of a low-cost, handheld 3D imaging system for child anthropometry
Accuracy and reliability of a low-cost, handheld 3D imaging system for child anthropometry
Joel ConkleID 0 1
Parminder S. Suchdev 0 1
Eugene Alexander 1
Rafael Flores-Ayala 0 1
Usha Ramakrishnan 0 1
Reynaldo Martorell 0 1
0 Nutrition and Health Sciences Program, Laney Graduate School, Emory University , Atlanta, GA , United States of America, 2 Hubert Department of Global Health, Rollins School of Public Health, Emory University , Atlanta, GA , United States of America, 3 Division of Nutrition, Physical Activity and Obesity, National Center for Chronic Disease Prevention and Health Promotion, U.S. Centers for Disease Control and Prevention , Atlanta, GA , United States of America, 4 Department of Pediatrics, School of Medicine, Emory University , Atlanta, GA , United States of America, 5 Body Surface Translations, Inc. (BST) , Athens, GA , United States of America
1 Editor: Frank Wieringa, Institut de recherche pour le developpement , FRANCE
Funding: The Bill and Melinda Gates Foundation
(OPP1132308 to RM) funded the study. The funder
provided support in the form of salaries for authors
PS, EA, UR and RM. The specific roles of these
authors are articulated in the ‘author contributions’
section. The funder’s project officer contributed to
study design and interpretation of the data, and had
The usefulness of anthropometry to define childhood malnutrition is undermined by poor
measurement quality, which led to calls for new measurement approaches. We evaluated
the ability of a 3D imaging system to correctly measure child stature (length or height), head
circumference and arm circumference. In 2016–7 we recruited and measured children at 20
facilities in and around metro Atlanta, Georgia, USA; including at daycare, higher education,
religious, and medical facilities. We selected recruitment sites to reflect a generally
representative population of Atlanta and to oversample newborns and children under two years of
age. Using convenience sampling, a total of 474 children 0–5 years of age who were
apparently healthy and who were present at the time of data collection were included in the
analysis. Two anthropometrists each took repeated manual measures and repeated 3D scans of
each child. We evaluated the reliability and accuracy of 3D scan-derived measurements
against manual measurements. The mean child age was 26 months, and 48% of children
were female. Based on reported race and ethnicity, the sample was 42% Black, 28% White,
8% Asian, 21% multiple races, other or race not reported; and 16% Hispanic. Measurement
reliability of repeated 3D scans was within 1 mm of manual measurement reliability for
stature, head circumference and arm circumference. We found systematic bias when analyzing
accuracy—on average 3D imaging overestimated stature and head circumference by 6 mm
and 3 mm respectively, and underestimated arm circumference by 2 mm. The 3D imaging
system used in this study is reliable, low-cost, portable, and can handle movement; making
it ideal for use in routine nutritional assessment. However, additional research, particularly
on accuracy, and further development of the scanning and processing software is needed
before making policy and clinical practice recommendations on the routine use of 3D
imaging for child anthropometry.
no role in collection, management or analysis of
data, and no role in preparation, review, or approval
of the manuscript and the decision to submit the
manuscript for publication.
Competing interests: Dr. Eugene Alexander is
employed by BST, Inc. and has a patent pending
related to the study subject matter: Determining
Anthropometric Measurements of a Non-Stationary
Subject. All other authors do not have affiliations
with or financial involvement with any organization
or entity with a financial interest in the subject
matter or materials discussed in the manuscript.
We were able to adhere to PLOS ONE policy on
sharing data, but could not share the data
acquisition software code due to commercial
interests of BST, Inc.
Body measurement, or anthropometry, can be compared to a reference population to define
nutritional status and to monitor child growth. Length or height, weight, and head
circumference (HC) are common anthropometric measures for infants and children under 5 years of
age. Anthropometry is used clinically to diagnose malnutrition [
], to identify underlying
], to assess risk for future disease [
], and for clinical research . At the
population level, public health practitioners include anthropometry in research and surveys to
identify causes and effects of abnormal nutritional status, to monitor trends through surveillance,
and to target and evaluate interventions related to nutrition [
]. Anthropometry is also used to
evaluate agricultural initiatives, and the global development community uses population-level
anthropometry as an indicator of national economic development. Height-for-age is accepted
as a more comprehensive indicator of poverty than income [
], and there is recognition that
nutrition is essential for human capital development [
]. There is a target to improve stunting
in the Sustainable Development Goals [
], and anthropometric indicators are used for
allocation of Official Development Assistance [
Given that child growth has broad effects on health, nutrition, and development, it is
important that anthropometric measurements are of high quality. Studies in primary care
facilities of developed countries found that measurement error led to inaccurate and unreliable
circumference measurement for adults [
] and unreliable length and circumference
measurements for children [
]. There is also evidence that a lack of standardization and
maintenance of anthropometric equipment in health facilities leads to misclassification of child
weight status [
]. Three separate evaluations covering hundreds of large-scale, established
surveys in developing countries found that on average more than 3% of weight or height
measurements were biologically implausible [
]. According to a World Health Organization
(WHO) Expert Committee, when more than one percent of measurements are considered
biologically implausible, a survey is likely to be of poor quality [
The usefulness of anthropometry is undermined by poor measurement quality, which has
led to calls for the use of technology to improve quality of child anthropometry [
study evaluated the ability of a portable, three-dimensional (3D) imaging system to accurately
and reliably measure child stature (length or height), head circumference, and mid-upper arm
Subjects and methods
Study design and participants
We designed the Body Imaging for Nutritional Assessment Study (BINA) to evaluate the
accuracy and reliability of a 3D imaging system in comparison to manual measurements for child
anthropometry. We chose to compare to manual measurement because growth standards are
based on manual measurement, and when manual measurement is done well the levels of
precision and accuracy are sufficient for nutritional assessment [
]. The study was approved
by the Emory Institutional Review Board (IRB), and included two phases. In the first phase we
calibrated software to process 3D scans into measurements by scanning and measuring 36
children. In the second phase, the topic of this paper, we tested 3D imaging on a new sample
of children. Children under five years of age who were apparently healthy and whose primary
caregiver gave informed, written consent were eligible for the study. Caretakers received a
nominal gift card ($15) for each child participating in the study. We recruited and measured
children at 20 facilities in and around metro Atlanta, GA, USA; including at daycare, higher
education, religious, and medical facilities. We selected recruitment sites to reflect a generally
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representative population of Atlanta children and included a maternity ward to sample
newborns. Daycare centers received gift cards for participating as a study site. We formed a
convenience sample by recruiting children on-site, via email, and through facility administrative
staff; recruitment was ongoing throughout data collection, which lasted from September 2016
to February 2017. The intended sample size for the study was set at 500, with a target sample
size of 100 for each of the following age groups: 0–5 months, 6–11 months, 12–17 months, 18–
23 months and 24–59 months. We did not carry out a-priori power calculations. We set
sample size targets by age group to oversample children under two years of age, an age group that
is particularly difficult to measure manually, and to allow for an assessment of variability of
measurement error across the entire span of 0–4 years.
Five trained anthropometrists with post-secondary education performed all manual
measurements and 3D scans. Anthropometrists received training over a three week period in August 2016
from expert anthropometrists at Emory University and passed a standardization test for manual
anthropometry. Manual measurements followed the protocol used to develop the 2006 WHO
Child Growth Standards (CGS) [
]; detailed methods for manual anthropometry in BINA are
published elsewhere [
]. Staff from Body Surface Translations Inc. (BST) trained
anthropometrists to take 3D scans in one day, and anthropometrists informally used 3D scanners throughout
the three week training period to familiarize themselves with the technology. During the
standardization test anthropometrists scanned children following study protocol, and after visual
assessment we determined scans were of sufficient quality to proceed with the study.
Each anthropometrist carried a 3D scanning device: a tablet with attached Structure Sensor
3D scanner (Occipital, San Francisco, CA, USA) and custom software from BST, AutoAnthro,
for scanning and data entry of demographic information and manual measurements.
AutoAnthro will be commercially available from BST. The 3D scanner we used was off-the-shelf,
commercial hardware; and it was a fraction of the price of other scanners (USD $379). The scanner
uses a Class 1 laser, which does not cause eye injury, and is the same type of laser used in video
game technology. We collected scans (Fig 1) and then manual measurements consecutively at
the same time of the day, usually in the morning. Each individual 3D imaging session
comprised six scans, with three scans of the front of the child and three of the back. The software
was designed for automated processing of six scans into body measurements. Consistent with
manual anthropometry procedures, we scanned children two years of age and over standing
up, and instructed younger children to lie down (S1–S3 Figs). Each child was scanned and
measured twice by two different people, resulting in four sessions of scans and four sessions of
manual measurements per child. Multiple measurements allowed analysis of both inter- and
In this study, one anthropometrist could be triggered to take a third measurement for manual
measurements based on maximum allowable difference [
], but not for scans. To
determine a best-estimate from manual measurements, we excluded the outlying measurement in
the case of a triggered, third measurement; and took the mean from the four remaining
measurements (two from each anthropometrist). In this paper we refer to the average of four
measurements as “best-estimate” and “all scan” for manual and scan-derived measurements
respectively, and consider the former the reference standard. For analyzing reliability we
limited our analysis to the first two manual measurements, ignoring any triggered third
measurement; which provided a like-for-like comparison with scan-derived measurements. In the text
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Fig 1. 3D scan. Scan of child over two years of age with anthropometrist kneeling in the background. Scan as it
appears to anthropometrist during data collection and before processing.
we refer to the mean of two measurements as “repeated-manual” and “repeated-scan,” and to
measurements derived from one measurement as “single-manual” and “single-scan”.
We used SPSS 20 (IBM Corp., Armonk, NY, USA) to test statistical significance of average
bias with a two-sided, paired t-test with alpha of 0.05. Average bias is a metric of systematic
bias. We also carried out Sign Tests—another metric of systematic bias that tests whether there
were the same number of positive and negative differences using a Binomial Test.
Using StataSE 13’s (StataCorp, College Station, TX, USA) baplot module we created
BlandAltman (BA) Plots [
] to assess if accuracy remained constant across different child body
sizes and to look at random bias. For the y-axis of the BA Plot we subtracted the best-estimate
from the single-scan value, and for the x-axis we used the mean of single-scan and
best-estimate. We used Pitman’s Test of Difference in Variance [
] to test the correlation between
accuracy and the size of the child, and we calculated and plotted Limits of Agreement, which is
the 95% precision interval for individual differences and is a metric of random bias. We
disaggregated analysis based on age groups corresponding to a division in the estimation software,
which used two anatomic models—one for children less than one month of age and another
for children 1–59 months. If accuracy was not consistent across different sizes, indicated by a
statistically significant Pitman’s Test, we carried out the additional step of regressing the
difference on the independent, second single-scan as suggested by Bartlett and Frost to rule out
difference in SD as the cause of a statistically significant Pitman’s Test [
]. We used Technical
Error of Measurement (TEM) and the Coefficient of Reliability (R) as described by Ulijaszek
] to measure reliability, which are the same measurements of reliability used to develop the
WHO Child Growth Standards [
]. TEM represents one standard deviation and a 95%
precision margin can be calculated by multiplying TEM by two. R measures the strength of
]. We used SPSS 20 to calculate the Intraclass Correlation Coefficient based on
absolute agreement, which is another measurement of correlation that is familiar to a wider
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audience. Additional details of test and analysis methods are included in Methods of the
supplementary online content.
Participation and sample characteristics
S4 Fig shows the flow of participants in the study. We received informed consent for 555
children, of which 26 children were either not present or had aged out by the day of data
collection. Of the remaining 529, we excluded 55 due to: refusal to be measured (n = 18), incomplete
measurements (n = 8), health status (n = 5), loss of data due to technical errors during upload
(upload software since corrected) (n = 10), and use of child in calibration of the 3D imaging
system (n = 14); resulting in a final sample size of 474.
Table 1 presents sample characteristics. There was a low prevalence of wasting, stunting,
underweight and overweight. The mean child age was 26 months and 48% of children were
female. Based on reported race and ethnicity, the sample was 42% Black, 28% White, 8%
Asian, 21% Multiple Races, Other or Race Not Reported; and 16% Hispanic. Children under
two years of age and newborns were overrepresented, and nearly all of the newborns were less
than four days old.
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Fig 2. Bland-Altman plot. Length/height best-estimate manual measurement subtracted from single-scan
measurement (y-axis) plotted against average based on both measurement types (x-axis) among children 0–59 months
When using all-scan, the average bias of scan-derived measurements in cm was +0.6 (95%
confidence interval (CI): 0.56, 0.62) for stature, +0.3 (CI: 0.30, 0.34) for HC, and -0.2 (CI: -0.21,
-0.17) for MUAC (S1 Table). Differences were consistent and statistically significant at p <
.0001 whether measurements were derived from single-scan, repeated-scan, or all-scan.
However, the number of scan sessions did have an effect on the spread of differences and repeated
measurements reduced variance as expected. For stature 97% of all-scan measurements were
higher than manual measurements, or positive, and the 95% limit of agreement (LoA) showed
that 95% of individual differences were within -0.1 to 1.2 cm; single-scan measurements were
78% positive with a LoA of -0.7 to 1.9 cm.
We visually inspected the accuracy of scan-derived measurements using Bland-Altman
Plots (Fig 2). Compared to children 1–59 months of age 3D imaging was less accurate for
newborns for all measures (Table 2). After disaggregating by age group (corresponding to the two
anatomic models) Pitman’s Test was not significant for stature and HC, indicating no
differential accuracy by size within the two age groups. For MUAC, Pitman’s Test was statistically
significant (p < .01), suggesting differential accuracy by size within both age groups. Subsequent
linear regression confirmed differential accuracy by size for MUAC— 3D imaging was less
accurate for children with smaller MUAC. After separating children 1–59 months of age into
quintiles based on MUAC, average bias of scan-derived measurements in cm was -0.31
(MUAC 9.6–15.1 cm), -0.18 (MUAC 15.1–16.0 cm), -0.15 (MUAC 16.0–16.7 cm), -0.02
(MUAC 16.7–17.6 cm), and -0.05 (MUAC 17.6–25.3 cm).
Among children 1–59 months of age there were no statistically significant or meaningful
differences in accuracy by race or hairstyle (S2 Table). The largest difference was a 0.04 cm
difference in average bias for head circumference between Black and White children.
The intra-observer TEM for stature among children of all ages was 0.62 cm for scan-derived
measurements, indicating that for a single observer the second scan-derived stature was within
±0.62 cm of the first scan-derived stature for two out of three children, and that for 95% of
children the difference was within ±1.2 cm (Fig 3A and S3 Table). Manual measurement
intraobserver TEM for stature among children of all ages was within ±0.72 cm for 95% of children.
Intra-observer TEM from scan-derived measurements was higher than that from manual
measurements for all measures and across all age groups, but unlike manual measurements, there
were no meaningful differences by age group for scan-derived measurements (Fig 3A).
For all children under 5 years of age inter-observer TEM from repeated scans was within
0.1 cm of TEM from repeated manual measurements for all measures (Fig 3B). We also
examined inter-observer TEM based on single measurements. Single-scan inter-observer TEM was
higher than single-manual inter-observer TEM (Fig 4).
When using single measurements inter-observer TEM was higher than intra-observer TEM
for manual measurement, but not for scans (Fig 4), indicating that scanning produced similar
results for anyone who repeated the scan. Total TEM combines the intra- and inter-observer
TEM from S3 Table into a single metric. For manual measurements Total TEM was 0.51 cm,
0.33 cm, and 0.31 cm for stature, HC and MUAC respectively; compared to 0.77 cm, 0.51 cm,
and 0.43 cm for scan-derived measurements.
The Coefficient of Reliability based on Total TEM was 1.00, 1.00, and 0.99 for stature, HC
and MUAC respectively from manual measurements; and 1.00, 0.99, and 0.98 for scan-derived
measurements. The high R indicates excellent agreement for repeated measurements.
Intraclass correlation coefficients, another measure of agreement, were also close to 1.00 for
intraand inter-observer repeated measurements (S3 Table), confirming the excellent correlation
between repeated measurements for both manual and scan-derived measurements.
We previously demonstrated that BINA collected gold-standard, manual anthropometry
based on analysis of biological plausibility, reliability, and z-score standard deviations [
this paper we compared measurements derived from 3D imaging to these gold-standard,
manual measurements. For biological plausibility, 3D imaging and manual measurement were
exactly the same, with both methods producing plausible measurements >99% of the time;
this finding indicates acceptable quality based on WHO expert committee criteria for
biological plausibility [
]. We also found that repeated-scan 3D imaging produced measurement
reliability that was within 1 mm of manual measurement reliability for stature, HC and
MUAC; this level of reliability puts 3D imaging on par with manual measurements collected
in the Multicenter Growth Reference Study (MGRS) used to develop the 2006 WHO CGS
]. Considering only biological plausibility and reliability, 3D imaging performed as well as
gold-standard manual measurements for child anthropometry. However, 3D imaging
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Fig 3. Intra- and inter-observer technical error of measurement (TEM). Scan-derived (light bars) versus manual
measurement (dark bars) intra-observer TEM (A) and inter-observer TEM (B) for stature, head circumference and
arm circumference disaggregated by age group. Inter-observer TEM based on average of repeated measures and
intraobserver TEM based on single measures.
systematically underestimated or overestimated child size when compared to our best-estimate
of size from manual measurement.
Before reaching any conclusion on the readiness of 3D imaging for child anthropometry,
we would need to determine if the systematic inaccuracy found in this study is population
specific. If the same under- and overestimation was found in a different sample with different
anthropometrists, we could then identify and fix the cause of the bias in the model fit or simply
build adjustments into the software. Knowing the cause of bias could facilitate adjustments.
We hypothesized that inaccuracies in our study were the result of difficulty in manual
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Fig 4. Single measure intra- and inter-observer technical error of measurement (TEM). Inter-observer TEM (dark
bars) versus intra-observer TEM (light bars) for scan-derived (right) and manual measurements (left). Both inter- and
intra-observer TEM based on single measures.
measurement for MUAC, and not accounting for exact protocol of manual measurement in
the design of scan processing software for head circumference and stature.
Research similar to BINA should be carried out, ideally in developed and low and middle
income countries, to help answer questions on systematic inaccuracy and also to address some
of the other limitations of our study. The 3D imaging system may perform differently under
the harsher conditions of a household survey or community-based screening. Increased
handling during transport, lack of access to electricity, lighting, dust, space constraints and other
environmental factors could all affect the functionality of the 3D scanner.
Additional limitations to our study stem from sampling design and automated processing.
The sample size was not specified during study design based on power calculations, and due to
limited sample size and the choice of population we did not fully explore differences in
prevalence estimates and did not analyze sensitivity and specificity for clinically significant
indicators, such as obesity, wasting and severe stunting. In addition, findings from our non-random
sample cannot be generalized to any specific age group, and the processing of 3D scans was
not fully automated as originally planned. Anthropometrists took more scans than needed and
manually selected the best quality scans. Also, the orientation (front/back) of each scan was
manually coded. Further software development is needed to achieve full automation, which
could improve repeatability.
Our primary interest in researching 3D imaging for child anthropometry was to improve
the quality of anthropometric data, and while not conclusive, our findings suggest that 3D
imaging could play a role in quality improvement. Compared to manual measurement, we
spent substantially less time on training and supervision for 3D scanning, and achieved similar
reliability. Also, our findings on scan-derived measurement reliability suggest that scanning
was not affected by child age, which can be viewed as a proxy for cooperation, or
anthropometrist’s technique. Both cooperation and measuring technique are known to negatively affect
anthropometric data quality. Qualitative research on BINA anthropometrists’ experiences
using 3D scanners is currently underway and this may help to provide additional evidence on
the potential of 3D imaging to improve anthropometric data quality. However, our study was
not designed to determine if 3D imaging led to better quality, and anthropometrists in BINA;
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who were well educated, highly motivated, and well-trained; achieved high quality
anthropometric data with both 3D imaging and manual measurement. Conclusive evidence on quality
improvement will not be available until 3D imaging is tested in a setting of poor quality
Results from our analysis of z-scores and classification (S4 and S5 Tables); along with an
expanded discussion on reliability, bias hypotheses and study limitations; is included in the
supplementary online content.
3D imaging is not new for anthropometry [
], but the 3D scanner used in our study was
inexpensive, brought unique functionality, and shows promise as a substitute for traditional
anthropometry measurements. The scanning device is small, lightweight, and the software
developed by BST only requires a series of snapshots, which allows some subject movement.
The 3D imaging system used in our study, AutoAnthro, could be an ideal replacement for
bulky height boards used in surveys, and to our knowledge it is the first portable 3D system
specifically designed for whole body scanning of infants and young children. In conclusion,
our findings indicate that AutoAnthro can produce reliable child anthropometry, but further
research and development is needed before 3D imaging can be recommended as a solution to
improving the quality of anthropometric data.
S1 Fig. 3D scan arm poses. Poses for children two years of age and over.
S2 Fig. 3D scan measurement points. Points (in black) selected on base model to measure
head and arm circumference.
S3 Fig. The basic fitting process. Scan data is in green, articulated model surface in red,
“bones” and “joints” in blue. On the left, the initial size and pose of model relative to data. On
the right, the model has been automatically sized and posed to fit the scan data.
S4 Fig. Flow of study participants.
S1 Table. Accuracy of scan-derived measurements. Comparison to best-estimate, manual
measurements among all children under five years of age.
S2 Table. Accuracy by race and hairstyle. Considering best-estimate manual measurements
and scan-derived measurements from all sessions among children 1 to 59.9 months of age.
S3 Table. Intra-observer reliability and inter-observer reliability. Based on repeated manual
measurements and repeated scan sessions by age group.
S4 Table. Z-score mean, standard deviation (SD) and prevalence by selected
z-score-forage cutoffs. Among children 1–59.9 months of age.
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S5 Table. Sensitivity and specificity of adjusted, scan-derived measures. Comparison to
best-estimate manual measures among children 1–59.9 months of age.
S1 Text. Supplementary methods, results and discussion.
Disclaimer: The findings and conclusions in this article are those of the authors and do not
necessarily represent the official position of the Centers for Disease Control and Prevention.
Jennifer Breiman, RN; Carma Graham, MS; Ashton Hughes, BA; Kate Keirsey, MS; and
Terrell Williams, MPH were research specialists for the study who helped to develop the study
manual, collected high quality anthropometry and 3D scans, and provided recommendations
for further improvement of the 3D imaging system. JB and TW also contributed to literature
review on the use of manual measurements and 3D imaging for child anthropometry.
Guidance from the 2015 Standards for Reporting Diagnostic Accuracy Studies (STARD) helped to
ensure completeness and transparency in reporting our study. Kenneth H. Brown, MD, The
Bill and Melinda Gates Foundation, championed development of the 3D imaging system and
assisted in the study design and interpretation.
Data curation: Joel Conkle.
Formal analysis: Joel Conkle.
Conceptualization: Joel Conkle, Parminder S. Suchdev, Eugene Alexander, Usha
Ramakrishnan, Reynaldo Martorell.
Funding acquisition: Parminder S. Suchdev, Eugene Alexander, Usha Ramakrishnan,
Investigation: Joel Conkle, Parminder S. Suchdev, Eugene Alexander, Usha Ramakrishnan,
Methodology: Joel Conkle, Parminder S. Suchdev, Eugene Alexander, Rafael Flores-Ayala,
Usha Ramakrishnan, Reynaldo Martorell.
Project administration: Usha Ramakrishnan, Reynaldo Martorell.
Supervision: Joel Conkle, Parminder S. Suchdev, Eugene Alexander, Reynaldo Martorell.
Writing – original draft: Joel Conkle.
Writing – review & editing: Joel Conkle, Parminder S. Suchdev, Eugene Alexander, Rafael
Flores-Ayala, Usha Ramakrishnan, Reynaldo Martorell.
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