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European Journal of Clinical Nutrition
https://doi.org/10.1038/s41430-017-0046-1
BRIEF COMMUNICATION
Validation of a three-dimensional body scanner for body
composition measures
Michelle M. Harbin1 ● Alexander Kasak1 ● Joseph D. Ostrem2 ● Donald R. Dengel1
Received: 12 March 2017 / Revised: 5 October 2017 / Accepted: 20 November 2017
© Macmillan Publishers Limited, part of Springer Nature 2017
Abstract
The accuracy of an infrared three-dimensional (3D) body scanner in determining body composition was compared against
hydrostatic weighing (HW), bioelectrical impedance analysis (BIA), and anthropometry. A total of 265 adults (119 males;
age= 22.1± 2.5 years; body mass index= 24.5± 3.9 kg/m2) had their body fat percent (BF%) estimated from 3D scanning,
HW, BIA, skinfolds, and girths. A repeated measures analysis of variance (ANOVA) indicated significant differences among
methods (p o 0.001). Multivariate ANOVA indicated a significant main effect of sex and method (p o 0.001), with a non-
significant interaction (p= 0.101). Bonferroni post-hoc comparisons identified that BF% from 3D scanning (18.1 ± 7.8%)
was significantly less than HW (22.8 ± 8.5%, p o 0.001), BIA (20.1 ± 9.1%, p o 0.001), skinfolds (19.7± 9.7%, p o
0.001), and girths (21.2 ± 10.4%, p o 0.001). The 3D scanner decreased in precision with increasing adiposity, potentially
resulting from inconsistences in the 3D scanner’s analysis algorithm. A correction factor within the algorithm is required
before infrared 3D scanning can be considered valid in measuring BF%.
Introduction
Two-compartment models, which separate the human body
into fat mass and fat-free mass, are widely used methods of
determining body composition and include techniques such
as hydrostatic weighing (HW) and air displacement ple-
thysmography (ADP) [1, 2]. Multi-compartment body
composition models (e.g., dual X-ray absorptiometry
[DXA]) offer greater accuracy than traditional two-
component methodology. However, the high cost of using
such methods and amount of radiation exposure limit their
use in certain settings and populations [1, 2].
Current advances in scanning technology allow for the
ability to capture a three-dimensional (3D) image of the
human body and digitally extract numerous anthropometric
measurements [1–4]. To date, few studies have investigated
the accuracy of infrared 3D technology in estimating body
composition. As such, the aim of this study was to compare
and validate the accuracy of an infrared 3D body scanner in
determining body composition against hydrostatic weighing
(HW), bioelectrical impedance analysis (BIA), and anthro-
pometry (e.g., skinfold thickness and girths).
Methods
Subjects
Healthy college students (N= 265; males= 119) at the
University of Minnesota, Twin Cities were recruited from
December 2015 to December 2016. The study protocol was
reviewed and approved by the University of Minnesota,
Twin Cities Institutional Review Board (IRB). All proce-
dures were followed in accordance with the IRB and
HIPAA guidelines. Exclusion criteria included claus-
trophobia, hypersensitivity to chlorine, and medical con-
traindications that limit water submersion (pregnancy,
orthopedic casting, open lacerations).
* Michelle M. Harbin
harb0085@umn.edu
1 Laboratory of Integrative Human Physiology, School of
Kinesiology, University of Minnesota, Minneapolis, MN 55455,
USA
2 Kinesiology and Health Sciences, College of Education and
Science, Concordia University - St. Paul, St. Paul, MN 55104,
USA
12
34
56
78
90
Procedures
Testing was performed at the Laboratory of Integrative
Human Physiology on the University of Minnesota, Twin
Cities campus. Subjects wore tight-fit clothing (compres-
sion shorts, spandex).
BIA and anthropometry measures
Body mass and BF% were obtained via a BIA scale (Tanita
Corporation, Tokyo, Japan). Height was obtained from a
stadiometer (Seca GmbH & Co. KG, Hamburg, Germany).
Body mass index (BMI) was calculated as body mass in
kilograms (kg) divided by height in squared meters (m2).
Lange C-120 skinfold calipers (Beta Technology Inc.,
Cambridge, Maryland) measured skinfold thickness. The
Jackson and Pollock equation estimated body density from
the sum of three skinfold sites [5]. A 60-inch Gulick mea-
suring tape assessed girth (Creative Health Products, Ann
Arbor, MI). The Navy circumference-based equation esti-
mated body density from the average of three girths taken at
each gender specific site [6].
Infrared 3D body scanner measures
The 3D body scanner (MYBODEE™; Styku, Los Angeles,
CA) estimated BF% via automated girths and an undi-
sclosed linear regression algorithm.
The scanner consisted of a Microsoft Kinect V2 infrared
depth sensor (Microsoft Corporation, Redmond, WA)
positioned 157 centimeters (cm) away from a rotating cir-
cular platform. Subjects stood erect on the circular platform
with their feet flat and directed forward. Subjects were
required to remain motionless with arms abducted at a 45-
degree angle away from the torso and aligned within the
coronal plane of their body. Once scanning initiated, the
circular platform rotated at a constant speed for approxi-
mately 30 seconds while the infrared depth sensor simulta-
neously relayed information to a compatible computer.
Hydrostatic weighing measures
Hydrostatic weight was measured via a wireless underwater
weighing system (Exertech®, Dresbach, MN). An estimate
of residual volume (RV) was made based on the subject’s
height, age, and sex [7]. The average of three hydrostatic
weights was used to calculate body density and to predict
BF% using the estimated RV and the Siri equation [8].
Statistical analysis
IBM SPSS Statistics 23 (IBM Corp. Armonk, NY) was
used for statistical analysis. Descriptive characteristicsTa
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MM Harbin et al.
between sexes were compared by a one-way analysis of
variance (ANOVA). Bland-Altman plots evaluated the level
of agreement between the 3D body scanner against other
body composition methods. A repeated measures ANOVA
evaluated mean differences in estimating BF% according to
the different body composition methods with Bonferroni
post-hoc analysis, and a multivariate ANOVA tested for an
interaction.
Results
The subject population was predominately Caucasian (87%)
and consisted of 119 males and 147 females (males vs.
females: age= 22.4± 2.7 vs. 21.8± 2.4 years, p= 0.071;
body mass= 81.4± 12.8 vs. 66.6± 11.8 kg, p o 0.001;
height= 179.3± 7.3 vs. 165.5± 11.1 cm, p o 0.001; BMI
= 25.2± 3.3 vs. 23.9± 4.2 kg/m2, p= 0.007).
There were significant differences among the body
composition methods (p o 0.001). Bonferroni post-hoc
analysis revealed that BF% estimated by 3D body scanning
was significantly less than all the other techniques (Table 1).
The multivariate ANOVA showed a significant effect
between each body composition method (p o 0.001). As
expected, males had significantly lower BF% compared to
females on all body composition methods (p o 0.001),
though the interaction between body composition method
and sex was not (p= 0.101). Body fat percentages for both
males and females from the 3D body scanner were sig-
nificantly less than HW (p o 0.001), BIA (p o 0.001),
skinfolds (p o 0.001), and girth measures (p o 0.001)
(Table 1). Bland-Altman plots exhibited proportional bias in
3D scanning and reduced precision among subjects with
increased adiposity (Fig. 1).
Discussion
Three-dimensional body scanners using infrared technology
underestimated BF% among healthy, young adults when
compared to HW, BIA, and anthropometry. The non-
significant interaction from multivariate analysis revealed
that the underestimation in BF% from the 3D body scanner
was consistent among both males and females. Similarly,
Ryder and Ball [9] observed that a fan-beam 3D body
scanner underestimated BF% across all BMI categories
when compared to both DXA and ADP.
In addition to the 3D body scanner underestimating BF
%, proportional bias occurred. Proportional bias occurs
when two methods have unequal agreement through the
range of measurements. The 3D scanner had decreased
accuracy among subjects with increased adiposity, which is
largely attributable to inconsistencies with landmark and
partition positioning in the 3D surface scan analysis algo-
rithms [10]. Software updates in infrared technology and a
correction factor are warranted to resolve biases [9, 10].
A possible limitation includes inconsistences of manu-
ally measuring skinfold thickness and girths between the
subjects. Another limitation was that HW with estimated
RV was used instead of DXA or other multi-compartment
models. However, 3D body scanning underestimated BF
compared to HW, BIA and anthropometry. In conclusion,
3D body scanners have the potential application for mon-
itoring body composition. Advancements in technology are
Fig. 1 Bland-Altman plots for
differences in body fat percent
comparing the 3D body scanner
to hydrostatic weighing a, bioe-
lectrical impedance analysis b,
skinfold thickness c, and girth
measures d
Validation of Infrared Three-Dimensional Body Scanning for Body Composition
required before infrared 3D body scanners can be desig-
nated as an accurate method for assessing body
composition.
Acknowledgements The authors wish to thank the University of
Minnesota, Twin Cities.
Funding The authors report no funding source.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
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MM Harbin et al.
	Validation of a three-dimensional body scanner for body composition measures
	Abstract
	Introduction
	Methods
	Subjects
	Procedures
	BIA and anthropometry measures
	Infrared 3D body scanner measures
	Hydrostatic weighing measures
	Statistical analysis
	Results
	Discussion
	Compliance with ethical standards
	ACKNOWLEDGMENTS
	References

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