JMIR FORMATIVE RESEARCH

Schmucker & Haag

Original Paper

Automated Size Recognition in Pediatric Emergencies Using
Machine Learning and Augmented Reality: Within-Group
Comparative Study
Michael Schmucker, BEng, MSc; Martin Haag, Dr sc hum
GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany

Corresponding Author:
Michael Schmucker, BEng, MSc
GECKO InstituteHeilbronn University of Applied Sciences
Max-Planck-Str 39
Heilbronn, 74074
Germany
Phone: 49 1757581477
Email: michael.schmucker@hs-heilbronn.de

Abstract
Background: Pediatric emergencies involving children are rare events, and the experience of emergency physicians and theresults of such emergencies are accordingly poor. Anatomical peculiarities and individual adjustments make treatment during
pediatric emergency susceptible to error. Critical mistakes especially occur in the calculation of weight-based drug doses.
Accordingly, the need for a ubiquitous assistance service that can, for example, automate dose calculation is high. However, few
approaches exist due to the complexity of the problem.
Objective: Technically, an assistance service is possible, among other approaches, with an app that uses a depth camera that is
integrated in smartphones or head-mounted displays to provide a 3D understanding of the environment. The goal of this study
was to automate this technology as much as possible to develop and statistically evaluate an assistance service that does not havesignificantly worse measurement performance than an emergency ruler (the state of the art).
Methods: An assistance service was developed that uses machine learning to recognize patients and then automatically determines
their size. Based on the size, the weight is automatically derived, and the dosages are calculated and presented to the physician.
To evaluate the app, a small within-group design study was conducted with 17 children, who were each measured with the app
installed on a smartphone with a built-in depth camera and a state-of-the-art emergency ruler.
Results: According to the statistical results (one-sample t test; P=.42; α=.05), there is no significant difference between themeasurement performance of the app and an emergency ruler under the test conditions (indoor, daylight). The newly developed
measurement method is thus not technically inferior to the established one in terms of accuracy.
Conclusions: An assistance service with an integrated augmented reality emergency ruler is technically possible, although somegroundwork is still needed. The results of this study clear the way for further research, for example, usability testing.
(JMIR Form Res 2021;5(9):e28345) doi: 10.2196/28345
KEYWORDS
resuscitation; emergency medicine; mobile applications; mobile phone; user-computer interface; augmented reality; machinelearning

Introduction
Background
The results in pediatric emergencies are not satisfactory. Too
few children survive such emergencies with favorable
neurological outcomes [1,2]. There are several reasons for this.
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RenderXFirst, this type of emergency is very rare; in Germany, forexample, there are only 1000 prehospital resuscitations for
30,000 emergency physicians per year, that is, on average, one
emergency physician resuscitates a child every 30 years [3].
This observation deliberately ignores that there are speciallytrained pediatric emergency physicians in urban areas, as
pediatric emergency physicians are not common. Second,
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emergency physicians find it difficult to remain calm in a childemergency. In a survey of 104 emergency physicians, conductedby Zink et al [4], 88% said that they had already felt anxiety orexcessive pressure at work. When asked for the reason, 84%
said they had experienced these feelings in a pediatricemergency, followed by polytraumatized patients (20%) andobstetric emergencies (18%). Multiple answers were possible.
Apart from the fact that the patient is a child and thepsychological consequences that may result, it is mainly the
anatomical differences between children and adults and theassociated peculiarities of resuscitation that cause problems for
emergency physicians. Although the resuscitation of an adultis quite standardized, there are individual differences in every
child. The choice of using different processes as well as
equipment is influenced by the size or weight of the child. For
example, the size of the endotracheal tube and depth of insertion
are specified [5] or the dosage of medication is calculated
individually based on the patient’s weight [6]. Especially indrug dosing, mistakes happen rather frequently, sometimes withlife-threatening consequences [7-9]. This is because it is difficult
to determine a child's weight and thus the correct dose. There
are various methods to determine a child’s weight. As Youngand Korotzer [10] describe in their systematic analysis, the mostprecise method is parental estimation. If the parents are notpresent, the state of the art is to derive the weight from the heightof the child using a so-called emergency ruler (eg, Broselow
tape [6]). These tools are important, because the medics’
estimations are not very accurate, according to the systematic
analysis mentioned earlier [10]. Despite these aids, emergency
physicians repeatedly express a desire for technical aids [7,8].
Therefore, one idea is to create a ubiquitous assistance service
that uses modern wearables (eg, a smartwatch for measuring
the compression depth [11], head-mounted displays [HMDs]
as screens or for telemedical scenarios [12,13]) to provide aservice that requires as little attention as possible while still
providing great assistance (principle of calm technology [14]).
To accomplish this, a high degree of automation must be
achieved in addition to a high level of usability. The idea is torecognize the patient with computer vision algorithms and to
be able to measure the patient directly using a depth camera.
All other parameters can then be automatically derived,
calculated, and displayed on an HMD, for example, as integrated
into the process steps of the American Heart Association [15]or the European Resuscitation Council (ERC) [16] guidelines.
After literature research, expert interviews, and initial research
results [17], an app was programmed and evaluated using acomparative study to apply this level of automation.

State of the Art
There are several approaches to replace emergency rulers using
technical support, for example, with a smartphone or a tablet[18-24]. Promising studies have already confirmed that the useof an app can minimize errors [22-24]. However, the problem
with most apps (all mentioned earlier but one) is that there is
no automation of size recognition, that is, manual entries arenecessary. Apart from usability, there is also the problem thatthese values (age, weight, or height) must be known first. For
inhospital cases, it can be assumed that the weight of the childis known; however, this does not apply to prehospital cases.
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For some apps, even the now-obsolete age-based formula for
calculating the dosages is used [18], which is inferior to thelength-based method [10]. A very interesting app is Optisizer
developed by Wetzel et al [21]. A 20×20-cm tag placed next tothe child is used as a reference value for the size. A first clinical
trial looks promising [21]. However, the tag must be at the samelevel as the child, and the measurement must always be taken
at a 90° angle. This is simply because a camera without
additional sensor technology has no relationship to angle anddepth, and therefore, an accurate calculation cannot be madeautomatically. A revised version of this app, which should solve
this problem, is announced by the authors in the outlook [21].
The aim of this paper is therefore to fill this gap. An app is
programmed and evaluated that uses augmented reality (AR)and a depth camera to provide a simple, fast, and safe way toautomatically determine a child’s weight and thus the medication
dosage.

Methods
BackgroundThe evaluated app is based on a prototype in which themeasuring accuracy of the Asus ZenFone AR’s depth cameras
has already been proven [17]. This does not deviate significantly
from the measurements made with the aid of an emergency
ruler. However, the handling was problematic; the individual
measuring points (head and foot of the child) had to be marked
manually. To address this problem, the app was further
developed so that it recognizes the child using machine learningand then performs an automated measurement. Furthermore,dosages for adrenalin and amiodarone are calculated and madeavailable to the user. The calculations are based on the data of
the KiGGS (Studie zur Gesundheit von Kindern undJugendlichen in Deutschland [German Health Interview andExamination Survey for Children and Adolescents]) study of
the Robert Koch Institute (RKI) [25] and the formulas stated in
the ERC guidelines [16]. It must therefore be evaluated whether
the new functionality of the app can repeat the good values ofthe previous study. The decisive factor here is how well theprocess of machine vision (recognition of the child, head tofoot) and the size recognition work in combination.

App Design and Technology
The size recognition of a patient and the dose calculation
basically consist of three steps. In the first step, a person isdetected in the camera’s field of view using an object
recognition algorithm and is classified as a human being. In
addition, the area in the image in which the child is located must
be delimited as precisely as possible from the surroundings, andthe measurement points (upper and lower limits) must be
defined. In the second step, the distance between these twopoints and the camera is measured. This defines two points in
3D space, and the size can be calculated. In the final step, basedon stored data, the respective dosages must be loaded anddisplayed.

Object Recognition
As soon as the app is started, it is ready to detect objects. The
object detection is performed using the TensorFlow Object
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Detection API [26] and the TensorFlow Detection Model Zoo[27]. Based on different parameters, such as GitHub activity,
Google searches, books, or job descriptions, it can be said that
TensorFlow is the leading deep learning framework [28].
Recognizing people is one of the standard tasks of machine
vision, so it should not be necessary to train the entire
functionality from the beginning. To simplify the clarity of theapp and to save resources, only the functionality for recognizing
persons is activated. If a person is recognized, a bounding box
is placed around this person and the confidence is displayed.
With a confidence of 98% or more, the coordinates of thebounding box are stored in variables. The respective
y-coordinates of two diagonally opposite points of the four
corner points of the box indicate the size of the person.

Size Measurement
The size is measured using the Google Tango framework.
Switching between the activities is done using the Intent classof Android. Tango is the predecessor of ARCore [29] and wasdeveloped for mobile phones with depth cameras. By default,
Tango uses touch to set the measurement points manually. To
automate this, the points from before are adopted. To prevent
diagonal measurements, the x values of the two points areaveraged and used as x-coordinates for both measuring points.
It is important that both the object detection and the size
measurement work with the same resolution (in this case,
1920×1080 pixels).

Schmucker & Haag

Dose Calculation
Depending on the size, the corresponding weight is loaded froma store and the appropriate dose is calculated and displayedusing the formulas specified in the guidelines of the ERC [16].
The size to weight ratio is, as already mentioned, created using
data from the KiGGS study of the RKI [25] and the formulas
given in the guidelines of the ERC [26].

Study Design and Measurements
The study design was a within-group setting in which each of
the children was anonymously measured first with the app,installed on a ZenFone AR, and then with an emergency ruler(Pediatape [30]). The measurements (S1, S2) were performed
in a room of a kindergarten during daylight. The children werelying on a wooden floor. For the measurement with the app (S1),the person taking the measurement stood in front of the child;the angle or the height of the camera was not specified. The
process was similar to taking a photo with the smartphone. The
only important factor was that the person being measured was
captured as a whole by the camera (see Figure 1). Whenmeasuring with the emergency ruler (S2), the beginning of themeasurement was placed at the head and the size was then readat the foot (see Figure 1). For both measurements, it was
important that the person did not curve their legs. The
parameters of age, height, weight, and gender of the children
were not known from the beginning and were therefore
randomly selected. In conclusion, there was one independent
variable (measuring device) that took two values (app,emergency ruler).

Figure 1. Schematic representation of the two measurements. S1, measurement with the app; S2, measurement with the emergency ruler.

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Recruitment
The study included 17 children from two kindergartens.
Originally, over 50 parent letters were issued, but at the timeof the study, only 17 permissions were obtained. There was noformal sample size calculation; the analysis plan was decided
post hoc.

Legal Aspects
Because the measurements performed could not cause any
definable harm, no ethics committee was consulted prior to the
execution. A legal review of the app with regard to the Medical
Devices Act is pending but is considered to be too early at thisstage.

Statistical Analysis
Shapiro-Wilk Test
Because fewer than 30 subjects were available and we could
not rely on the central limit theorem, which states that a sample
is normally distributed when more than 30 samples are present,a Shapiro-Wilk test was performed prior to the statistical test
selection to check for a normal distribution of the collected data.

Descriptive Statistical Analysis
To better understand the data before performing a statisticaltest, some descriptive statistics were calculated and plotted.
These include the average of the respective measurements (S1,
S2), the median, and the standard deviation. In addition, it waschecked whether the expected high correlation between therelated measurements and the expected correlation of themeasurements with the corresponding weight of the children
was actually present. Furthermore, the measured heights arepresented with the corresponding weights.

One-Sample t Test
To test for a significant difference, a one-sample t test comparing
the mean deviation of the difference (S1 – S2) to the reference
value zero was executed. Two measurements are identical if

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the difference of the individual measurements is zero. The
following hypothesis was tested at the significance level α=.05:
H0: There is no difference between the augmented
reality application with automated size recognition
using machine vision on the ZenFone AR and thePediatape emergency ruler in the quality of themeasurements.
If there were substantial differences, the app would not besuitable to act as an automated AR emergency ruler.

Bland-Altman Limits of Agreement
For a better overview, the results were additionally presented
graphically. For this purpose, the Bland-Altman plot was used
[31,32]. Thereby, the differences (delta) S1 – S2 of the individual
measurements (S1, S2) were plotted against their mean ((S1 +S2)/2). This resulted in the following formula:

The upper and lower limits of agreement (LOA) are defined as± 1.96s at a significance level of α=.05, where represents
the mean difference and s the standard deviation of pairwise
differences. If 95% of the measurements lie within the LOA,
both methods can be considered interchangeable, that is, bothmethods are equally appropriate [32].

Regression Analysis
To exclude a proportional bias, a linear regression analysis was
performed at the end with the dependent variable (themeasurement difference) and the independent variable (themean).

Results
Table 1 lists the measurements performed and the associatedweight, sorted ascending by the height, measured with the app.
If the height was somewhere between x.3 and x.7, x.5 was used;otherwise, the nearest full centimeter was used. This level ofambiguity is not a problem in the tested use case.

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Table 1. Participants’ characteristics.
Participant no.

Height measured with the app (cm)Height measured with the emergency ruler (cm)Weight (kg)

1

98

98

15.1

2

98.5

97.5

15.3

3

99

101

15.3

4

99.5

98.5

15.4

5

100

100

18.9

6

101

105

20.2

7

102

100

15.1

8

104.5

105.5

19.4

9

107

107

19.4

10

111

113

21

11

111.5

112.5

22.6

12

112.5

113.5

21.5

13

113.5

112.5

20.5

14

113.5

116.5

23.1

15

119

118

20.8

16

119

117

23.1

17

121

119

19.4

Shapiro-Wilk Test
Due to the small sample size, before applying a t test, it must
be examined whether the sample is normally distributed. The
difference (S1 – S2) and the mean ((S1 + S2)/2) were tested. The
test was carried out using SPSS (IBM Corporation).

As Table 2 shows, none of the P values are smaller than thesignificance level α=.05 (Pdiff=.38; Pmean=.06). It can therefore
be assumed that the sample is normally distributed in terms ofboth difference and mean.

Table 2. Shapiro-Wilk test results.
StatisticW

P value

Difference

.95

17

.38

Mean

.90

17

.06

Table 4 and Figure 2 show a strong correlation between bothmeasurements, as expected.

Descriptive Statistical Analysis
Measured with the emergency ruler, the subjects are on averageøer≈108 cm tall with a median of=107 cm and a standard
deviation of ser=7.88 cm. The app comes to an average of

There is also a strong correlation between the measured heights(S1, S2) and the associated weights (Table 5, Table 6, and Figure3).

øapp≈107.5 cm, also with a median of=107 cm and astandard deviation of sapp=7.82 cm (see Table 3).
Table 3. Descriptive statistics.
Ø (cm)(cm)

s (cm)

Emergency ruler

108

107

7.88

Augmented reality app

107.5

107

7.82

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Table 4. Correlation analysis (Pearson r and significance) between the measured heights using the app (S1) and ruler (S2) (N=17).
Variable

Height measured by emergency ruler

Height measured by the app

1.98

Height measured by emergency rulerr
P valuea<.001b—Height measured by the appr
P value
a

.98

1
b

<.001

—

Not applicable.

b

The correlation is significant at a significance level of .05 (two-tailed).

Figure 2. Correlation between the measurements taken with the emergency ruler and the app.

Table 5. Correlation analysis (Pearson r and significance) between the measured height using the app and the weight.
Variable

Height measured by emergency ruler

Weightr

1.85

P value—a<.001br.85

1

P value<.001—

Height measured by emergency ruler

Weighta

Not applicable.

b

The correlation is significant at a significance level of .05 (two-tailed).

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Table 6. Correlation analysis (Pearson r and significance) between the measured height using emergency ruler and the weight.
Variable

Height measured by emergency ruler

Weightr

1.77

P value—a<.001br.77

1

P value<.001—

Height measured by emergency ruler

Weighta

Not applicable.

b

The correlation is significant at a significance level of .05 (two-tailed).

Figure 3. Correlation between both measured heights and the weight.

Bland-Altman LOA

One-Sample t Test
To determine a significant difference between the two
measurements, a one-sample t test of the measurement difference
variable to the reference value zero was performed.
There was no significant difference in the measurement qualityof the two measurements, according to the one-sample t test
(t16=.82, P=.42; mean difference .35, 95% CI –0.56 to 1.26).
The delta value of the measurements was not significantly
different from the reference value zero (can thus be retained.

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RenderX=.35, s=1.77). H0

In addition to the t test, the results are presented withBland-Altman LOA values to provide a better overview. As
can be seen in Figure 4, at least 95% of the measurements lie
between the upper and lower limits. Therefore, it can be said
that there is no significant difference in the quality of the two
measurements to the significance level α=.05 and that bothmeasurement methods are therefore interchangeable.
In most cases (9/17, 53%), there was a deviation of 0 to 1 cm.
In 14/17 cases (82%), the deviation was 2 cm or less. Deviations
with more than 3 cm were rare (1/17, 6%).

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Figure 4. Bland-Altman plot.

Regression Analysis
Table 7 shows no statistically significant result. The beta value
for the mean is close to zero; the t value is not significant. A

proportional bias can be excluded. Thus, the app works the same
regardless of the size of the children in the sample. For a better
understanding, the regression line is plotted in Figure 5.

Table 7. Regression coefficients.
UnstandardizedaStandardized

Model

Beta

SE

Beta

t (df)

P valueConstant

3.18

6.33

N/Aa

0.50 (15)

.63

Mean

–0.03

0.06

–0.12

–0.47 (15).65

N/A: not applicable.

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Figure 5. Regression line to exclude proportional bias.

Discussion
According to this study, there is no significant difference in thequality of the measurements between a state-of-the-art
emergency ruler and a smartphone with the newly developed
app. The development of an assistance service including an AR
emergency ruler is therefore technically possible. The t test
indicates no significant difference (t16=.82; P=.42; meandifference .35, 95% CI –0.56 to 1.26). As shown in Figure 4,
in more than half of the cases (9/17, 53%) the differences are
1 cm or less. The largest deviation is 4 cm. The sample size isadmittedly rather small, with 17 normally distributed samples,but the results suggest that the distribution will not change withlarger sample sizes. For quality assurance purposes, only theweight was collected in addition to the two measurements. Ageand gender were not included due to the principle of data
minimization and the promised protection of anonymity. Thismeans that it should not be necessary to calculate BMI
retrospectively. Although this would be theoretically feasible,
for the interpretation both age and gender are necessary inchildren [33]. Therefore, no classification of weight can be
made. It should be further noted that the measurements werecarried out in an indoor environment in daylight. Especiallystrong light or a bright background might affect the infrared
camera. It should also be noted that Google discontinued its
ambitious “Tango” project in 2018 [34], which was used forthis app. Due to the additionally required expensive hardware(depth camera with infrared sensor), the Tango project could
not become generally accepted. For AR game apps, it is notimportant how faultlessly objects are placed in space. Therefore,
Google has decided on a type of technology that runs on almost
all smartphones (ARCore). However, the Tango API is stillavailable in Google Archive [35]. The latest trend in AR,
however, is again moving toward depth sensing, as shown by
Microsoft with the HoloLens [36], by Samsung with the Galaxy
S20+ [37], or by Apple’s new iPhone 12 Pro with light detection
and ranging technology [38]. The extent to which these
implementations are suitable for continuing this research needs
to be evaluated. The long-term goal of this research is to createa ubiquitous pediatric emergency assistance service. An
important step has been achieved with the automatic sizerecognition. At the moment, a study at a simulation center isplanned to explore the usability aspects of the app under themost realistic conditions possible.

Conflicts of Interest
None declared.

References1.
2.

Mick NW, Williams RJ. Pediatric cardiac arrest resuscitation. Emerg Med Clin North Am 2020 Nov;38(4):819-839. [doi:10.1016/j.emc.2020.06.007] [Medline: 32981620]
Donoghue AJ, Nadkarni V, Berg RA, Osmond MH, Wells G, Nesbitt L, et al. Out-of-hospital pediatric cardiac arrest: anepidemiologic review and assessment of current knowledge. Ann Emerg Med 2005 Dec;46(6):512-522. [doi:10.1016/j.annemergmed.2005.05.028] [Medline: 16308066]

https://formative.jmir.org/2021/9/e28345

XSL• FO
RenderX

JMIR Form Res 2021 | vol. 5 | iss. 9 | e28345 | p. 9
(page number not for citation purposes)

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3.
4.
5.

6.
7.
8.

9.
10.
11.

12.

13.

14.
15.

16.

17.
18.
19.
20.
21.

22.

23.

24.

25.
26.
27.

ÄrzteZeitung. Kinder-Notfälle. Die Angst fährt mit zum Einsatz. URL: https://www.aerztezeitung.de/Medizin/
Die-Angst-faehrt-mit-zum-Einsatz-235732.html [accessed 2021-05-04]
Zink W, Bernhard M, Keul W, Martin E, Völkl A, Gries A. Invasive Techniken in der Notfallmedizin. Anaesthesist 2004
Nov;53(11):1086-1092. [doi: 10.1007/s00101-004-0762-5]
Boensch M, Schick V, Spelten O, Hinkelbein J. Estimation of the optimal tube length: systematic review article on publishedformulae for infants and children. Article in German. Anaesthesist 2016 Feb;65(2):115-121. [doi: 10.1007/s00101-015-0123-6][Medline: 26696266]
Schmidt AR, Haas T, Buehler KP, Ruetzler K, Weiss M, Schmitz A. Broselow®-Tape und Pädiatrisches Notfalllineal im
Vergleich. Notfall Rettungsmed 2015 Sep 21;19(2):129-135. [doi: 10.1007/s10049-015-0070-4]
Kaufmann J, Laschat M, Wappler F. Medication errors in pediatric emergencies: a systematic analysis. Dtsch Arztebl Int
2012 Sep;109(38):609-616 [FREE Full text] [doi: 10.3238/arztebl.2012.0609] [Medline: 23093991]
Appelbaum N, Clarke J, Feather C, Franklin B, Sinha R, Pratt P, et al. Medication errors during simulated paediatricresuscitations: a prospective, observational human reliability analysis. BMJ Open 2019 Nov 25;9(11):e032686 [FREE Fulltext] [doi: 10.1136/bmjopen-2019-032686] [Medline: 31772103]
Hoyle JD, Davis AT, Putman KK, Trytko JA, Fales WD. Medication dosing errors in pediatric patients treated by emergency
medical services. Prehosp Emerg Care 2012;16(1):59-66. [doi: 10.3109/10903127.2011.614043] [Medline: 21999707]
Young KD, Korotzer NC. Weight estimation methods in children: a systematic review. Ann Emerg Med 2016
Oct;68(4):441-451.e10. [doi: 10.1016/j.annemergmed.2016.02.043] [Medline: 27105839]
Gruenerbl A, Pirkl G, Monger E, Gobbi M, Lukowicz P. Smart-watch life saver: Smart-watch interactive-feedback system
for improving bystander CPR. 2015 Sep 07 Presented at: UbiComp '15: The 2015 ACM International Joint Conference on
Pervasive and Ubiquitous Computing; 2015; Osaka, Japan. [doi: 10.1145/2802083.2802086]
Drummond D, Arnaud C, Guedj R, Duguet A, de Suremain N, Petit A. Google Glass for residents dealing with pediatriccardiopulmonary arrest: a randomized, controlled, simulation-based study. Pediatr Crit Care Med 2017 Feb;18(2):120-127.
[doi: 10.1097/PCC.0000000000000977] [Medline: 28165347]
Siebert JN, Ehrler F, Gervaix A, Haddad K, Lacroix L, Schrurs P, et al. Adherence to AHA guidelines when adapted foraugmented reality glasses for assisted pediatric cardiopulmonary resuscitation: a randomized controlled trial. J Med Internet
Res 2017 May 29;19(5):e183 [FREE Full text] [doi: 10.2196/jmir.7379] [Medline: 28554878]
Designing calm technology. Weiser M, Brown JS. 1995. URL: https://calmtech.com/papers/designing-calm-technology.
html [accessed 2021-05-04]
Merchant RM, Topjian AA, Panchal AR, Cheng A, Aziz K, Berg KM, et al. Part 1: Executive Summary: 2020 American
Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation 2020
Oct 20;142(16_suppl_2):337-357. [doi: 10.1161/cir.0000000000000918]
Maconochie IK, Bingham R, Eich C, López-Herce J, Rodríguez-Núñez A, Rajka T, Paediatric life support section
Collaborators. European Resuscitation Council Guidelines for Resuscitation 2015: Section 6. Paediatric life support.
Resuscitation 2015 Oct;95:223-248. [doi: 10.1016/j.resuscitation.2015.07.028] [Medline: 26477414]
Schmucker M, Igel C, Haag M. Evaluation of depth cameras for use as an augmented reality emergency ruler. Stud HealthTechnol Inform 2019;260:17-24. [Medline: 31118314]
Banker S. PediCalc medical app, customizable pediatric drug dosing at the touch of a button. iMedical Apps. URL: https://www.imedicalapps.com/2012/02/pedicalc-medical-app-pediatric-drug-dosing/# [accessed 2021-05-04]
Paediatricks. URL: http://paediatricks.com/peadiatrics [accessed 2021-05-04]
Pedi Safe. iAnesthesia LLC. URL: https://ianesthesia.org/apps/pedi-safe/ [accessed 2021-05-04]
Wetzel O, Schmidt AR, Seiler M, Scaramuzza D, Seifert B, Spahn DR, et al. A smartphone application to determine bodylength for body weight estimation in children: a prospective clinical trial. J Clin Monit Comput 2018 Jun;32(3):571-578.
[doi: 10.1007/s10877-017-0041-z] [Medline: 28660564]
Corazza F, Snijders D, Arpone M, Stritoni V, Martinolli F, Daverio M, et al. Development and usability of a novel interactive
tablet app (PediAppRREST) to support the management of pediatric cardiac arrest: pilot high-fidelity simulation-based
study. JMIR Mhealth Uhealth 2020 Oct 01;8(10):e19070 [FREE Full text] [doi: 10.2196/19070] [Medline: 32788142]
Siebert JN, Lacroix L, Cantais A, Manzano S, Ehrler F. The impact of a tablet app on adherence to American Heart
Association guidelines during simulated pediatric cardiopulmonary resuscitation: randomized controlled trial. J Med Internet
Res 2020 May 27;22(5):e17792 [FREE Full text] [doi: 10.2196/17792] [Medline: 32292179]
Siebert JN, Ehrler F, Combescure C, Lacroix L, Haddad K, Sanchez O, et al. A mobile device app to reduce time to drugdelivery and medication errors during simulated pediatric cardiopulmonary resuscitation: a randomized controlled trial. J
Med Internet Res 2017 Feb 01;19(2):e31 [FREE Full text] [doi: 10.2196/jmir.7005] [Medline: 28148473]
KiGGS: Studie zur Gesundheit von Kindern und Jugendlichen in Deutschland. Robert Koch Institut. URL: https://www.
kiggs-studie.de/english/home.html [accessed 2021-05-04]
GitHub. TensorFlow Object Detection API. URL: https://github.com/tensorflow/models/tree/master/research/object_detection[accessed 2021-05-11]
GitHub. TensorFlow 2 Detection Model Zoo. URL: https://github.com/tensorflow/models/blob/master/research/
object_detection/g3doc/tf2_detection_zoo.md [accessed 2021-05-11]

https://formative.jmir.org/2021/9/e28345

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RenderX

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28.
29.
30.
31.
32.
33.

34.
35.
36.

37.
38.

Schmucker & Haag

Hale J. Deep learning power scores 2018. towards data science. URL: https://towardsdatascience.com/deep-learningframework-power-scores-2018-23607ddf297a [accessed 2021-05-11]
Build the future. Google ARCore. URL: https://developers.google.com/ar [accessed 2021-05-11]
Pediatape. URL: http://www.pediatape.com/ [accessed 2021-05-04]
Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. Statistician 1983 Sep;32(3):307.
[doi: 10.2307/2987937]
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet
1986 Feb 8;1(8476):307-310. [Medline: 2868172]
Kromeyer-Hauschild K, Wabitsch M, Kunze D, Geller F, Geiß HC, Hesse V, et al. Perzentile für den Body-mass-Index
für das Kindes- und Jugendalter unter Heranziehung verschiedener deutscher Stichproben. Monatsschrift Kinderheilkunde
2001 Aug 1;149(8):807-818. [doi: 10.1007/s001120170107]
Pluta W. Google stellt Project Tango ein. Golem. URL: https://www.golem.de/news/augmented-reality-google-stellt-projecttango-ein-1712-131709.html [accessed 2021-05-04]
Project Tango Java API example projects. GitHub. URL: https://github.com/googlearchive/tango-examples-java [accessed2021-05-04]
Hübner P, Clintworth K, Liu Q, Weinmann M, Wursthorn S. Evaluation of HoloLens tracking and depth sensing for indoor
mapping applications. Sensors (Basel) 2020 Feb 14;20(4):1021 [FREE Full text] [doi: 10.3390/s20041021] [Medline:
32074980]
What is DepthVision camera and how do you use it? Samsung. URL: https://www.samsung.com/global/galaxy/what-is/
3d-depth-camera/ [accessed 2021-05-04]
iPhone 12 Pro and iPhone 12 Pro Max. Apple. URL: https://www.apple.com/iphone-12-pro/ [accessed 2021-05-04]

Abbreviations
AR: augmented reality
ERC: European Resuscitation Council
HMD: head-mounted display
KiGGS: Studie zur Gesundheit von Kindern und Jugendlichen in Deutschland (German Health Interview andExamination Survey for Children and Adolescents)
LOA: limit of agreement
RKI: Robert Koch Institute

Edited by M Focsa; submitted 02.03.21; peer-reviewed by D Kessler, L Suppan; comments to author 02.04.21; revised version received
25.05.21; accepted 19.06.21; published 20.09.21
Please cite as:
Schmucker M, Haag M
Automated Size Recognition in Pediatric Emergencies Using Machine Learning and Augmented Reality: Within-Group Comparative
Study
JMIR Form Res 2021;5(9):e28345
URL: https://formative.jmir.org/2021/9/e28345
doi: 10.2196/28345
PMID:©Michael Schmucker, Martin Haag. Originally published in JMIR Formative Research (https://formative.jmir.org), 20.09.2021.
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