A Smartphone Tool for Evaluating Cardiopulmonary Resuscitation
(CPR) Delivery
Gavin Corkery and Kenneth Dawson-Howe
School of Computer Science and Statistics, Trinity College, University of Dublin, Ireland
Keywords:
Video Processing, Machine Vision, Optical Flow, Cardiopulmonary Resuscitation.
Abstract:
This paper presents a prototype smartphone application to aid with the delivery of cardiopulmonary resuscita-
tion (CPR). The person giving CPR is viewed from the side and both compressions and breaths are identified
primarily using optical flow. This allows the system to provide near real time feedback on the chest com-
pression rate (CCR) and on the timing of breaths (which affects the Chest Compression Fraction (CCF)). The
system is evaluated on over 25 minutes of video of 6 different participants delivering CPR to a test dummy. A
quantitative evaluation is presented which shows that the system recognised 99% of compressions and all of
the breaths (although two false positive breaths were classified). It computed the CCF to within 1%.
1 INTRODUCTION
The use of cardiopulmonary resuscitation (CPR) has
been shown to increase survival levels from 5.2% to
7.8% in the case of normal CPR and to 13.3% in
the case of Compression-only CPR (COCPR) (Bo-
brow et al., 2010). In the same study they found that
CPR or COCPR was only administered in around one
third (34.3%) of the out-of-hospital cardiac arrest in-
cidents. However it seems that this rate can be signi-
ficantly increased (to around 60%) when proposed by
the emergency services telephone dispatcher (Dami
et al., 2010). In addition, it appears that if a defibrilla-
tor (AED) is available the survival rate increases sig-
nificantly. In a study by Iwani et al. (Iwami et al.,
2012) considering only cases where an public access
AED was used, the survival rate for normal CPR was
32.9% and for COCPR was 40.7%.
1.1 CPR Guidelines
The (American Heart Association (AHA)) re-
commendations for CPR are currently that chest
compressions (CC) should be applied at a rate (CCR)
of 100-120 per minute to a depth (CCD) of 5-6cm
allowing full recoil (decompression of the chest)
after each compression (AHA, 2015). The two hands
should be placed (one on top of the other) with the
heel of the lower hand in the centre of the chest on
the lower half of the breastbone (sternum), and com-
pressions should be done firmly and smoothly
pressing downward while keeping the arms straight.
For a layperson who is trained in giving rescue bre-
aths it is recommended that the chest compressions
should be alternated with rescue breaths in a ratio of
30 compressions to 2 breaths. Each breath should
be delivered over one second causing the chest to
rise, and the chest should fall again between breaths.
The period taken for the breaths reduces the chest
compression fraction (CCF - i.e. the portion of time
during which compressions are performed) which
should be as high as possible with a target of at least
60%.
1.2 Existing Systems for
Aiding/Evaluating CPR
The AHA guidelines state that it “may be reasonable
to use audiovisual feedback devices during CPR for
real-time optimization of CPR performance”, but no-
tes that “studies to date have not demonstrated a signi-
ficant improvement in favorable neurologic outcome
or survival to hospital discharge with the use of CPR
feedback devices during actual cardiac arrest events”
(AHA, 2015). In recent years the range of techno-
logy available to provide CPR quality feedback has
significantly expanded so there is some potential for
this feedback to improve positive outcomes. In addi-
tion such technologies provide methods for improving
CPR training.
Corkery, G. and Dawson-Howe, K.
A Smartphone Tool for Evaluating Cardiopulmonary Resuscitation (CPR) Delivery.
DOI: 10.5220/0007258904890496
In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019), pages 489-496
ISBN: 978-989-758-354-4
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
489
In lab/training scenarios, transmitter-receiver
pairs have been used for evaluating chest compression
depth (Kim et al., 2017; Kang et al., 2010) with high
precision. Also in a lab environments evaluation (of
CCR, CCD and CCF) has been done using RGB-D
sensors (Higashi et al., 2017; Loconsole et al., 2016)
and balance boards (Hayashi and Minazuki, 2017; Hi-
gashi et al., 2017; Ferreira et al., 2017) which can give
a measure of the force direction as well as the depth.
These preceding technologies are only appropriate for
use in lab/training environments but a number of new
technologies have potential in the field.
Every AED has sensors built into it and these have
been used to measure both CCR and CCF (Torney
et al., 2016; Gonzlez-Otero et al., 2012). Accelero-
meters have been used independently (Yamamoto and
Ohmura, 2015; de Gauna et al., 2015), on smartwat-
ches (Ahn et al., 2016), or smartphones (Song et al.,
2015; Amemiya and Maeda, 2013) to evaluate CCD
and CCR. In addition smartphone cameras have been
used to evaluate CCR based on a view looking up-
wards at the person applying CPR (Meinich-Bache
et al., 2017; Frisch et al., 2014), where the smartp-
hone is lying flat on the ground
There are hundreds of smartphone applications
which aid in the training and delivery of CPR (Ahn
et al., 2016) although most of these tools do not pro-
vide feedback regarding the quality of the CPR being
given. For CPR tools which do provide CPR quality
feedback, this is done as audio feedback, typically
in the form of a metronome (Amemiya and Maeda,
2013; Loconsole et al., 2016) or messages such as
“Push Faster”, “Good Speed”, “Push Slower”) (Tor-
ney et al., 2016) and as video feedback, typically a
colour indication of CCR quality (Ahn et al., 2017;
Amemiya and Maeda, 2013).
Only one piece of research (that we could locate)
considers the posture of the person performing CPR
in terms of keeping the arms straight (Higashi et al.,
2017). No research could be located which consider
the position of the hands on the chest of the patient.
In addition Panicker et al. presented work identi-
fying CPR scenes in medical. simulation videos (Pa-
nicker et al., 2015)
1.3 System Concept
The research presented in this paper describes the cre-
ation and evaluation of the first version of a smartp-
hone application intended to aid in the delivery of
CPR. The concept of the system is that it should as-
sess the CCR (and the breathing phases) based on the
analysis of a video of a person giving CPR taken on
a smartphone. This evaluation must happen as close
Figure 1: Dense optical flow showing downward movement
(left) and upward movement (right).
to real-time as possible and provide feedback on the
CCR so that the person can increase or decrease the
compression rate appropriately.
2 SYSTEM
To recognise both the compressions and the breaths
we rely on dense optical flow (as implemented in
OpenCV); See Figures 1 and 2.
In order to eliminate some noise, the amount of
movement from frame to frame must meet a certain
threshold before it is considered. This reduces the im-
pact of very small movements on the algorithm. Exa-
mining video footage showed a great amount of mo-
vement in the compressions, so a small threshold does
not limit the recognition of this movement. The thres-
hold was chosen to be quite small, requiring at least
0.5 pixels of movement between frames. This recog-
nises the moving region of interest while reducing a
lot of noise.
In order to reduce noise from the movement of
background objects, a model of the moving and non-
moving regions of the scene is maintained. As the
CPR session progresses, the regions with a great
amount of historical movement are given a greater
weighting over those that have been historically sta-
tic, meaning that background movement will cause
fewer false positives. The weighting is updated at
each frame for each pixel by the amount of movement
from the previous frame, multiplied by a small lear-
ning rate (experimentally chosen to be 0.005). After
several compressions, there will be a clear model of
VISAPP 2019 - 14th International Conference on Computer Vision Theory and Applications
490
the moving regions of the image, which include the
head, chest and arms of the person giving CPR. The
weighting is not updated during the breathing phase,
as the movement model is focused solely on the mo-
vement during compressions.
2.1 Recognising Compressions
Having a weighted motion field for each frame
forms the basis for recognising chest compressions.
Through analysis of test videos, it is clear that a chest
compression can be described by two phases: a strong
upward motion phase and a strong downward motion
phase (See Figure 1). A compression is deemed to be
found when strong upward movement occurs after a
strong downward movement. The upward movement
must be within a small window of time after the do-
wnward movement.
Compressions are characterised by both strong up-
ward and downward movement. To account for noisy
scenes, the dominant movement must also be several
times (the ratio chosen was 3) greater than the mo-
vement in the other direction. This ratio is useful in
a scene with a lot of background movement. There is
also a threshold for the amount of the scene that must
be moving. This was chosen to be 10% to ensure that
the movement takes up at least a small portion of the
scene.
In order to reduce false positives, the timing of the
upward movement in relation to the downward mo-
vement is also measured. For a compression to be
recognised, the upward movement must take place no
longer than 1 second after the downward movement.
This value was chosen to reduce false positives, ho-
wever it may cause difficulties with recognising ex-
tremely slow compressions.
2.2 Recognising Breaths
The recognition of breaths uses a similar technique to
the recognition of compressions. The recognition of a
breath is split into two phases: the downward lateral
movement and the upward movement at the end of the
breath (See Figure 2).
The lateral movement is recognised by using a
threshold based on the mean lateral movement du-
ring compressions. The total movement to the left
or right must be 5 times greater than the mean mo-
vement in that direction. The lateral movement must
also be at least a third of the downward movement.
This movement must take place in at least 3 conse-
cutive frames to be counted as the start of a breath.
This is to ensure that the movement is actually taking
place. The upward movement at the end of the bre-
Figure 2: Rescuer movement while starting breaths (left),
during breaths (middle) and moving back into compressions
(right).
Figure 3: Simplified flow of vision algorithm upon recei-
ving a new frame.
ath is recognised in the same way that the end of a
compression is recognised. This also acts as a mecha-
nism for recovering from error if a breath was falsely
identified.
The overall algorithm for processing individual
frames is illustrated in Figure 3.
2.3 Calculating CCR and CCF
In order to provide useful feedback, the application
calculates the CCR while performing compressions,
and the CCF when the CPR session has concluded.
The CCR is calculated using a weighted learning
approach. For each compression, the calculated rate
is computed as 36000 (the number of milliseconds in
a minute) divided by the number of milliseconds be-
tween compressions. This means that if there is half
a second (500 milliseconds) between two compressi-
ons, the rate is 120 compressions per minute. The
A Smartphone Tool for Evaluating Cardiopulmonary Resuscitation (CPR) Delivery
491
”time” of a compression is defined as the time of the
first strong upward movement after downward mo-
vement.
For the first 5 compressions, the impact of each
new compression is 50% of the overall rate. This al-
lows for a quick establishment of the rate at the start
of compressions. After the first 5 compressions, each
new compression makes up 15% of the overall rate.
The reason for this lower rate is to minimise the user’s
oscillation between slow and fast compressions due to
negative feedback causing vast overcorrections.
The CCF is calculated using the following formula
CCF =
(Totaltime InterruptedTime)
Totaltime
In this case, the interrupted time refers to the sum
of the periods of time which were at least 2 seconds
long and did not contain any compressions. This will
include time taken for breaths.
3 MOBILE APPLICATION
The implementation of the aforementioned techni-
ques is realised through a smartphone application.
The landing page of the application provides the user
with a view of the current scene through either the
front or rear camera, depending on user selection. In
training/practice situations, the smartphone should be
placed at a close distance from the CPR dummy, with
the CPR dummy lying between the smartphone and
the rescuer. Since different smartphone cameras will
have different fields of view, a precise optimal dis-
tance from the phone cannot be recommended. Ide-
ally, the rescuer and dummy should both be clearly
visible and fill a majority of the camera view.
3.1 Realising Acceptable Frame Rate
The most important aspect to consider in an applica-
tion processing live video is the resulting frame rate
after processing each frame, which can be quite ex-
pensive. Some of the videos were of a very high re-
solution (1920x1080) and this resulted in a frame rate
of approximately 1 frame per second (fps). To handle
this, each image frame is resized to a 216x216 pixels
frame, which is over 25 times smaller. This gives
a massive performance boost without degradation to
the computer vision algorithm. The resulting perfor-
mance increase affords a frame rate of approximately
15fps, which we found to be sufficient to provide high
quality feedback to the user. In addition, for speed
purposes, the vision algorithm does not analyse every
pixel, but samples at a constant rate. This is due to
the observation that the moving area will be a large,
mainly contiguous area. Analysing every pixel is the-
refore unnecessary so we analyse every 16th pixel in
each row or column is analysed, leading to
1
16
2
of mo-
vement to analyse. This spacing works well for all
tested distances, but may be too wide if the rescuer is
extremely far from the camera. The assumption, ho-
wever, is that the rescuer will never be so far away
from the camera for this to be an issue.
3.2 Methods of Feedback
As the person giving CPR would be focused on the
task, it is important that feedback is clear and does
not require careful analysis of the phone screen. The
visual feedback is as obvious and legible as possible,
while audio feedback is used to provide evaluation
without having to focus on the smartphone screen.
The information needed to provide metrics are pro-
cessed after the previous camera frame has been pro-
cessed, ensuring that feedback is always as up-to-date
as possible.
3.2.1 Visual Feedback
The primary measurable metric of CPR by the appli-
cation is the chest compression rate. When perfor-
ming CPR compressions, this is the aspect that should
be the main focus of the administrator’s attention. The
region at the top of the screen is used to provide an
easily-readable indicator of their current rate. The rate
is shown as a large number, which is colour-coded to
align with the recommended compression rate (See
Figure 4). Rates between 100 and 120 compressi-
ons per minute are optimal, and are thus displayed
with a green colour. Rates within 10 compressions
per minute of being optimal are shown with orange,
and rates further outside the optimal range are shown
in red. The displayed rate is updated after every com-
pression. This is done to ensure that any large rate
changes can be rectified as soon as possible.
Visual feedback is also provided to tell the user to
start breaths or to resume compressions (See Figure
5).
3.2.2 Audio Feedback
In cases where the person giving CPR may not be able
to focus their attention on the phone screen, it is pre-
ferable to have another way of delivering feedback in
a timely manner. This is achieved by using a speech
synthesiser giving feedback with a clear voice.
The audio feedback is related to the same pertinent
information dealt with by the visual feedback. Du-
ring the compression phase, the application will tell
VISAPP 2019 - 14th International Conference on Computer Vision Theory and Applications
492
Figure 4: Screenshots from the smartphone app showing
colour coded rates: optimal compression rate (left), sub-
optimal rate (middle), poor compression rate (right).
Figure 5: Screenshots from the smartphone app showing vi-
sual cues to perform breathing (left), that breathing is hap-
pening (middle), and that breaths have gone for too long and
compressions need to resume (right).
the user to keep going at their current rate if they are
achieving an optimal rate. If they are slightly out of
the optimal range they will be told to speed up slightly
or slow down slightly, and if they are very far out of
the range they will be told to speed up or slow down.
The frequency at which the user receives feedback on
their compression rate can be slow, medium or fast
(feedback every 6, 4 or 2 seconds respectively).
Feedback is also provided for other important as-
pects. If the user chooses to perform the 30:2 cycles
detailed above, they can choose to be notified to start
artificial ventilation every 30 compressions. Along
with this, they will be notified if their breaths have ta-
ken too long, and told to continue compressions. The
audio aspect of this is important, as the user would not
be focused on the phone screen if they are performing
artificial ventilation.
3.3 Viewing Summaries
At the conclusion of the CPR session, the administra-
tor is presented with a more in-depth analysis on the
summary screen (See Figure 6).
The goal of the summary screen is to provide furt-
her insight into the user’s habits that may not be appa-
rent from their CPR session. Examples of this could
Figure 6: Screenshot from the smartphone app showing
summary after CPR compressions.
be that the user starts their compressions too slowly
during each compression cycle, or that they are per-
forming too few or too many compressions per cycle.
Similarly to the visual feedback, the compression rate
per cycle is colour-coded in the same rate ranges. The
compression rate over time is graphed. If the user has
improved their CPR quality significantly over time,
this should be identifiable through analysis of current
and past CPR sessions.
4 EVALUATION
The performance of the computer vision algorithm
was measured on 11 pre-recorded videos of CPR
being performed by ourselves and also by volun-
teers from the Dalkey First Responders Group. This
footage consists of over 25 minutes of CPR with bre-
athes being delivered to test dummies (8 videos with
resolution 768x576 pixels 25 fps, and 3 videos with
resolution 1920x1080 at 30 fps). To evaluate the per-
formance of our system we created ground truth for
this footage in terms of the frame numbers of the lo-
west point of each compression, and the presence of
A Smartphone Tool for Evaluating Cardiopulmonary Resuscitation (CPR) Delivery
493
breaths between sets of compressions.
4.1 Evaluation of Individual
Compressions and Breaths
When performing the overall calculations for the test
videos, the following formulae for accuracy, recall
and precision are used (where TP is a True Positive:
A compression or breath which occurred and was re-
cognised by the system; FP is a False Positive: A
compression or breath which was recognised by the
system, which did not actually occur; FN is a False
Negative: A compression or breath which did occur
but was not recognised by the system):
Accuracy =
T P
T P + FP + FN
Accuracy defines the proportion of times that the clas-
sification is correct.
Recall =
T P
T P + FN
Recall defines the proportion of breaths and compres-
sions which are correctly identified.
Precision =
T P
T P + FP
Precision defines the proportion of times the classifi-
cation is correct when it says a breath or compression
has been identified.
4.2 Results
4.2.1 Compressions
The results for recognising compressions are very
good. The vision algorithm has an accuracy, preci-
sion and recall of 0.99. The false positives mainly
occur at the beginning and ending of the CPR ses-
sion, where the rescuer is kneeling down and posi-
tioning their hands, or standing up and exiting the
frame. This occurs due to the upward or downward
movement within the scene, which is falsely attribu-
ted to compressions. This will not affect the live rate
hugely, but it will affect the compression statistics on
the summary page. False negatives (missed compres-
sions) occur in two test videos. One reason for this is
a large amount of lateral movement is mistakenly as-
sumed to be the start of artificial ventilation, and sub-
sequent compressions are missed before the algorithm
self-corrects. The other reason is that compressions in
one video did not meet the movement threshold.
The results for recognising breaths are also very
good. Most notable is the fact that there are no missed
breaths. If missed breaths were to occur it is possible
that the overall rate would become inaccurate due to a
large gap between compressions without recognising
breaths in between them.
4.2.2 Evaluation of Chest Compression Rate and
Chest Compression Fraction
In all cases, the chest compression rate is sampled
every 2 seconds from the beginning of compressions
in the ground truth. This means that the results will in-
dicate the rate at the exact same points in time to give
a fair comparison. The chosen interval is 2 seconds
as this is the quickest option available for audio feed-
back on compression rates. The metrics used were
both the mean compression rate calculated throughout
each test video, but also the mean difference between
each calculated rate and the percentage of rates within
3 compressions per minute of each other. It is not only
important for the mean rates to be close, but for the
majority of rates at any point in time to be close to
one another. The results were very good (See Table
2), with the mean rates all within 1 compression per
minute of each other, and over 95% of rates in each
video being within 3 compressions per minute of the
real value. This suggests the calculated rate is good
enough to be considered an accurate assessment.
Discrepancies in the CCF between the vision algo-
rithm and the ground truth are mainly due to false po-
sitives at the start and end of the CPR session. These
false positives will falsely indicate that the CPR ses-
sion was longer than it actually was, giving a greater
overall fraction. The results for the CCF are encoura-
ging, with most results being within 1% of the true
result. In all cases, the computed CCF gives a rea-
sonable idea of whether the compressions are being
done for a suitable portion of time.
4.3 Comment on Results
The system gives extremely good results. Given that
there are very few false positives or false negatives
for compressions and breaths, it follows that the cal-
culated CCR and CCF are also very close to each ot-
her. The vision algorithm is not only able to recognise
breaths and compressions, but also of giving good re-
sults of the current rate at most points in time during
the CPR administration.
5 CONCLUSIONS
The prototype system presented here successfully
monitors CCR and breaths for people giving CPR. It
VISAPP 2019 - 14th International Conference on Computer Vision Theory and Applications
494
Table 1: True positives (TP), false positives (FP) and false negatives (FN) of compressions and breaths for each test video,
along with their totals. In addition the overall accuracy, precision and recall for compressions and breaths are presented.
Compressions Breaths
Video # TP FP FN TP FP FN
1 144 0 0 4 0 0
2.1 127 0 1 0 1 0
2.2 174 1 5 0 1 0
3 169 1 0 5 0 0
4 186 1 0 6 0 0
5.1 211 3 0 7 0 0
5.2 211 0 0 7 0 0
6.1 175 1 0 5 0 0
6.2 200 4 0 7 0 0
6.3 170 1 0 5 0 0
6.4 201 2 0 7 0 0
Totals 1974 14 6 53 2 0
Overall Accuracy Precision Recall Accuracy Precision Recall
0.99 0.99 0.99 0.96 0.96 1
Table 2: Mean CCR calculated from the ground truth and vision algorithm for each video, along with the proportion of rates
which were with 3 compressions per minute. In addition the CCF from the ground truth and from the system are presented.
Ground Truth Computed Proportion within Ground Truth Computed
Video # Mean CCR Mean CCR 3 compressions/min CCF CCF
1 108.00 108.15 1.00 0.773 0.773
2.1 103.80 103.90 0.95 0.958 0958
2.2 73.95 73.60 0.93 1.000 1.000
3 123.27 122.98 0.99 0.558 0.555
4 125.55 125.74 1.00 0.700 0.700
5.1 115.10 114.28 0.93 0.696 0.701
5.2 114.98 114.60 0.96 0.731 0.732
6.1 92.50 92.50 1.00 0.800 0.796
6.2 92.17 91.60 0.95 0.795 0.807
6.3 92.84 92.69 1.00 0.800 0.791
6.4 92.22 91.59 0.96 0.771 0.807
also provides an accurate value of the CCF. As this is
the first stage in a larger development it has not yet
been tested for robustness with respect to the position
of the camera relative to the person giving CPR.
The concept clearly has potential for use in
training environments, where volunteers typically
practice CPR every time they meet but rarely with
precise feedback on CCR or CCF. In the longer term
this technique might have potential for use in the field
although a number of other issues need to be addres-
sed (such as CCD, robustness, camera location requi-
rements, etc.).
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