Heart rate and respiratory rate detection algorithm based on the

Heart rate and respiratory rate detection algorithm based on
the Kinect for Windows v2
Paul Hofland
10558969
July 7, 2016
15 EC Bachelor Thesis Physics & Astronomy conducted between 01-03-2016 and 07-07-2016
Supervisors: Prof. Dr. M.C.G. Aalders & Ir. M.J. Brandt
Second evaluator: Prof. Dr. A.G.J.M. van Leeuwen
Academisch Medisch Centrum
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Universiteit van Amsterdam
1
Abstract
An algorithm based on the Kinect for Windows v2 that performs human heart- and
respiratory rate measurements without physical contact is developed. Both the theoretical
methods and the experimental results of this algorithm are presented. The respiratory
rate is determined by tracking the subject chest while measuring its movements with the
RGD-D depth camera. The heart rate is determined by tracking the subject’s face while
measuring its RGB color values, this allows for a photoplethysmography method to be
used. The green channel values are later analyzed in LabVIEW to determine the heart
rate. To experimentally validate this algorithm, fourteen tests with different operating
conditions were performed on eight subjects. The experimental results show that the
respiratory and heart rate can be determined. This shows that the Kinect v2 is a viable
alternative to other respiratory- and heart rate measurement devices, while it still has
more possible functionalities to offer.
2
Hartslag en ademhaling meten met een Kinect v2
Hartslag en ademhaling worden in het ziekenhuis normaal gesproken met meetinstrumenten gemeten die fysiek contact hebben met de patiënt. Dit zorgt ervoor dat er accuraat gemeten kan worden. Maar fysiek contact zorgt ook voor stress of ongemak bij de
patiënt waardoor de meting beı̈nvloed kan worden. Om dit probleem te vermijden moet
er zonder fysiek contact gemeten worden.
In dit onderzoek wordt er gebruik gemaakt van de Microsoft Kinect for Windows v2.
De Kinect v2 heeft een RGB kleurencamera, een RGB-D dieptecamera en een infrarood
camera die gebruik maakt van een infrarood licht bron. Deze sensoren zorgen ervoor dat er
beeld en diepte informatie tegelijkertijd gemeten kan worden. De Kinect v2 kan met deze
sensoren verschillende metingen doen. De emoties van de patiënt zouden bijvoorbeeld ook
gemeten kunnen worden. Het is daarom belangrijk om uit te zoeken wat de Kinect v2 kan.
Dit onderzoek presenteert een algoritme voor de Kinect v2 dat de hartslag en ademhaling
kan bepalen zonder fysiek contact. De ademhaling wordt bepaald door continue enkele
punten op het lichaam te volgen die samen een rechthoek over de borstkas vormen. Tijdens elke frame wordt van de ingesloten pixels de gemiddelde afstand tot aan de Kinect v2
gemeten. Het algoritme slaat dit op en vergelijkt deze afstand met de afstanden die gemeten zijn in vorige frames om te bepalen hoe snel de borstkas naar voren of naar achteren
beweegt. Door deze beweging te meten, kan het algoritme de ademhaling bepalen.
Om de hartslag te bepalen, worden er enkele punten op het gezicht bepaald en gevolgd
die samen twee rechthoekjes over de wangen vormen. We kijken naar de wangen omdat
daar de bloedstroom goed zichtbaar is vanwege de bloedvaten die daar onder de huid
lopen. Vervolgens worden de gemiddelde primaire kleuren (rood, groen en blauw) van de
ingesloten pixels opgeslagen. Het zuurstofrijke bloed dat vanaf het hart komt, absorbeert
meer rood licht dan het zuurstofarme bloed. Dit komt omdat verschillende atomen verschillende kleuren licht absorberen. Net als zwarte of rode verf zijn het de atomen die
bepalen welke kleuren licht er worden geabsorbeerd en welke kleuren licht niet. Hierdoor
kan het verschil gemeten worden tussen zuurstofrijk en zuurstofarm bloed ondanks dat
het zich onder de huid bevindt.
Om het voorgestelde algoritme te valideren, zijn er veertien metingen in verschillende
omstandigheden uitgevoerd, waarbij het algoritme de hartslag en ademhaling van de gebruiker bepaalde. Zo kan er geconcludeerd worden dat ademhaling en de hartslag in ideale
omstandigheden gemeten kan worden. Hiermee bewijst de Kinect v2 zich als alternatief
voor andere meetinstrumenten, en dat terwijl de Kinect v2 nog veel meer mogelijkheden
biedt.
3
Contents
1 Introduction
5
2 System configuration
6
2.1
Microsoft Kinect for Windows v2 . . . . . . . . . . . . . . . . . . . . . . .
6
2.2
Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
2.3
Crime-lite 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
3 Rate detection methods
7
3.1
Respiratory rate detection . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
3.2
Heart rate detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
4 Experimental work
10
4.1
Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
4.2
Work method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
5 Results
11
5.1
Respiratory rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
5.2
Heart rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
6 Discussion
16
7 Conclusion
17
8 Acknowledgements
17
Appendices
18
A
Microsoft Kinect for Windows v2 system requirements
18
Respiratory rate detection code
19
Heart rate data analysis graphical code
20
B
C
4
1
Introduction
Heartbeat and breathing are two fundamental processes in living organisms. There are
many respiratory and heart diseases which require monitoring. Most monitoring methods
use medical devices that require physical contact, methods such as plethysmography [7]
and pulse oximetry [8]. These methods may cause unease for the user, and will therefore
influence the measurement. Non contact monitoring methods have also been researched
[6] [9]. These methods work with great accuracy, but they can only measure the respiratory rate and/or the heart rate.
Nowadays, the field of computer vision has been developed rapidly for both the commercial and non-commercial market. The Kinect v2 is a product of this development and
delivers high accuracy sensors for a low consumer-grade cost [1]. The Kinect v2 offers a
RGB color camera, a RGB-D depth camera and a infrared camera by using a infrared
light source, which makes the Kinect v2 capable of capturing depth and image data simultaneously. These features have the potential to measure more than only the respiratory
and heart rate.
The SDK 2.0 from Microsoft allows new programmers, such as myself, to investigate
the possibilities that the Kinect v2 has to offer. Simultaneously detecting bruises and the
emotions of the user are potential functions of the Kinect v2. Combining these possibilities might even lead to a lie detector for abused kids. Capabilities of such a device are
therefore worth researching.
In this paper, we present a heart- and respiratory rate detection algorithm based on
the Kinect v2. The heart rate is determined by tracking the chest and using the RGB-D
depth camera to measure its distance, a similar method is presented in [6]. To determine
the heart rate, a photoplethysmography (rPPG) method is used, which looks at subtle
color changes on the skins surface [11]. To experimentally validate the algorithm, several
test with different operating conditions were performed and are presented.
In this paper, the Kinect for Windows v2, the software that has been used to create
the proposed algorithm and the light source are introduced in chapter two. Both methods
of calculating the heart- and respiratory rate are explained in chapter three. In chapter four, the experimental work is explained. The experimental results are presented in
chapter five. And finally, in chapters six and seven the paper is discussed and concluded.
5
2
System configuration
The proposed algorithm is based on the following hardware and software.
2.1
Microsoft Kinect for Windows v2
Microsoft’s Kinect for Windows v2 features a RGB color camera at a resolution of 1920
x 1080 pixels, a RGB-D depth camera at a resolution of 512 x 424 pixels and an infrared
camera at a resolution of 512 x 424 pixels, by using an infrared light source, as shown in
Figure 1. Microsoft stated that the Kinect v2 runs at 30 frames per second [3]. But in
our case, probably due to the performance demanding algorithm, an average sample rate
of 20.1 frames per second was achieved on the computer that the experimental measurements were done with. For the Kinect v2 system requirements and price see appendix A.
Figure 1: Microsoft Kinect for Windows v2. (left): Kinect v2 (front view). (right): Camera
configuration (front view). Copyright of IEEE Sensors Journal.
To determine the depth of objects, the Kinect v2 utilizes a Time-of-Flight method by
calculating the round-trip time of a pulse of light [2]. This method gives a better depth
image quality than its predecessor, that used a structured-light method. The depth accuracy and resolution are not uniform because they depend on distance, viewing angle
and edge noise [1]. For this reason, the error on the measurements are assumed to not be
significant for this research.
2.2
Software
The proposed algorithm is written in C# using Microsoft Visual Studio 2015. Microsoft
Kinect for Windows SDK 2.0 was used for its tracking abilities and libraries. NI LabVIEW
2015 was used to analyze the heart rate data.
2.3
Crime-lite 2
The Crime-lite 2 is a light emitting diode (LED) that has been used as the only light
source for all measurements. It provides a uniform white (400-700 nm) illumination
that is independent of the mains frequency [4]. This will avoid that the frequency of a
6
normal light source, which is the same as the main frequency (50Hz), would influence the
measurements.
3
Rate detection methods
To determine the respiratory and heart rate, the following methods were used.
3.1
Respiratory rate detection
To determine the respiratory rate, the subject has to be identified by the algorithm first.
This is done by recognizing several body parts (joints) on the human body and tracking
them every frame. This operation was possible by using the functionalities of the SDK
2.0 software from Microsoft, which can recognize up to 25 joints on the human body. A
method of identifying different human body parts from a single depth image is researched
and presented in [5]. For our purposes, only three joints on the human body are tracked:
the shoulders and the torso, as shown in Figure 2. These three points define the region
of interest, the subject’s chest.
Figure 2: Region of interest in red and tracked joints in yellow, achieved by using Microsoft’s
SDK 2.0. Image taken with the RGB-D depth camera of the Kinect v2.
After identifying and tracking the chest wall, the algorithm uses the RGB-D depth camera
to measure the distance of every pixel inside the region of interest. The mean value of
these distances will be calculated and saved every frame at an average rate of 20.1 frames
per second. To improve the signal, the algorithm takes the average of the mean depth
7
values of three consecutive frames. This results into an average data sample frequency of
6.7 frames per second, which is a good sample rate to detect the respiratory rate [6]. The
experimental results of this depth signal improvement are presented in chapter five.
The algorithm then compares the averaged measurements with each other by calculating the difference between them. The algorithm keeps track of the sign and determines
whether the subject is either inhaling or exhaling when the sign stays negative or positive
respectively three times in a row. This eliminates the irregularities of breathing.
After deciding whether the subject is inhaling, the algorithm will eventually find the
subject exhaling. When this happens, the algorithm finds and saves the point in time at
which the maximum distance between the chest and the Kinect v2 was measured. The
algorithm will only do this after the subject first inhaled and then exhaled. This assures
that the algorithm only takes the maximum distances into account. When three maximum
distances are found, the algorithm can calculate the frequency of the chest movements by
extrapolating the time between the three maxima. Doing this will give an almost real-time
respiratory rate of the subject. As the algorithm requires three maxima it will be three
breaths behind on the real-time respiratory rate. This method has been chosen instead
of taking more measurements and calculating its weighted average over time because this
would give an statistical respiratory rate, which was researched in [6], instead of a almost
real-time one. The experimental results of this respiratory rate detection method is presented in chapter five. For the respiratory rate detection code, see appendix B.
3.2
Heart rate detection
To determine the heart rate, the subject’s face has to be identified as well as the body.
This is done by recognizing several key points on the face such as the nose, eyes and
mouth. A mesh is created to fit over the subject’s face. This will create stationary points
(vertices) on the face which the algorithm can track. This operation is again achieved
by the SDK 2.0 software from Microsoft. A method of creating a non-uniform face mesh
from a three dimensional image is researched and presented in [10]. Using the vertices
that are positioned on the mouth corners and on the bottom of the eyes, two rectangular
boxes that form over the cheeks are created. These vertices define two regions of interest,
as shown in Figure 3.
The algorithm then measures the RGB values of every pixel in the regions of interest.
This allows for a photoplethysmography (rPPG) method to be used, which looks at subtle color changes on the skin surface using the RGB color camera [11]. The heart rate
detection of this method is based on the oxygenated blood flow that changes the amount
8
of hemoglobin molecules and proteins. These changes also affect the optical absorption
of the light spectrum. It is therefore possible to identify the heart rate based on color
changes in the reflections of the skin.
Figure 3: Regions of interest in red and tracked joints in yellow, achieved by using Microsoft’s
SDK 2.0. Image taken with the infrared camera of the Kinect v2.
Only the green channel is taken into account, as this channel has the best features for
photoplethysmography [11]. In NI LabVIEW 2015 the signal is first resampled at a steady
rate of 20 Hz. A steady sample rate instead of a varying one is required for a Fourier
analysis, which will be used later on. A low and high cutoff bandpass filter are applied
to ignore inhuman heart rate frequencies and to reduce noise. These bandpass filters are
set to 0.667 Hz and 1.667 Hz respectively. These frequencies represent a heart rate of
40 bpm and 100 bpm respectively. The assumption is made that the subjects will not
have a heart rate frequency outside of these values. A fast Fourier transformation is then
applied, which gives a spectrum of frequencies. The results found with this method are
presented in chapter five. For the heart rate data analysis graphical code, see Appendix
C.
9
4
Experimental work
To experimentally validate the proposed algorithm for the Kinect v2, fourteen tests with
different operating conditions were performed on eight subjects. The experimental measurements were executed as follows.
4.1
Experimental setup
The Kinect v2 was stationary positioned on top of a desk, one meter away from the
subject. The Crime-lite 2 light source was positioned behind the Kinect v2 to illuminate
the subject in both sitting and standing positions. The room in which the measurements
took place had no windows and was only illuminated by this light source. The subjects
had to wear a M70 fingertip pulse oximeter which was filmed by a camera that was also
positioned on top of the desk.
4.2
Work method
The eight subjects that were tested consisted of both man and women between the age
of 20 and 30. To not influence the measurement, the subjects were not told what was
measured. Instead they had been given the instruction to read a short story on a tablet.
Since the respiratory rate is easy to manipulate, this method was chosen to give a more
natural and realistic result. The subjects also had to wear a M70 fingertip pulse oximeter.
The measurements performed with this tool are used as a golden standard for the heart
rate detection results, as shown in chapter five. Every test that was done with the Kinect
v2 consisted of 1000 frames, comparable to approximately 50 seconds.
In total fourteen experimental tests were performed, consisting of nine tests in sitting
position and five tests in standing position. Out of the nine tests in sitting position, two
are depth signal improvement tests, five tests were done in ideal conditions, one test was
done while the subject was wearing a jacket and one test was done in bad lighting conditions. Out of the five tests in standing position, four tests were done in ideal conditions
and one test was done while the subject was wearing a jacket.
10
5
Results
The results of the respiratory and heart rate measurements and the depth signal improvement are presented in this chapter.
5.1
Respiratory rate
First a depth signal improvement measurement was done by averaging the mean distance
values of three consecutive frames. This results in a sampling rate of 6.7 frames per second
instead of 20.1 frames per second, which is a better sample rate to detect the respiratory
rate [6]. This also reduces the noise and makes the signal easier to analyze, as shown in
Figure 4.
Figure 4: Depth signal improvement of the proposed algorithm. (left): Improved signal at
6.7 fps. (right): Raw signal at 20.1 fps.
Using the depth signal improvement, four tests were done in ideal conditions with the
subjects in standing position. These results are shown in Figure 5, which shows the maximum distances found by the algorithm in red. The first maximum in all measurements
were not found by the algorithm because it has to detect the signal going down before it
can set the boundaries in between which the algorithm searches for a maximum distance.
The results show that after the first found maximum, some maxima aren’t found by the
algorithm. It is also notable that the signal is significantly worse when the subjects are
in a standing position. Results of the two tests that were performed in ideal conditions
with the subjects in sitting position are shown in Figure 6.
11
Figure 5: Depth signal of the subjects in standing position, in red the maxima found by the
algorithm.
Figure 6: Depth signal of the subjects in sitting position, in red the maxima found by the
algorithm.
Clothing such as a jacket, seem to only significantly affect the signal when the subject is
in a standing (a) position as shown in Figure 7. Again, a clear difference in signal quality
is visible between the subjects in standing (a) and sitting (b) positions.
12
Figure 7: Depth signal of the subject with a jacket on, in red the maxima found by the
algorithm. (a): Subject in standing position. (b): Subject in sitting position.
In bad lighting conditions, the algorithm is still capable of finding the maximum distances,
that are shown in red, see Figure 8. The results also show that the signal is noticeable
worse than the signal measured in ideal conditions (Figure 5).
Figure 8: Depth signal of the subject in standing position in bad lighting conditions, in red
the maxima found by the algorithm.
13
5.2
Heart rate
Figure 9 shows the green channel signals that are achieved by measuring the RGB values
of the cheeks. These signals have much more noise than the depth signals in Figure 5,6,7
and 8. Noise reduction and the removal of inhuman heart rate frequencies is necessary
before detecting a heart rate. The signals are analyzed in NI LabVIEW 2015, which
applies two bandpass filters and a fast Fourier transformation. These results are shown
in Figure 10.
Figure 9: Green channel signals of the subject’s cheeks, measured with the Kinect v2.
Figure 10 shows the frequency spectra with the the peak value in red, which indicates the
heart rate frequency. The determined heart rate using this proposed method, as well as
the measured heart rate with the M70 fingertip pulse oximeter, are shown in table 1.
14
Figure 10: Frequency spectrum of the subjects heart rate. In red the peak values which
indicates the heart rate frequency. (a): The peak indicates a heart rate of 55.4 bpm. (b):
The peak indicates a heart rate of 67.2 bpm. (c): The peak indicates a heart rate of 53.7
bpm.
Subject
M70 oximeter heart rate (bpm)
Detection heart rate (bpm)
(a)
60
55.4
(b)
70
67.2
(c)
51
53.7
Table 1: Heart rate measurement results.
15
6
Discussion
The average sampling rate was not consistent, as the time between two frames was varying
between 126 ms and 39 ms. This may result into too few data points in between breaths,
which means that the algorithm won’t detect it as a breath as it needs at least four data
points. Examples of this problem are visible in Figure 5. The algorithm is not optimized
which makes it performance demanding and probably increases the effect of a varying
sample rate. Using shapes instead of boxes to define the regions of interest has also been
tried, but this method was even more performance demanding.
Figure 5 also shows the signal being more chaotic than in Figure 6. This is due to
the subject movements while standing. For example, when the subject moves forward
while exhaling, the signal would be reduced as these movements cancel each other out
when measuring depth. This problem also occurs when the subject moves backward while
inhaling. A method in which the relative distance between the chest and the shoulders
are analyzed could solve this problem, because the shoulders move along with the subject
while they are independent of the movements caused by breathing. Such a method is
researched and presented in [6].
As mentioned before, the algorithm calculates the respiratory rate by extrapolating the
time between three found maximum distances. This means that when the algorithm fails
to locate a maximum distance, the error is significantly large. On the other hand, it only
takes another three breaths to determine a new respiratory rate, since the algorithm is
almost real-time. This problem could be partially solved by taking more measurements
and calculating a weighted average, this method however would give a statistical respiratory rate [6] instead of a almost real-time one.
There are no respiratory measurements done with other medical devices to use as a gold
standard. This means that a comparison with the actual respiratory rate in not possible.
Instead, only the depth signal can be used to confirm the respiratory rate detection.
It also has to be noticed that Microsoft does not state with which methods the body
and face recognition are achieved. As mentioned before, there are methods researched to
achieve this but it is not certain that Microsoft uses similar methods [5] [10].
All heart rate results are achieved by analyzing the measured data in LabVIEW 2015
and not in the actual algorithm. Although this could be implemented, due to a shortage
of time this method has been chosen instead. The measurements done with the M70 fingertip oximeter were filmed, which makes the average heart rate, that is used as a golden
standard, an estimate average of these measurements.
16
Table 1 shows the results of the heart rate detection and the results of the M70 fingertip pulse oximeter. Both measurements don’t exactly agree, but they are quite similar.
There are no errors on the detected heart rates, since these errors are difficult to determine, they depend on distance, viewing angle and edge noise [1]. We can therefore only
conclude that the measurements are similar.
7
Conclusion
As mentioned before, the SDK 2.0 from Microsoft allows new programmers, such as
myself, to investigating the possibilities of the Kinect v2. This research shows one of the
applications that the Kinect v2 is capable of. The signals measured with the Kinect v2
show a lot of detail, which is promising for further research of this device. The results show
that the respiratory rate could be determined, although the algorithm sometimes fails to
recognize some maximum distances. Since the algorithm determines the respiratory rate
almost in real-time, the error would be ignored after three breaths. The heart rate was
determined by analyzing the green channel color values in NI LabVIEW 2015. By applying
two bandpass filters and a fast Fourier transformation, a heart rate could be determined
which was similar to the golden standard measurements.
8
Acknowledgements
This bachelor thesis was a very exciting project for me because I started it without
any programming skills. I’m very grateful that my supervisor Maurice Aalders accepted
me and supported me throughout this challenge. I would also like to thank my second
supervisor Martin Brandt for helping me with solving all the bugs and problems that I’ve
encountered as a new programmer. He motivated and supported me every single time I
was stuck on a problem.
17
Appendices
A
Microsoft Kinect for Windows v2 system requirements
• Windows 8 (x64)
• 64 bit (x64) processor
• 4 GB Memory (or more)
• I7 3.1 GHz (or higher)
• Built-in USB 3.0 host controller (Intel or Renesas chipset)
• DX11 capable graphics adapter
The current price of the Microsoft Kinect for Windows v2 is e99,-
18
B
Respiratory rate detection code
r i c o c h e c k = ( z v a l u e s t o t a l a v e r a g e [ frame count − 1]
− z v a l u e s t o t a l a v e r a g e [ frame count − 2 ] ) ;
i f ( r i c o c h e c k > 0)
{
positive = positive + 1;
negative = 0;
}
i f ( r i c o c h e c k < 0)
{
negative = negative + 1;
positive = 0;
}
i f ( p o s i t i v e == 3 )
{
inhale = 0;
exhale = 1;
s t a r t i n d e x = frame count ;
}
i f ( n e g a t i v e == 3 && e x h a l e == 1 )
{
max value = 0 ;
f o r ( i n t i n d e x = s t a r t i n d e x ; i n d e x < f r a m e c o u n t ; i n d e x++)
{
i f ( z v a l u e s t o t a l a v e r a g e [ i n d e x ] > max value )
{
max value = z v a l u e s t o t a l a v e r a g e [ i n d e x ] ;
max index = i n d e x ;
}
}
max times . Add( t i j d 3 [ max index ] ) ;
exhale = 0;
inhale = 1;
}
19
C
Heart rate data analysis graphical code
This heart rate data analysis graphical code was made by Prof. Dr. M.C.G. Aalders.
20
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