Remote Respiration Rate Determination in Video Data

Vital Parameter Extraction based on Optical Flow

and Principal Component Analysis

Christian Wiede, Julia Richter, Manu Manuel and Gangolf Hirtz

Department of Electrical Engineering and Information Technology,

Chemnitz University of Technology, Reichenhainer Str. 70, 09126 Chemnitz, Germany

Keywords:

Remote Respiration Rate Determination, Vital Parameters, Optical Flow, Principal Component Analysis.

Abstract:

Due to the steadily ageing society, the determination of vital parameters, such as the respiration rate, has come

into focus of research in recent years. The respiration rate is an essential parameter to monitor a person’s

health status. This study presents a robust method to remotely determine a person’s respiration rate with an

RGB camera. In our approach, we detected four subregions on a person’s chest, tracked features over time with

optical ﬂow, applied a principal component analysis (PCA) and several frequency determination techniques.

Furthermore, this method was evaluated in various recorded scenarios. Overall, the results show that this

method is applicable in the ﬁeld Ambient Assisted Living (AAL).

1 INTRODUCTION

Europe, in particular Germany, is facing the problem

of a steadily ageing society. According to the national

German statistical agency, 29 % of the population will

be older than 65 years in 2030 (Statistisches Bunde-

samt, 2015). Moreover, there will be a lack of medical

personnel to care for the elderly. This development

goes hand in hand with a higher demand for technical

assistance systems, which can support elderly people

in their self-determined living.

Current technical assistance systems can either

detect emergencies such as falls (Wohlrab et al.,

2015) or monitor the daily activities of elderly peo-

ple (Meinel et al., 2015). Heretofore, these systems

cannot monitor a person’s current health status, which

is one essential requirement to stay longer in the own

ﬂat and living in a self-determined way at the same

time. This issue could be solved by detecting and

tracking vital parameters. These vital parameters,

such as heart rate, respiration rate and oxygen sat-

uration, can be measured by means of RGB cam-

eras. In this work, we focus on determining a per-

son’s respiration rate accurately and robustly. For this,

we propose an algorithm that detects multiple fea-

ture points in a region of interest (ROI) at the chest

and tracks these points with a Kanade-Lucas-Tomasi

(KLT) point tracker (Tomasi and Kanade, 1991). This

step is followed by a temporal ﬁltering, a PCA and

a frequency determination. Moreover, we proved the

reliability for various scenarios in our experiments.

The presented approach has the crucial advantage

that it works contact-less. This will be more comfort-

able for future users, because they are not required to

wear additional devices. Moreover, this approach al-

lows the determination of the respiration rate at home

in the ﬁrst place. The respiration rate as vital parame-

ter can provide information about the general current

health status and miscellaneous abnormal respiration

patterns, which can be indicators for pulmonary dis-

eases. For example, it is possible to detect a sudden

breathlessness and inform an emergency doctor im-

mediately, who can react earlier than nowadays. This

can save lives and prevent restrictions induced by sec-

ondary diseases, which could then be preventable as

well. Besides the ﬁeld of AAL, there exist further

possible applications, such as the prevention of sud-

den infant death syndrome at neonatals at an early

stage, sleep monitoring and the tracking of a driver’s

well being.

This study is structured as follows: In Section 2, a

survey about the existing literature is conducted. This

is followed by Section 3, where the proposed method

is described in detail. Based on this, an experimen-

tal study for multiple scenarios is performed in Sec-

tion 4. This is accompanied by a detailed discussion.

Finally, the ﬁndings are condensed and future work is

outlined.

326

Wiede C., Richter J., Manuel M. and Hirtz G.

Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component Analysis.

DOI: 10.5220/0006095003260333

In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pages 326-333

ISBN: 978-989-758-225-7

 2017 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

2 RELATED WORK

The determination of vital parameters is a relevant

ﬁeld in medicine, which has been studied for cen-

turies. Beside the heart rate, the respiration rate in-

dicates a person’s individual health state and is there-

fore suited to assess the personal well-being. Further-

more, a pathologically varied respiration rate, which

occurs during breathlessness or hyperventilation, can

be a symptom for multiple diseases.

In the clinical environment, there are multiple pos-

sibilities to measure the respiration rate, for exam-

ple respiratory effort belts, nasal thermistors, pres-

sure transducers or by taking the modulated signal

of the electrocardiography (ECG). All these estab-

lished methods have in common the disadvantage that

they need direct body contact. These measurement

methods cause a large discomfort for elderly people.

Therefore, optical methods can contribute to a higher

comfort due to their contact-less working mode.

Fei and Pavlidis applied a face and nostrils recog-

nition on thermal images and used a wavelet analysis

to determine the thermal change at the nostrils during

inhalation and exhalation (Jin Fei and Pavlidis, 2010).

Other researchers have chosen the Kinect device for

respiration rate detection to monitor the movement of

the chest during the breathing. Martinetz et al. tracked

the Kinect infra-red pattern over time, applied a PCA

and auto regressive (AR) methods to determine the

frequency (Martinez and Stiefelhagen, 2012). In con-

trast to that, Lim et al. used a moving average ﬁlter

and a spline interpolation on the depth points (Lim

et al., 2014). Ostadabbas et al. extended this idea by

automatically selecting an ROI on the chest and by

measuring depth changes (Ostadabbas et al., 2015).

While all these approaches used different wave-

lengths in the infra-red spectrum, we want to focus

on the visible light spectrum, i. e. by using RGB cam-

eras. In general, the lifting and lowering of the torso

and the abdomen could be observed in all existing

approaches that determined the respiration rate. Tan

et al. proposed to measure the motion by subtract-

ing two consecutive frames and detecting edges (Tan

et al., 2010). In comparison to that, Bartula et al.

created 1D-proﬁles of a person and correlated these

1D-vectors of adjacent frames to determine the res-

piration rate (Bartula et al., 2013). An alternative ap-

proach consist of the detection of intensity changes on

the skin and the application of an independent compo-

nent analysis (ICA) to determine the heart rate (Poh

et al., 2011). The respiration rate can be determined

by a modulation of the heart rate signal. Tarassenko

et al. expanded this concept by using AR models for

the frequency determination (Tarassenko et al., 2014).

In 2015, Sharma et al. used the Eulerian Video Mag-

niﬁcation presented by Wu et al. (Wu et al., 2012) to

determine the respiration rate by means of intensity

changes (Sharma et al., 2015).

Another large group of approaches are tracking

based and optical ﬂow based methods. The ba-

sics were investigated by Nakajima et al. (Nakajima

et al., 2001) and Frigola et al. (Frigola et al., 2002),

who used the methods of Horn-Schunck (Horn and

Schunck, 1981) or (Pentland and Horowitz, 1991) re-

spectively. Based on this, Lukac et al. use a KLT

tracker (Tomasi and Kanade, 1991) for the optical

ﬂow (Lukac et al., 2014). This principle was im-

proved by Koolen et al., who applied a PCA, an ICA

and a Short Time Fourier Transform (STFT) for sig-

nal analysis (Koolen et al., 2015). In the application

ﬁeld of remote heart rate determination, Balakrishnan

et al. suggested to ﬁrstly ﬁnd features in the face and

then use a point tracker to extract the trajectories (Bal-

akrishnan, 2014). Subsequently, they applied a PCA

and determined the heart rate. Li et al. adapted this

approach and used it to detect points on the chest for

respiration rate determination (Li et al., 2014).

The extant literature revealed that it is possible to

extract the human respiration rate with an RGB cam-

era remotely. However, there exist different solutions

to reach this aim. We combined several of such so-

lutions to design an accurate and robust system for

remote respiration rate detection. Furthermore, we

evaluated the proposed algorithm under various con-

ditions.

3 METHODS

3.1 Overview

In the following section, the implemented methods to

remotely obtain the respiration rate are presented. The

principal steps are visualised in Figure 1. In the ﬁrst

place, images were acquired from an RGB camera.

This was followed by an ROI selection on the chest, a

feature detection in this ROI and the tracking of these

features. The trajectories of the tracked points were

extracted and bandpass ﬁltered. Afterwards, a PCA, a

channel selection and a frequency determination were

performed. This chain outputs the ﬁnal respiration

rate.

3.2 ROI Selection

For the proposed approach, it is necessary to observe

the chest or a part of the chest to monitor the lifting

and the lowering of the torso, which is induced by the

Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component

Analysis

327

Respiration Rate

Remote Respiration Rate

Determination Method

Image Acquisition

ROI Selection

Feature Tracking

Bandpass Filtering

and PCA

Frequency

Determination

Figure 1: Block diagram of the proposed remote respiration

rate determination algorithm.

breathing. There are two possibilities to obtain a sta-

ble region of interest on the chest. On the one hand,

there is the face detection. By concluding from the

position of the face bounding box, the chest bounding

box could be estimated. On the other hand, there is

the the upper body detection. In this case, the chest

bounding box was selected as a part within the upper

body bounding box. Both possibilities use a Viola-

Jones detector (Viola and Jones, 2004). While the

face detector is more accurate, the upper body detec-

tor can be applied if the face is not visible, e. g. when

the person is not facing the cameras.

In the ﬁrst place, we applied the face detector. The

ROI was placed centrally below the face bounding

box by 125 % of the face bounding box height. The

height of this ROI is 60 % of the face bounding box

height and its width is 120 % of the bounding box

width.

If no face could be detected, the upper body de-

tector was applied. The corresponding bounding box

height is 20 % of the upper body bounding box height

and its width is 36 % to obtain an ROI of the chest.

The center is shifted in vertical direction centrally by

65 % of the height of the upper body bounding box.

Finally, the ROI was split in four equally sized

quarters, see Figure 2. The advantage of splitting the

ROI is a higher robustness of the further processing

steps. Noise that effects only one part of the ROIs

could be reduced or eliminated in this way.

Figure 2: Proband with the chest bounding box consisting

of four subregions and the tracked points.

3.3 Feature Tracking

Before the tracking could be applied, a preliminary

feature detection was necessary to identify unique im-

age parts. For this purpose, we used minimum eigen-

value feature (Shi and Tomasi, 1993) in each of the

four subregions. Possible corner points were identi-

ﬁed by a minimum eigenvalue metric. In Figure 2,

the detected features are shown. In order to limit the

number of features, only the ﬁfteen strongest points

per region were selected.

Based on this, we applied a so-called KLT tracker

(Tomasi and Kanade, 1991), which is the base for the

optical ﬂow as well. According to the brightness as-

sumption, the intensity of a pixel I(x, y, t

) at the time

will remain stable over short time durations and

small movements. For a single time step dt, the fol-

lowing equation is valid:

I(x, y, t

) = I(x + dx, y + dy, t

+ dt) (1)

dx and dy denote the small displacements in x and

y direction. Obviously, this equation with two un-

known variables cannot be solved. To overcome this

issue, Tomasi and Kanade suggested to use the neigh-

bouring pixels as well, since they perform all the same

trajectory for a small movement. A 3×3 patch results

in nine equations for two unknown variables. This

over-determined equation system can be solved by a

least square ﬁtting method.

As a result, the trajectories of the n feature points

in one subregion were observed in x and y direction:

(t); x

(t) (2a)

(t); x

(t) (2b)

(t); x

(t) (2c)

(t); x

(t) (2d)

VISAPP 2017 - International Conference on Computer Vision Theory and Applications

328

In the following processing, only the y coordinates

were considered, because the principal component of

the chest motion during the breathing is the y direc-

tion. The mean value Y

avg

of all feature points in y di-

rection can be considered as the motion of the whole

subregion, see Equation 3.

avg

(t) =

∑

(t) (3a)

avg

(t) =

∑

(t) (3b)

avg

(t) =

∑

(t) (3c)

avg

(t) =

∑

(t) (3d)

If no feature points were found in one subregion,

the ﬁnal signal was substituted by the subregion with

the highest number of features. This procedure guar-

antees a robust processing even if there are no feature

points in one subregion.

100

150

200

−1

−0.5

0.5

Time in s

Vertical shift in pixels

Trajectory from one subregion

Figure 3: Raw signal of the trajectory of one subregion.

3.4 Bandpass Filtering and PCA

In order to exclude implausible frequencies, which

do not lie in the physiological range of the human

respiration rate, a bandpass ﬁlter BP was applied to

all four subregions. An FIR ﬁlter with 128 ﬁlter co-

efﬁcients was selected to guarantee a constant group

delay. The designed ﬁlter allows all frequencies

between 0.125 Hz (7.5 BPM) and 0.7 Hz (42 BPM)

to pass. This range complies with the natural human

respiration rate. Y

denotes the ﬁltered signal, see

Equation 4.

(t) = BP(t) ∗Y

avg

(t) (4a)

(t) = BP(t) ∗Y

avg

(t) (4b)

(t) = BP(t) ∗Y

avg

(t) (4c)

(t) = BP(t) ∗Y

avg

(t) (4d)

100

150

200

−0.5

0.5

Time in s

Vertical shift in pixels

Filtered vertical trajectory

Figure 4: Filtered signal of the trajectory of one subregion.

These ﬁltered signals contain the information of

all vertical motions in the assigned frequency range.

However, there is still motion present in these signals,

which cannot be referred to as breathing. In order

to split the bandpass ﬁltered signals in the respiration

rate signal and noise signals, we applied a PCA, ac-

cording to Equation 5. This method detects the prin-

cipal components in a new orthogonal coordinate sys-

tem.

PC(t) = PCA(Y

(t); ...;Y

(t)) (5)

In this equation, PC denotes one principal compo-

nent. In total, there are four resulting principal com-

ponents. The ﬁrst principal component is shown in

Figure 5. For the subsequent frequency determina-

tion, the principal component with the highest spec-

trum density was selected. In our experiments, the

principal component with the highest spectrum den-

sity was always the ﬁrst component.

3.5 Frequency Determination

In this study, we implemented three different meth-

ods for frequency determination: the FFT, the spec-

tral estimator described by Welch and a peak counting

method. A sliding window of 30 seconds was used for

all three methods.

For a speciﬁc time t

, the three methods can be

Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component

Analysis

329

100

150

200

−2

−1

Time in s

Magnitude in pixels

PC 1

Figure 5: First principal component.

deﬁned as:

FFT

) = max(

FFT(PC)

) (6a)

Welch

) = max(

Welch(PC)

) (6b)

Peaks

) =

peaks

+30s

∑

peak

−t

peak−1

, (6c)

whereas F

FFT

, F

Welch

and F

Peaks

are the determined

respiration rates.

In Figure 6, these respiration rates are displayed

over time.

4 RESULTS AND DISCUSSION

4.1 Setting

In order to test the proposed algorithm under vari-

ous conditions, we created a database of 35 differ-

ent videos in total. These videos were recorded us-

ing an RGB camera, i. e. a Basler acA640-100gc with

a resolution of 658 × 492 pixel. The automatic ex-

posure time control and the automatic white balanc-

ing were disabled in order to avoid inﬂuences on our

measurements. We recorded the videos with a ﬁxed

frame rate of 30 frames per second. Each video has

a duration of approximately three minutes. In total,

ﬁve probands took part in our experiments. For the

records, no other light sources than daylight or nor-

mal ceiling light were used.

For the evaluation, we considered the distance

from the camera, the brightness of the image, the in-

ﬂuence of different clothing and the orientation of the

probands. The considered scenarios are summarised

in Table 1. These scenarios will be discussed in the

following sections in detail. The scenario with a dis-

tant of three metres from the camera, good lighting

100

150

29.5

30.5

Time in s

Breaths/Minute

Frequency determination using Welch

100

150

29.5

30.5

Time in s

Breaths/Minute

Frequency determination using FFT

100

150

Time in s

Breaths/Minute

Frequency determination using peak detection

Figure 6: Outputs of the three frequency determination

methods: F

FFT

, F

Welch

and F

Peaks

. In this case, the deter-

mined respiration rate is approximately 30 breaths per sec-

ond.

conditions and with a textured shirt was deﬁned as

ideal.

Table 1: Evaluated scenarios in our experiments.

Evaluation criterion Scenario

Distance 1 m

3 m

5 m

Illumination Good

Insufﬁcient

Clothing inﬂuence Textured shirt

Plain shirt

Extra jacket

View Back view

For evaluation, we used a set of pre-deﬁned res-

piration rates as ground truth data. For this, the

probands performed guided breathing patterns. That

means that they had to breath in a certain ﬁxed breath-

ing frequency to approximately reach the deﬁned

ground truth value. Deﬁned ground truth values were

30 BPM, 25 BPM, 20 BPM, 15 BPM and 7.5 BPM.

This results in a maximal possible error resolution

of 3.75 BPM. The breathing patterns were randomly

chosen for different probands and sequences. In or-

VISAPP 2017 - International Conference on Computer Vision Theory and Applications

330

der to compare the determined respiration rate with

the ground truth, we assigned this determined respira-

tion rate to the nearest value of the deﬁned breathing

pattern values. Then, we evaluated whether this as-

signed value is equal to the ground truth value of the

sequence or not.

4.2 Distance from Camera

One essential criterion for a robust algorithm is the

invariance against scaling. The respiration rate de-

termination should not be depended on the distance

between the camera and a person. For evaluation,

three different distances to the camera were chosen,

i. e. 1 m, 3 m and 5 m. Five videos were recorded for

each scenario, which results in quantisation steps of

20 % for ﬁve probands. All three frequency determi-

nation measurement methods were applied.

Table 2: Classiﬁcation rates for the distance scenarios.

Distance FFT Welch Peak

1 m 100 % 100 % 100 %

3 m 100 % 100 % 100 %

5 m 100 % 100 % 80 %

As shown in Table 2, the distance does not sub-

stantially inﬂuence the proposed algorithm. The al-

gorithm fails to determine the correct respiration rate

only for one proband at a distance of ﬁve metres for

the peak detection. This person’s chest was partially

covered by long hair falling from the head. Nonethe-

less, the respiration rate for the other videos could be

successfully determined for this person. Hence, it can

be concluded that the algorithm is scale-invariant as

long as there are still features available.

4.3 Illumination

The lighting condition in a room can change very

quickly, for example when ceiling lights are switched

on or off or the incoming sunlight changes its inten-

sity due to clouds. In these scenarios, the proposed al-

gorithm should continuously track the features with-

out loosing too many features. In our ﬁrst setting,

we recorded the ﬁve probands under perfect lighting

conditions. Then, in the second setting, the ceiling

lights were switched off, so that the illumination and

the contrast were low in this setting.

Table 3: Classiﬁcation rates for the illumination scenarios.

Illumination FFT Welch Peak

Good 100 % 100 % 100 %

Insufﬁcient 100 % 100 % 100 %

The proposed method managed both good illu-

mination conditions and insufﬁcient illumination, see

Table 3. The respiration rate was detected in all

videos and with all probands for this scenario. It can

be concluded that the illumination does not have a

substantial effect on the algorithm output.

4.4 Inﬂuence of Clothing

Another major inﬂuence factor consists in the type of

clothing that is worn. Since the proposed method is

feature-based, the texture of the shirt plays an impor-

tant role. In the standard case, a shirt with patterns

(texture) was used. In contrast to that, in the second

case, the probands wore a blank shirt with a homoge-

neous appearance. We expected that less or no fea-

tures would be found in the subregions. In a third

case, the probands wore an additional jacket. Due to

the fact that there is partially air between the shirt and

the jacket, the breathing patterns might not be trans-

ferred to the jacket.

Table 4: Classiﬁcation rates for the clothing scenarios.

Clothing FFT Welch Peak

Textured shirt 100 % 100 % 100 %

Plain shirt 100 % 100 % 100 %

Extra jacket 100 % 100 % 100 %

In all three scenarios, the correct respiration rates

were estimated for all probands, see Table 4. Despite

the decreased number of features in the four subre-

gions for the blank shirt, a sufﬁcient number of fea-

tures was still detectable. These features appeared

especially at the collar or buttons of the shirt. Ad-

ditionally, it can be observed that the respiration rate

could be determined even when the probands wore a

jacket. That means that the transfer of the breathing

motion was still large enough to accurately determine

the respiration rate.

4.5 Back View

In real scenarios, it is not guaranteed that a person is

always looking frontal at the camera. For that reason,

all probands’ back views were recorded for one sce-

nario.

Table 5: Classiﬁcation rates for the back view.

View FFT Welch Peak

Back view 100 % 100 % 80 %

As shown in Table 5, the peak detection algorithm

failed to detect the correct respiration rate for only

one proband. This, again, can be explained with the

Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component

Analysis

331

proband’s long hair, which covered the back of the up-

per body. The FFT and the Welch estimation method

were still able to predict the correct respiration rate.

4.6 Overall Comparison

Overall, 35 videos were considered for the testing. In

general, in all scenarios the respiration rate could be

classiﬁed correctly, see Table 6. While the peak de-

tection method was able to detect the correct respi-

ration rate only in 94.4 % of the cases, the FFT and

the Welch spectral estimation worked perfectly for all

scenarios. Therefore, it is recommended to use one of

those two methods to determine the frequency for the

respiration rate.

Table 6: Overall classiﬁcation rates.

Method FFT Welch Peak

Overall 100 % 100 % 94.4 %

5 CONCLUSION AND FUTURE

WORK

In this study, we developed a new method for remote

respiration rate determination, which is based on four

ROIs, an optical ﬂow based tracking, a PCA and a fre-

quency determination. Furthermore, an intense eval-

uation of different environmental parameters and sce-

narios was conducted. The results show that the pre-

sented method worked robustly in all scenarios. The

best frequency determination methods were the FFT

and the Welch spectral estimation. The results reveal

that it is possible to use such a respiration rate esti-

mation system in a domestic environment for AAL.

Further studies have to evaluate whether the accuracy

is sufﬁcient for clinical use.

For future work, the inﬂuence of motion has to

be evaluated. If the respiration rate and the superim-

posed motion signal could be separated, this method

could be used as well in the ﬁeld of e-rehabilitation

or in professional sports. Moreover, we intend to de-

velop a real-time working system to detect the respi-

ration rate immediately.

ACKNOWLEDGEMENTS

This project is funded by the European Social Fund

(ESF). We furthermore would like to express our

thanks to all the persons who contributed to this

project with their recordings.

REFERENCES

Balakrishnan, G. (2014). Analyzing pulse from head mo-

tions in video. PhD thesis, Massachusetts Institute of

Technology.

Bartula, M., Tigges, T., and Muehlsteff, J. (2013). Camera-

based system for contactless monitoring of respira-

tion. In Engineering in Medicine and Biology Society

(EMBC), 2013 35th Annual International Conference

of the IEEE, pages 2672–2675. IEEE.

Frigola, M., Amat, J., and Pags, J. (2002). Vision based

respiratory monitoring system. In Proceedings of the

10th Mediterranean Conference on Control and Au-

tomationMED2002 Lisbon, Portugal.

Horn, B. K. and Schunck, B. G. (1981). Determining optical

ﬂow. Artiﬁcial intelligence, 17(1-3):185–203.

Jin Fei and Pavlidis, I. (2010). Thermistor at a Distance:

Unobtrusive Measurement of Breathing. IEEE Trans-

actions on Biomedical Engineering, 57(4):988–998.

Koolen, N., Decroupet, O., Dereymaeker, A., Jansen, K.,

Vervisch, J., Matic, V., Vanrumste, B., Naulaers, G.,

Van Huffel, S., and De Vos, M. (2015). Automated

Respiration Detection from Neonatal Video Data:. In

Proceedings of the International Conference on Pat-

tern Recognition Applications and Methods, pages

164–169. SCITEPRESS - Science and and Technol-

ogy Publications.

Li, M. H., Yadollahi, A., and Taati, B. (2014). A non-

contact vision-based system for respiratory rate esti-

mation. In Engineering in Medicine and Biology So-

ciety (EMBC), 2014 36th Annual International Con-

ference of the IEEE, pages 2119–2122. IEEE.

Lim, S. H., Golkar, E., Rahni, A., and Ashrani, A. (2014).

Respiratory motion tracking using the kinect camera.

In Biomedical Engineering and Sciences (IECBES),

2014 IEEE Conference on, pages 797–800. IEEE.

Lukac, T., Pucik, J., and Chrenko, L. (2014). Contactless

recognition of respiration phases using web camera.

In Radioelektronika (RADIOELEKTRONIKA), 2014

24th International Conference, pages 1–4. IEEE.

Martinez, M. and Stiefelhagen, R. (2012). Breath rate mon-

itoring during sleep using near-ir imagery and pca. In

Pattern Recognition (ICPR), 2012 21st International

Conference on, pages 3472–3475. IEEE.

Meinel, L., Richter, J., Schmidt, R., Findeisen, M., and

Hirtz, G. (2015). Opdemiva: An integrated assis-

tance and information system for elderly with demen-

tia. In Consumer Electronics (ICCE), 2015 IEEE In-

ternational Conference on, pages 76–77.

Nakajima, K., Matsumoto, Y., and Tamura, T. (2001).

Development of real-time image sequence analysis

for evaluating posture change and respiratory rate

of a subject in bed. Physiological Measurement,

22(3):N21.

Ostadabbas, S., Sebkhi, N., Zhang, M., Rahim, S., Ander-

son, L. J., Lee, F. E.-H., and Ghovanloo, M. (2015). A

Vision-Based Respiration Monitoring System for Pas-

sive Airway Resistance Estimation. IEEE Transac-

tions on Biomedical Engineering, pages 1–1.

VISAPP 2017 - International Conference on Computer Vision Theory and Applications

332

Pentland, A. and Horowitz, B. (1991). Recovery of nonrigid

motion and structure. IEEE Transactions on Pattern

Analysis and Machine Intelligence, 13(7):730–742.

Poh, M.-Z., McDuff, D., and Picard, R. (2011). Advance-

ments in Noncontact, Multiparameter Physiological

Measurements Using a Webcam. Biomedical Engi-

neering, IEEE Transactions on, 58(1):7–11.

Sharma, S., Bhattacharyya, S., Mukherjee, J., Purkait, P. K.,

Biswas, A., and Deb, A. K. (2015). Automated detec-

tion of newborn sleep apnea using video monitoring

system. In Advances in Pattern Recognition (ICAPR),

2015 Eighth International Conference on, pages 1–6.

IEEE.

Shi, J. and Tomasi, C. (1993). Good Features to Track.

Technical report, Cornell University, Ithaca, NY,

USA.

Statistisches Bundesamt (2015). 13. koordinierte

Bev

oelkerungsvorausberechnung f

ur Deutschland.

https://www.destatis.de/bevoelkerungspyramide/.

[Online; accessed 07-Septemper-2016].

Tan, K. S., Saatchi, R., Elphick, H., and Burke, D. (2010).

Real-time vision based respiration monitoring sys-

tem. In Communication Systems Networks and Digital

Signal Processing (CSNDSP), 2010 7th International

Symposium on, pages 770–774. IEEE.

Tarassenko, L., Villarroel, M., Guazzi, A., Jorge, J., Clifton,

D. A., and Pugh, C. (2014). Non-contact video-

based vital sign monitoring using ambient light and

auto-regressive models. Physiological Measurement,

35(5):807–831.

Tomasi, C. and Kanade, T. (1991). Detection and Tracking

of Point Features. Technical report, Carnegie Mellon

University.

Viola, P. and Jones, M. J. (2004). Robust real-time face

detection. International Journal of Computer Vision,

57(2):137–154.

Wohlrab, D., Heß, M., Apitzsch, A., Langklotz, M.,

Schwarzenberger, A., Bilda, S., Schulz, H., Hirtz,

G., and Mehner, J. (2015). Hom-e-call - an en-

hanced fall detection system based on accelerometer

and optical sensors applicable in domestic environ-

ment. In Jaffray, D. A., editor, World Congress on

Medical Physics and Biomedical Engineering, June 7-

12, 2015, Toronto, Canada, volume 51 of IFMBE Pro-

ceedings, pages 1461–1464. Springer International

Publishing.

Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F.,

and Freeman, W. T. (2012). Eulerian Video Magni-

ﬁcation for Revealing Subtle Changes in the World.

ACM Trans. Graph. (Proceedings SIGGRAPH 2012),

31(4).

Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component

Analysis

333