ecti2013-respiration_rate_20130402_submission.pdf

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Estimation of Respiratory Rate from Smartphones

Acceleration DataThanakij Pechprasarn

1, Suporn Pongnumkul

2

National Electronics and Computer Technology Center

112 Thailand Science Park, Phahonyothin Road, Klong 1, Klong Luang, Pathumthani 12120, [email protected]@nectec.or.th

AbstractAbnormal respiratory rates have been shown to be an

important predictor of serious clinical illness, but respiratory

rate is a vital sign that is often not recorded because methods for

measuring respiratory rates are cumbersome. We propose an

approach to record and monitor respiratory rate of a patient that

is lying down by placing an accelerometer-equipped smartphone

on the patients chest. We develop an algorithm based on fast

Fourier transform (FFT) to estimate the respiratory rate from

the noisy acceleration data. The main contribution of this paper

is that our proposed algorithm can estimate respiratory rates

using only tri-axial acceleration data from sensor in commodity

smartphones without any other special equipment. Preliminary

results show that our method can reasonably estimate the

respiratory rate.

Keywordsrespiratory rate, accelerometer, time series, movingaverage, Fourier transform

I. INTRODUCTIONRespiratory rate is the number of breaths that a person takes

in one minute while at rest. In practice, medical practitioners

measure respiratory rate by counting how many times the

chest moves up and down within one minute, or within 30seconds and multiply the count by two. The method for

measuring respiratory rate is tedious and time-consuming;therefore, it is a vital sign that is often neglected [1]. However,

as respiratory rates may increase with fever, illness, or other

medical conditions, it is an important predictor of serious

clinical illnesses. The technologies for measuring respiratory

rates are still an active area of research [2].

Most smartphones nowadays offer various built-in sensors

and often include the tri-axial accelerometer, which measures

the acceleration in three orthogonal directions. An

accelerometer can be used to sense vibration, e.g. the vibration

of a machine, orientation, e.g. in human activity monitoring.The tri-axial accelerometer is used as an inclinometer to

reflect the abdomen or chest movement caused by respiration.

We aim at creating a tool that can be used by medical

practitioners and non-practitioners alike; therefore, we keep

the device and the measurement methods simple. The device

we use is an iPhone 4, a commodity smartphone that has tri-

axial accelerometers, and the measurement method is simplyplacing the smartphone on the patients chest for 30 seconds

while the patient is in a lying down position.

The algorithm we developed is based on the fast Fourier

transforms. The data is first pre-processed to reduce noiseusing smoothing and detrending. Then we perform FFT to

find the highest frequency. After that, we derived the

respiratory rate from the three axes of signals by choosing the

strongest frequency.

We conducted an experiment to verify our methods, where

we asked a healthy adult to lie down and had a generalpractitioner to measure the respiratory rate by counting the

chest movements for about 30 seconds. The subject was asked

to simulate fast breathing, normal breathing and slow

breathing. We compare the observed respiratory rate from the

general practitioners, the wave counts from the visual signalsand the answer from our algorithm and found that our method

can reasonably estimate the respiratory rate in all three cases.

Our main contribution is the algorithm for estimating

respiratory rates from just the acceleratory signals from

commodity smartphones.

The rest of this paper is organized as follows. Section II

reviews the related work. Section III describes our algorithm.Section IV describes the experiment including the setup and

the result. Then we conclude in Section V.

II. RELATED WORKRespiratory rate is one of a vital sign that can be used to

monitor the wellness of an individual [3-6]. It helps detect any

malfunction of breathing activities, especially during sleeping.

Respiratory rate is normally estimated from a recorded

waveform. There are 2 main categories of waveform

recording methods. In first category, a waveform of

respiratory efforts is directly recorded. For example,impedance pneumography (IP) measures a respiratory activity

through differential changes in capacitance. Respiratoryinductive plethysmography (RIP) uses stretch sensors on the

chest wall whereas flow thermography utilizes the changes in

temperature of air flow when breathing. These methods can be

viewed as a mainstream for the direct category. IP is the mostwidespread method used in a hospital whereas RIP is the most

common method for overnight monitoring [4]. Another class,

also classified to be in a direct category but much less

common, includes the use of an accelerometer, a laser-based

device, ultrasound, and audio and/or video processing. On the

other hand, it is also possible to record a respiratory waveform

978-1-4799-0545-4/13/$31.00 2013 IEEE


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in an indirect fashion. However, this category would involve

an extra step to rebuild and extract a respiratory waveformfrom other related waveforms. For instance, a respiratory

waveform can be derived from electrocardiogram (ECG),

photoplethysmogram (PPG), arterial blood pressure (ABP)

and the peripheral arterial tonometry (PAT). Derivation of

respiratory rate from ECG is an area that has been studied

extensively compared to the others in the category. Moreover,

recently researchers tend to include various signal sourcessimultaneously in order to improve a final estimate of

respiratory rate. For example, a novelty proposed by Nemati

et al. [7] is also based on data fusion which includes ECG,

PAT, IP and PPG.

Due to an emergence of microelectromechanical systems

(MEMS)-based accelerometer [8], there is more recent

published work that utilizes an accelerometer to estimate

respiratory rate. Primitive reasons for the usage of an

accelerometer, compared to traditional methods, are that it is a

cheaper, non-invasive approach, and also viable to be used

outside hospitals without a supervision of a professional. An

accelerometer can be attached to a different part of the body,for example, a chest/thorax [3,8], a thorax-abdomen wall

(including diaphragm muscle and lower costal margin) [4,5,9],

an abdomen/waist [8,10] and even suprasternal notch [6,8].

Yet, currently there is no consensus on the best placement of

the sensor. In our work, we decide to place a sensor on a

patients chest. Besides placement, the posture of a person is

reported to greatly affect the conducted breathing activity [5].

Thus, many require a patient to sit or lie down steadily to be

able to successfully extract respiratory rate [3-6,8-9]. On the

other hand, A novelty from Liu et al. [10] has studied theeffects of posture changes like walking and running and still

be able to compute respiratory rate out of that particular

activities. Nevertheless, activity detection is not our mainfocus of this paper so we collect data only when a patient lies

down steadily. It is proposed by previous publication [3] thatbreathing frequency is ranging between 0-1 Hz. Based on this

knowledge, it becomes very common to employ a band-pass

or low-pass filter like Butterworth allowing only a certain

range of frequency to persist [3-6,8-11]. In addition, some

groups improve on Butterworth filter by involving an adaptive

computation of the cut off frequencies [3,10]. After applying

the filter, the signal becomes cleaner. Our proposed method

includes an original technique that can also be used for the

purpose of cleansing the noisy data. Next, generally, a high

dimensional accelerometer like a tri-axial accelerometer

would be used. Therefore, many [4,5] have proposed a way toderive 1D respiratory signal out of a 2D/3D accelerometer.

The process can be a manual selection of one dimension to be

a representative of the signal. Bates et al. [5] suggest that we

can compute an angular rate of breathing motions to deal with

the problem of axis fusion. Some researchers prefer that a

method based on principal component analysis (PCA) shouldbe used to reduce the dimensionality [4,10]. In this paper,

because we want to focus on our proposed algorithm, we

employ a manual selection for simplification; however, our

view is that a method like either angular rate derivation or

PCA can cooperate with our algorithm and would help

improve a signal quality before operating. After cleaning,according to Bates [5], there are 3 respiratory derivation

methods including peak findings (counting) [6], threshold

crossing [5,11] and spectral analysis (Fourier analysis or

autoregression) [3-4,10]. Our work involves the use of Fourier

transformation to calculate the respiratory rate.

It would be useful to state that our approach makes use ofonly a smartphone without any other special hardware so our

approach fully takes an advantage of mobility provided by the

smartphone. In addition, in general the capability of a built-in

accelerometer equipped with a smartphone cannot be

compared with a dedicated high quality (i.e. sensitivity)

accelerometer being used in other publications. To the best of

our knowledge, we are the first who reports the work that

utilizes only a smartphones accelerometer. Nevertheless, we

found related work by Ono et al. [9] which also makes use of

an accelerometer of an iPod touch, but with some extra

hardware. In addition, their approach seems to be far obviated

from the common knowledge of the community and their

result is shown to have limited success.

III.OURMETHODThere are three main phases in our approach. The first step

can be viewed as a pre-processing step as it is mainly for

cleaning up the noisy input sensory data, a time series from anaccelerometer. For the second step, the smoothed signal in a

time domain will be transformed into a frequency domain

using a Fourier transform algorithm. After that, the algorithm

will continue to estimate a value of respiratory rate (RP) based

on the power spectrum given by the output of the Fourier

transformation.

A.Pre-Processing StepIn our first pre-processing part, there are two subtasks: 1)

smoothing and 2) detrending.

Firstly, for the smoothing task, our primary intention is to

decrease any obscure or less important features found in the

input and retain dominant characteristics or important features

of the signal. We employ a technique called, moving average

(MA), for this task. Theoretically, a time series can bedecomposed additively into

tttt ISTy . (1)

Where Tt = a trend component, St = a seasonal component

and It = an irregular component. Then, a trend component is

extracted using MA and the signal becomes

tt Ty . (2)

We employ 13-term moving average for all experiments

ranging from slow to fast breathing rate. We find that we donot have to alter a number of terms used in our experiments as

it still can successfully estimate a trend from each data set.


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After the noisy signal has been smoothed, we then proceed

to the second subtask which is to detrend the signal. In orderto have a clearer view of this particular step, we define the

input for this step to be yt*, which would be the result of (2),

i.e.yt* =yt. We repeat the MA technique again as we find that

it has served us well for this detrending purpose. A trend

component has been extracted, but this time is from yt*.

Finally, to remove a trend, we proceed as follows

***

ttt Tyy . (3)

At first it may be not obvious why this second subtask is

required. The reason is because occasionally we find a trendeither upwards or downwards in our certain data sets and this

trend can collapse the outcome of Fourier transform in the

next step. Therefore, the detrending process has been involved

for eliminating the situation.

B. Spectral AnalysisWe make use of a fast Fourier transform (FFT) algorithm to

transform the data given in a time domain and convert it into a

frequency domain. The output of FFT or power spectrum iscomposed of a series of frequency components associated

with their powers. After that, the fundamental frequency, or a

respiratory frequency, can then be revealed in the output

spectrum.

Now it is a good time to discuss about another reason for

the detrending subtask in the previous step. It is for ensuring

that the input signal has an average value of zero before

transforming it with Fouriers algorithm. We find that this step

becomes essential in our work because an input signal without

an average value of zero will yield perplexing output because

the result, frequency components, are not only from a desired

frequency but also from a constant factor silently resided inthe signal. Moreover, in our case, we find that these two tend

to be mixing together as we are calculating respiratory rate;

the targeted frequency could fall in a very small range (i.e.

between 0-1 Hz) and typical values would be as close as zero

like 0.2 Hz which has RP of 12 breaths per minute (BPM).With these low frequency components, it can be easily

obfuscated with zero and almost zero frequencies from a

constant term hidden in the signal due to its non-zero average.

This would yield higher complexity than necessary for further

processing. Hence, detrending is applied in order to obtain a

meaningful output and be able to do further analysis.

C.Derivation of Respiratory RateIn our last step, we try to derive the final respiratory rate

from the power spectrum generated from the previous step.

Before we begin, all the frequencies with very low powers

will be filtered out using a certain value of a threshold. The

value of the threshold is selected in a heuristic fashion after

conducting several experiments. By setting a threshold, we

find that the algorithm performs better in differentiating a

control case like bed and table from typical respiratory cases.

To compute the respiratory rate, firstly, as we knew that the

respiratory frequency would be in a range of 0-1 Hz, so onlythe frequencies within this range are preserved. After that,

there are still many peaks in the spectrum with different

heights (power); however, only the highest peak is considered

to be our fundamental frequency. Next, after we extract the

dominant peak, we compute an estimate of the respiratory rate

based on the breathing frequency of the selected peak.

IV.EXPERIMENT AND RESULTSA.Experimental Setup

We conducted an experiment where we recruited a healthyadult as a subject for measuring respiratory rate. The subject

was asked to lie down with an iPhone 4 placed on her chest.

We collected three sets of data where she was asked vary her

breathing ratebreathe normally, breathe fast, and breathe

slowly. Each data has 30 seconds of data. The accelerometer

is set to log sensory data with a sampling frequency of 10 Hz.

To get a baseline, we also asked a general practitioner to

observe the chest movements and report the respiratory rate of

each of these data sets. Lastly, we also include placements ofa smartphone on a bed and a table for the purpose of being a

control case.

B.Data CollectedThe baseline collected from the countings by a general

practitioner reported that the subjects breathings are 12, 18and 30 BPMs for slow, normal and fast breathing respectively.

The result is consistent with reports for normal breathings in

the literatures. The range of normal breathing rate of an adult

slightly differs from source to source. For instance, [12]

pointed out that 12-16 BPM is normal. On the other hand, 12-

20 BPM becomes normal according to [13]. These counting

reports from the practitioner are for having a general sense ofthe actual rate of the data set. Although the operation is

conducted by a professional, we agree that the counting could

introduce some kind of human errors (which would include

rounding errors.)

Fig. 1 displays a plot of a time series of the sensory data.

According to the figure, the chart is noisy; nevertheless, it

clearly exhibits a sine-wave pattern. This is a positive sign for

us as this suggests that our acceleration data may correspond

to the breathing and would potentially infer the respiratory

rate of a subject. We proceed by counting the number of

waves in the signal for each data set and derive an estimate of

respiratory rate based on this counting. We call the estimatefrom this method as WCRP. We find that the number falls

within about the same range of an estimate from the chest-

movement counting (will be referred to as HCRP) done by a

professional. Further detail is reported in Table I. This finding

would support our argument in using an accelerometer to

estimate the respiratory rate.


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TABLEICOMPARISON OF HUMAN COUNT AND WAVE COUNT

Human Count

(HCRP)

Wave Count

(WCRP)

12 12

18 18

30 40

Fig. 1 A trend found in RP30

In addition to the noisiness of the data, there are other

incurring problems. For example, the signal can also contain

either an upward or downward trend as appeared in Fig. 1.

Moreover, Fig. 2 indicates that the breathing may not be done

at a constant rate for a whole period of time. All of these

would contribute into the more difficulty of the problem.

Fig. 2 A signal with different breathing frequencies

To cope with the noisy data, we employ a smoothing

technique. Next, a detrending process has been used for

removing a trend from the signal. We use 13-term moving

average to handle the situation in both cases. Details of the

smoothing and detrending techniques are given in part III. An

example of results after smoothing is shown in Fig. 3.

Fig. 3 RP18 after smoothing

Furthermore, as we knew that the subject may not breathe

with the same frequency all the time. The difference of

breathing rates in the same signal would actually reconcile

provided that the practitioner still find that the condition forHCRP is met. Then, to show the effectiveness of our

algorithm, we carefully choose a specific range within a signalthat can be a good representative of that particular data set.

The criterion is that the WCRP from this shorter selected

range should be similar to the WCRP shown in Table I. After

this arrangement, we then can show that our proposed

algorithm can estimate the value of respiratory rate as close as

WCRP using the same period.

For instance, in a case of fast breathing set RP30, we have

WCRP of 40, which results in a frequency of 0.67 Hz (a

period of 1.5 second.) In our trials, we find that we need at

least 3 waves in order to obtain a reasonable result out of thisdata set using FFT. Therefore, we look for a range of 4.5

seconds that can fully cover 3 waves. For RP12 and RP18, wecalculate the required number of seconds in the same fashion

and find that they are 15 and 10 seconds, respectively.

Illustrations of this selection are given in Fig. 1 for RP30 and

in Fig. 3 for RP18 with a pair of red lines indicating a selected

range.

C.ResultsNext, we executed our algorithm on the selected portion of

the signal. The final result, an estimate of respiratory rate from

our method, ALRP, is shown in Table II.

TABLEII

RESULTS OF OURPROPOSED ALGORITHM

Data Set Respiratory Rate (BPM)

WCRP ALRP

RP12 12 11.72

RP18 18 18.75

RP30 40 37.50

bed N/A 0.00

table N/A 0.00


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Fig. 4 A power spectrum of RP18

We also display an example of a power spectrum of RP18

after the FFT step in Fig. 4. This intermediate output is used

for determining a breathing frequency.

Lastly, we plot our estimates in a real-time manner to

reveal any inconsistency in breathing of the subject. Inaddition, this plot would make a clearer view that ouralgorithm is not restricted to only specifically selected range.

We select the range just to establish a solid ground for our

experiment that our algorithm can give a similar result to

WCRP. An example of a real-time plot is displayed in Fig. 5.

The plot is updated at every 2 second using last 15, 10 and 4.5

seconds for RP12, RP18 and RP30, respectively.

5 10 15 20 250

20

40

Resp

iratoryRate

Real-time Respiratory Rate

5 10 15 20 250

20

40

RespiratoryRate

5 10 15 20 250

20

40

R

espiratoryRate

Time (sec)

Fig. 5 A real-time plot of RP12, RP18 and RP30 respectively

V. CONCLUSIONSThis paper proposes a method and an algorithm for

measuring and monitoring humans respiratory rates using the

accelerometer data from smartphones. The device setup is

designed to be simpleusing only a commodity smartphone

with no other devices. An algorithm that we developed, based

on fast Fourier transform shows promising results in the

experiment we conducted. This approach can be easily

deployed and assist in measuring and monitoring respiratory

rate by medical practitioners and normal users alike.

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