2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

Design and Implementation of Monitoring System

for Breathing and Heart Rate Pattern using WiFi
Signals
Sangyoun Lee, Young-Deok Park, Young-Joo Suh Seokseong Jeon
Department of Computer Science & Engineering WCU Division of ITCE
Pohang University of Science and Technology (POSTECH) Pohang University of Science and Technology (POSTECH)
Pohang, South Korea Pohang, South Korea
{e36net, ydpark, yjsuh}@postech.ac.kr s.jeon@postech.ac.kr

Abstract—Breathing pattern and heart rate can be major Breathing and heart rate of a person have often been mea-
indicators of a person’s physical condition, and an easy way sured by attaching special-purpose sensors as part of medical
to measure the vital signs can be useful in health monitoring. In treatment [7]. However, this method must be used in a limited
this paper, we propose a new method for identifying the changes
in breathing and heart rate pattern of a person using commercial space and has a disadvantage that sensors should be attached
WiFi devices. The amplitude of signal waves can represent the directly to the body. Due to recent advances in smartphones
periodic up-and-down chest movements caused by breathing and and wearable devices, we can use those devices to measure
heartbeat, and prominent changes of the signal pattern can be breathing and heart rate anywhere and anytime. However, this
detected by using the Dynamic Time Warping algorithm. We approach is also inconvenient because health apps often have
verified the feasibility of the proposed method in real testbeds
and evaluated the method through various experiments with 10 high measurement errors and wearable devices must be used
participants. The proposed method achieves 94% accuracy in in contact with the body. To overcome these inconveniences,
identifying a subjects physical status. This low-cost method will several approaches that use RF signals have been suggested;
be useful for monitoring our health in everyday life. Doppler radar [8], UWB radar [9], Frequency Modulated
Index Terms—OFDM; CSI; DTW Algorithm; Breathing Mon- Continuous Wave (FMCW) radar [10], and Received Signal
itoring; Heart rate Monitoring Strength (RSS) of WiFi [11]. However, these approaches are
costly due to additional RF equipment, and the measurements
I. I NTRODUCTION may not reliable.
To solve the problems of existing methods, this paper
Recently, we have witnessed the wide spread of Internet of proposes a new low-cost breathing and heart rate measurement
Things (IoT) and smart-homes technologies. As the number method that does not require sensors to the body without
of IoT devices increases and expectations for smart-homes depending on the place and time. Specifically, we suggest
grow, the number of IT services related to human health is a method to recognize and distinguish a person’s breathing
increasing [1]. Especially, smartphones and wearable devices and heart rate pattern changes by using the Channel State
are expanding the scope to measure vital signs [2][3]. Information (CSI) of WiFi. We also show that this method can
Breathing and heart rate can be major indicators of a identify the person’s physical status. CSI accurately represents
person’s physical condition. For example, when a person the channel situation. We use OFDM subcarriers as multiple
enters a sleep state, breathing and heart rate patterns may be sensors to detect the physical changes of a person only with
changed. In a non-REM sleep state, the intensity and frequency the WiFi signals by analyzing the CSI waveforms of minute
of breathing and heart rate are generally lowered and kept movements of the body due to the breathing and heartbeat. In
relatively stable. However, in a REM sleep state, they are addition, the proposed method does not need modifications on
strengthened again and the fluctuation becomes severe [4]. the AP side.
In addition, the breathing and the heart rate patterns change The remainder of this paper is organized as follows. We
significantly when emotional changes are occurred or heavy explain the background in Section II. In Section III, the
exercises are performed. The breathing frequency of a normal proposed method is described. We evaluate the proposed
adult is 12 to 20 times per min in a resting state [5], and method in Section IV. Then we summarize related research
the heart rate is 60 to 100 beats per min [6]. By observing the in Section V. Finally, we conclude this paper in Section VI.
patterns of a person’s breathing and heart rate, we can compare II. BACKGROUND
the measured pattern with previously collected patterns to
A. OFDM Subcarriers of IEEE 802.11 n/ac and Channel State
determine the man’s physical condition. The information can
Information
be fed to smart-home services via IoT devices.

978-1-5386-4790-5/18/$31.00 ©2018 IEEE

 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

Fig. 1: IEEE 802.11 OFDM subcarrier

Fig. 3: CSI amplitude change caused by breathing for 30 subcarriers (top)

and for specific one subcarrier (bottom)

B. Multipath Effect
In a typical indoor environment, signals propagate through
a single line-of-sight (LOS) and multiple non-line-of-sight
(NLOS) paths (Fig. 2). As the signal waves propagate in the
channel, they construct and destruct several multipath signals
due to reflection, diffraction, and scattering caused by walls
and surrounding objects. The subtle movements caused by
breathing and heartbeat change existing multipaths and create
Fig. 2: Multipath effect new multipaths. These effects are captured in the CSI, and
can be used to distinguish breathing and heart rate patterns.
Current WiFi standards such as 802.11 n/ac use Orthogonal Measuring CSI for a person resting without great motion
Frequency Division Modulation (OFDM) technology for their shows that the progress of breathing and the resulting change
physical layer. In OFDM, the channel is partitioned into 64 in CSI amplitude tend to be similar. For example, Fig. 3 shows
subcarriers and used 52 out of them (Fig. 1); 48 are data changes in CSI amplitude for 20 s, where each subcarrier
subcarriers, 4 are pilot subcarriers and no data at the center represents a change in amplitude of a sinusoidal waveform
and edges. Then detailed Channel State Information (CSI) of of 4 s-cycle similar to the flow of breath.
each subcarrier is provided. This structure has the advantage
of frequency selective attenuation caused by multipaths. III. P ROPOSED S YSTEM
The CSI represents the Channel Frequency Response (CFR) A. System Overview
for each subcarrier between transmit-receive antenna pairs.
The values of CSI include signal strength and phase infor- The process for identifying changes in the pattern of breath-
mation for OFDM subcarriers. The received signal in the ing and heart rate consists of four major parts as shown in Fig.
frequency domain can be modeled as 4. In the first part, the CSI data is collected from the WiFi
receiver. Then, the noise included in the data is removed, and
Y = H · X + N, (1) only the waveform of the human breathing and heart rate is
extracted. Next, a subcarrier that reflects the fine motion of
where Y and X are the received and transmitted vectors, the body is selected, and then the waveform of the subcarrier
respectively, H is the complex number channel matrix CSI is normalized and aligned. Finally, the current breathing and
and N is the noise vector. CSI of a single subcarrier can be heart rate status can be determined by comparing with the
expressed as waveform of the known patterns. The detailed process of each
H = |H|ej2πθ , (2) part will be described as follows.

where |H| is the amplitude and θ is the phase of each B. Multipath Mitigation
subcarrier. The presence and movement of a person between Multipath mitigation removes the signal components that
the transmitting and receiving antennas affects the propagation have long delays. The transmitted signal that experiences
path of the radio signal, which is reflected in the CSI values multipaths has a delayed signal component according to the
of all subcarriers. characteristics of each path. Generally, reflected or refracted
 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

Fig. 6: Breathing waveform extracted by the Butterworth filter

Fig. 4: System overview

Fig. 7: Example of filtered CSI amplitude pattern at 30 subcarriers (top) and

corresponding variance (bottom)

Fig. 5: Power delay profile with N-point IFFT, N=60 C. Filtering

The CSI data from which the delayed signal components
signal components in an NLOS situation are more likely to be were removed include information on body movements not
delayed than signal components delivered directly in a LOS related to human breathing and heartbeat, and high frequency
situation [12]. If a subject changes sleep posture in the bed, noise caused by the internal state changes of the WiFi device.
or someone is walking around the bed, the wireless signal Therefore, it is not recommended to use the CSI as it is. To
will be reflected due to the environment changes. Therefore, compare breathing and heart rate patterns, unnecessary data
the reflected signal creates new multipaths, and long-delayed and noise should be removed for all subcarriers, and only
multipaths distort the CSI. Accordingly, we should remove the data related to human breathing and heart rate should be
these reflected signal components that have long delays to extracted by a band-pass filter. Since humans generally have
obtain accurate information related to the chest movements breathing from 0.2 Hz to 0.33 Hz [5] and heartbeat cycles
caused by breathing and heartbeat. By performing Inverse from 1 Hz to 1.33 Hz [6], we used a Butterworth filter, which
Fast Fourier Transformation (IFFT) on the collected CSI to features a flat passband and a gentle transition band. For 12-20
approximate the time-domain power delay profile, we could breaths/s with a sampling frequency of 10 samples/s, only cut-
2π×f
empirically remove multipath components. The Power delay off frequency ωc = Fs = 2π×0.3310 ≈ 0.21 rad/s was applied
profile gives the distribution of the signal power received to extract periodic movements due to breathing and heartbeat.
through a multipath channel as a function of time delay. Fig. 6 shows breathing waveform extracted by the Butterworth
After we got the power delay profile with N-point IFFT for a filter.
person in rest that has normal-state breathing and heart rate,
we removed the signal components in the dashed circle on D. Subcarrier Selection
the right side of Fig. 5 to mitigate the effect of the changed Frequency diversity is a characteristic that the degree of
multipaths. Then, we can convert the remaining power delay attenuation varies with frequency. Due to this characteristic,
profile back to the frequency domain by applying the Fast each subcarrier has different sensitivity to movements caused
Fourier Transformation (FFT). by breathing and heartbeat. However, if all subcarriers are
 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

Fig. 9: Example of filtered CSI amplitude pattern at 30 subcarriers (top) and

corresponding variance (bottom)

Fig. 8: Example of filtered CSI amplitude pattern at 30 subcarriers (top) and

corresponding variance (bottom)

used, the computational cost is high. In addition, neighboring

subcarriers tend to have similar CSI value, and thus it is
effective to select only few subcarriers that closely exhibit Fig. 10: Example of filtered CSI amplitude pattern at 30 subcarriers (top)
and corresponding variance (bottom)
changes in CSI amplitude according to the changes in breath-
ing and heart rate. Thus, a subcarrier-selection mechanism
each point in the first waveform to one or more points in the
is necessary. We calculated the variance of CSI amplitude
second waveform, and then determines the minimum distance
in a moving time window to quantify the sensitivity of the
of the warping path between them. The output of DTW is
subcarriers to the movement. Subcarriers with high variance
the distance between two waveforms. A short distance and a
are more sensitive to subtle movements than subcarriers with
nearly-straight line of warping path indicate that the patterns
low variance as shown in Fig. 7. To match the breathing
of the two waveforms are similar (Fig. 10). However, if
and heart rate pattern, we chose subcarriers with the greatest
the warping path is severely distorted or the distance value
variance of CSI amplitude in a time window.
becomes large, it cannot be considered that the two signals are
E. Segmentation similar. Especially, if the slope of the warping path is less than
1, it is considered to be slower than the reference breathing and
It is important to align the starting point before comparing
heart rate. If it is larger than 1, it can be considered faster than
the waveforms of the current breathing and heart rate patterns
the reference breathing and heart rate. In conclusion, the point
with the waveforms of known patterns. For example, if the
at which the slope of the warping path or the warping distance
signal waveform for the known breathing pattern starts from
change becomes larger is the moment when the pattern of
inhalation and the signal waveform for the current breathing
breathing and heart rate changes.
starts from exhalation, the comparative result will not be the
same even if their patterns are similar. We divided the total IV. E VALUATION
input signal into multiple time-slice windows. Then, we used A. Experimental Method
a threshold-based peak detection technique to align the start
point of the two signal waveforms. We empirically determined We used two MinnowBoards [14], one as a transmitter
the threshold value. After normalization (Fig. 8), the highest (Access Point) and the other as a receiver (WiFi device).
point of the first peak which is equal to or greater than the The MinnowBoard has an Intel 5300 NIC and runs Ubuntu
threshold value was used as the starting point for the windows 14.04. The transmitter as an access point transmit packets
comparison as shown in Fig. 9. The waveforms from the heart to the receiver device at 10 packets/s in the 5 GHz band.
rate are also processed in the same manner. Thus, the CSI values are captured by the WiFi driver at every
beacon interval. In addition, we used Intel 5300 NIC and Linux
F. Pattern Matching 802.11n CSI Toolkit [15] to extract CSI for 30 subcarrier-
After obtaining an aligned signal segment, we compared the groups of the 20 MHz WiFi channel.
input signal to the reference signal of normal breathing and We placed a transmitter and a receiver on each side of a
heart rate. A Dynamic Time Warping (DTW) algorithm [13] bed in a large room. While the subject was lying on the bed
was used to determine the similarity of patterns for the two and changing his/her breathing pattern, we obtained CSI data
signal waveforms that were aligned. DTW can match non- of the moment from the receiver’s internal logs. We used ten
linear waveforms that may have different lengths, by mini- participants to see how our approach differentiates each change
mizing the distance between the waveforms. DTW matches in breathing and heart rate pattern.
 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

(a) CSI amplitude change for breathing rate=0.2 Hz (period=2 s)

Fig. 13: Result of the experiment on breathing and heart rate pattern
recognition

(b) CSI amplitude change for breathing rate=0.5 Hz (period=5 s) TABLE I: C ONFUSION MATRIX OF THE EXPERIMENT TO RECOGNIZE
BREATHING AND HEART RATE PATTERN CHANGES

(c) Result of DTW on the two waveforms

Fig. 11: DTW result of different waveform patterns

waveforms in a moving time window to monitor changes in

the breathing pattern. For every different pattern of breathing
waveforms (Fig. 11b), the DTW shows a distorted warping
path (Fig. 11c), not a straight line. The result distance value
of the DTW was also higher in the section where the breathing
and heart rate pattern was changed than in the section where
(a) Total breathing signal for 2 min the breathing and heart rate pattern was similar (Fig. 12).
C. Evaluation with Various Subjects
We performed the same experiment twice with ten subjects
and obtained 20 CSI data to determine if the pattern matched
the intended pattern (Fig. 13). It can be seen that each pattern
is recognized with a high probability in the remaining window
sections except for the seventh and tenth window sections in
which the breathing patterns are changed.
We achieved 94% accuracy in distinguishing breathing pat-
(b) DTW distance change for breathing pattern comparison tern changes over 20 experiments and a comparison of breath-
Fig. 12: DTW results according to breathing pattern changes, window ing patterns in 220 window sections (Table I). Especially, the
size=100 number of times that normal breathing (Positive) is recognized
as normal breathing is 139 times and the number of times
B. The Fisibility to Distinguish Breathing and Heart Rate that different breathing (Negative) is recognized as different
Pattern Changes breathing is 68 times, and it shows a high recognition rate.
We obtained several CSI data from one subject and evalu- When distinguishing the heart rate pattern, it shows about 90%
ated whether this method can discriminate the breathing and accuracy, which is relatively lower than the case of breathing
heart rate pattern change well. We let the subject breathe pattern. We presume the reason is that the heartbeat signal is
naturally for 1 min, then let him/her change the breathing more affected by the surrounding environment than breathing
heavily or slowly for 30 s, then let the subject breathe naturally since heartbeat is a faster and more subtle movement than
again for 30 s. We compared the first 20 s of the natural breathing. We will investigate more accurate measurement
breathing waveform (Fig. 11a) with the rest of the breathing methods to optimize our proposed method in future work.
 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

Fig. 14: Result of the physical status identification experiment Fig. 15: The experiment results using the packet rates of 5, 10 and 20
packets per second.
TABLE II: C ONFUSION MATRIX OF THE EXPERIMENT TO IDENTIFY THE
SUBJECT ’ S PHYSICAL STATUS

Fig. 16: The experiment results in the distance of 2, 4 and 6 m.

D. Physical Status Identification
We used four representative breathing and heart rate pattern
such as sleeping, resting, walking, and running to check the
physical status of the subject. We conducted the experiment
with the ten subjects and let them lay down and breathe slowly
and deeply for 1 min similar to sleeping. Then, we let them
sit and rest for 1 min, walk for 1 min, and run for 1 min.
A comparison result of the breathing patterns across all 1472
time-sliced windows showed 94% accuracy in identifying the
subjects physical status (Fig. 14; Table II). When using the
heart rate pattern, the accuracy was about 82%. As with the
previous experimental result, the accuracy of some window
sections is low due to changes of the subjects posture and Fig. 17: The experiment results in LOS and NLOS environment.
activity.
F. Distance between AP and WiFi Device
The distance between the transmitter and the receiver also
E. Packet Transmission Rate affects the overall accuracy. We performed the experiments
As the number of CSI data per unit time increases, the again at distances of 2, 4 and 6 m, but there was no significant
information about the fine movement caused by breathing difference in the accuracy of the physical status identification
also increase. Thus, we decided to compare the subjects using breathing pattern matching. However, we found that
physical status identification results according to the packet using heart rate pattern matching decreased the accuracy as
transmission rate. We changed the packet rates to 5, 10, and the distance increased (Fig. 16). Especially, when the distance
20 per second and repeated the experiment. We tested five exceeds 4 m, the accuracy drops sharply. The heart rate pattern
subjects for each transmission rate and then averaged the matching method is not suitable for identifying the subjects
accuracy and repeated this experiment several times (Fig. 15). physical status and may be used as an aid to the breathing
The result is the most accurate at a transfer rate of 20, as pattern matching method.
expected, but close to 88% accuracy at a transfer rate of 5. G. LOS vs. NLOS
Therefore, a transmission rate of 10 packets/s is appropriate,
We conducted the experiment again in the NLOS environ-
just like the WiFi beacon interval.
 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

ment to verify its usefulness in real-life. We installed a changes of the signal pattern mean that the person’s breathing
partition such as a chair, a desk, and a whiteboard to separate and heart rate pattern is also changing.
the transmitter and the receiver. As expected, the experimental We compared the person’s breathing and heart rate pattern
results in the NLOS environment are less accurate than those with pre-determined patterns using Dynamic Time Warping
in the LOS environment (Fig. 17). However, the difference is algorithm, and then identified the person’s physical status. We
not large and is accurate to more than 93%. If the transmitter evaluated the proposed method in 20 experiments with 10
and the receiver were installed in separate rooms, we could participants, and we got the achieved accuracy of 94%. We
not get meaningful results due to missing packets, which is a hope the proposed low-cost method be useful for monitoring
future work to solve. heath in our daily lives.
ACKNOWLEDGEMENT
V. R ELATED W ORK
This research was supported by NRF grant funded by the Korea govern-
ment (MSIP) (2015R1A2A1A15055311). This research was supported by the
Human breathing and heart rate measurement techniques Fire Fighting Safety & 119 Rescue Technology Research and Development
are mainly divided into sensor-based methods and RF signal- Program funded by the Ministry of Public Safety and Security(”MPSS-
based methods. Sensor-based methods include high-cost meth- FirefightingSafety-2015-78”)
ods such as Polysomnography (PSG) [7], which is used for R EFERENCES
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