RESEARCH

1. Wireless Vital Signs Monitoring System
using Visible Light Sensing (VLS)
Hisham Abuella and Sabit Ekin
School of Electrical and Computer Engineering,
Oklahoma State University, Stillwater, OK 74078.
Abstract—In this paper, we present a wireless system for
monitoring human vital signs like breathing and heartbeat via
visible light sensing (VLS). Typical techniques for tracking heath-
condition require body contact and most of these techniques are
intrusive in nature. Body contact might irritate the patient’s
skin and he/she might feel uncomfortable while sensors are
touching their body. However, in this method, we can estimate
the breathing and heartbeat rates without any body contact
using a photo-detector. Vitals monitoring using VLS make use
of the idea that reflected light signal off the human body
received at the photo-detector will be affected by the chest
motion during heartbeats and breathing. We implemented the
system using off-the-shelf photo-detector and a signal acquisition
system and obtained the results for different people and in
different scenarios. We found out that the accuracy of our system
compared to FDA approved equipment to measure heartbeats
and breathing rate is 94%. This system can be used in various
domains and applications in medical facilities and in residential
homes.
A provisional patent (US#62/639,524) has been obtained for
this work.
Index Terms—Visible Light Sensing, Health Vitals Monitoring,
Heart rate, Respiration rate, Visible Light Health applications,
Smart Health systems.
I. INTRODUCTION
Advances in ubiquitous sensing technologies have led to
intelligent systems that can wirelessly monitor our vital signs
such as respiration (breathing) and heart rates [1]–[5]. Vital
signs monitoring is a very important and critical for physi-
ological monitoring and health assessment of a patients [6].
Accurately measuring a patient’s vital signs is important as
it gives an indication of the patient’s physiological state.
Conventional techniques for tracking body condition require
body contact, and most of these techniques are intrusive.
Body contact might irritate the patients skin and can be very
inconvenient due to the wires and lack of mobility, particularly
for babies. In addition, patients might feel uncomfortable (e.g.,
anxious, nervous, and excited) when sensors/wires are placed
on their bodies. Such a negative experience can bias the
performance of respiration and heart rate measurements, and
hence, might mislead the patient and/or healthcare provider.
Consequently, this has prompted the need for effective non-
intrusive sensing methods that can wirelessly monitor the vital
signs without perturbing the state of patients.
The two well-known state-of-the-art (SOA) wireless vi-
tals monitoring methods are based on radio-frequency (RF)
(radar) [2], [3], [5], [7]–[9] and imaging (camera) [4], [10]–
[15]. However, these efforts are in their infancy and can present
safety (RF signal penetration through the body) and privacy
(camera) concerns. They require significant enhancements to
improve their functionalities, and to be adopted in commercial
products. Therefore, there is an urgent need to develop im-
proved, safe and reliable sensing methods for vitals monitoring
without raising any privacy issues.
Fig. 1. Basic idea of the VLS based Vitals monitoring system.
This study is proposes a novel wireless vital monitoring
system using visible light signals, a technology which we term
visible light sensing for vital monitoring (VLS)1
. Our proposed
method takes measurements for breathing (respiration) and
heartbeat rates without any body-contact electronic device.
The proposed system consists of a photo-detector sensor,
which measures the power level of the reflected visible light
signal from the patients body. In addition, an embedded pro-
cessing system is used for the signal processing and filtering
of the received light signal. In contrast, the RF-based method
uses the RF signal and physical movement of the body, while
imaging-based method uses the ambient light (visible light)
and the skin absorption level of the light. The VLS method
takes advantage of the visible light and physical movement
of the body. Clearly, the safety of visible light has already
been proven as it is used everywhere in illumination and the
complexity of signal processing algorithms required for one-
dimensional visible light data will be computationally lower
than that required for two-dimensional data processing in
imaging-based systems.
1The patent is pending: S. Ekin, H. Abuella, C. G. Teague, H. Idrees,
M. Uysal, and M. E.Oztemel, ”System and method of non-contact vitals
monitoring using visible light sensing”, US Patent App. 62/639,524.
arXiv:1807.05408v1[eess.SP]14Jul2018

2. Historically, it was believed that the small variations in
the received signal (in both RF- and imaging-based wireless
methods) were because of noise and undesirable (and un-
predictable) reflections and scattering from the environment.
However, as the technology in sensing and signal processing
improved, researchers have discovered that some of these
variations are actually from humans physiological movements
(heartbeats and respiration) and that they were able to extract
this information (RF-based in [2], [3], [7], [9], [16]–[19],
Imaging-based in [4], [12]–[14], [20]–[23]). Given these stud-
ies, we investigated if it is possible to extract the physiological
data from visible light signal variations and to monitor the
vital signs remotely. We introduce the idea of using visible
light sensing (VLS) in wireless vital signs monitoring. Visible
light (VL) is in the frequency range between 350-750 THz in
the electromagnetic spectrum. However, VL is still not utilized
by applications other than illumination. The proposed system
utilizes VL for vital signs estimation purposes and does not
interfere with other applications, unlike RF-based techniques.
Since light sources (LEDs) are available everywhere and
power efficient, our proposed method does not need to have a
dedicated transmitter (available light source can be used as the
transmitter). The VLS is a cost-effective novel approach and
is not yet fully explored in research. Nonetheless, in [24], the
authors suggested using VLS in several applications such as
Human-Computer Interaction (gesture recognition) and indoor
localization.
The main contributions of this study are as follows:
• A VLS-based detection and ranging system using visible
light as done in camera-based systems but using the idea
of reflected signal from the body as done in RF-based
systems as presented in Figure 1.
• Implementation of the system and testing it on different
targets.
• Comparing the system accuracy with the SOA contact-
based vitals monitoring systems such as pulse oximeter.
Related work
Since the 1980s, researchers have had the desire for wire-
less RF-based vitals monitoring methods and have presented
successful prototypes to measure the human vitals using RF
signals. Most of the studies adopted the basic idea of RADAR
systems by detecting the phase difference of the reflected
signal. Radar uses the reflected (echo) signal to determine the
direction and distance of the reflecting object [25], [26].In case
of the radar vitals monitoring system, an unmodulated signal is
transmitted toward a human body, where it is phase-modulated
by the physiological movement of the human body depending
on the distance variations. This distance varies slightly and pe-
riodically with the breathing and heart beats [5], [7], [9]. One
of the key challenges that impact reliability in using wireless
RF signals is the extraction of vital signs due to the fact that
the RF environment is very dynamic and any motion in the
environment affects the signal. Another challenge encountered
in radar-based vital signs detection is the presence of undesired
harmonic terms and inter-modulations other than the sinusoids
of interest [3].
An initial proof of concept using electromagnetic (EM) RF
signals (radar) was successfully carried out by the pioneers
in this field [2], [9]. In [2], Chen et al. developed a sensitive
microwave life-detection which can be used to find human
subjects buried earthquake rubble or hidden behind various
barriers has been constructed which is similar to the study
presented in [16], [27], [28]. Moreover, Fadel et al. presented
a system that can track breathing and heart rates of multiple
humans in a room without having to exist in the same room.
They predict that their system will be part of future smart
homes residents well-being [7]. In [5], Li et al. introduced a
low cost and low power baby monitor prototype that utilize
unlicensed band (5.8GHz) to detect respiration of babies in
their cribs. In [25], Obeid et al. proposed a new system
for contact-less heartbeat detection which uses 2.4, 5.8, 10,
16, and 60 GHz bands. They focused on determining the
minimum transmitted power to be able to detect the heart
beats from the received signal. In [29], Gu et al. they presented
contactless Doppler Radar Respiration Measurement for Gated
Lung Cancer Radiotherapy and showed that thier system
was able to supply reliable breathing motions and accurate
gating signals for radiotherapy of mobile tumors. Churkin and
Anishchenko presented a new type of sensor for vital signs
monitoring that utilizes mmWave radar (carrier wavelength is
about 3 mm), Hence, they achieved significant noise immunity,
sensitivity and accuracy [30] and also mmWave radar was
discussed in [31]. Tracking Respiration at Different Sleeping
Positions with off-the-shelf WiFi devices by collecting the
fine-grained wireless channel state information (CSI) around a
person was discussed by Lui et al. [32] and similarly in [33].
Another technology which have been widely used in non
contact vitals monitoring is the camera (visual) based sys-
tems. This type can be divided into two systems, firstly,
photoplethysmography (PPG), a volumetric measurement of
an organ, is obtained by illuminating the skin with a dedicated
light source and measuring changes in light absorption [34]. A
conventional contact-based pulse oximeter uses PPG to sense
valuable information about the cardiovascular system, such
as heart rate, arterial blood oxygen saturation, blood pres-
sure, cardiac output and autonomic function pulse wave [10],
[12]. Recently, following the same principles, advances in
image processing allowed researchers to develop a camera-
based PPG (wireless) vital signs monitoring system [11], [12],
[21]–[23], [35], which uses digital camcorders/cameras with
ambient light as the illumination source. Secondly, image
processing and motion detection measurements using normal
cameras [13], [14], [22].
In [20], Jorge et al. developed a novel method for the
extraction of respiration from camera-based measurements
taken from the top-view of an incubator for critically-ill or
premature infants, similar system was presented by Aarts et
al. in [10]. Moreover in [4], Tarassenko et al. have been able to
obtain estimates of heart rate and respiratory rate and prelim-
inary results on changes in oxygen saturation from double-

3. monitored patients undergoing haemodialysis in the Oxford
Kidney Unit. In [12], [23], Poh et al. presented a simple, low-
cost method for measuring multiple physiological parameters
using a basic webcam. By applying independent component
analysis on the color channels in video recordings, they
extracted the blood volume pulse from the facial regions. Heart
rate (HR), respiratory rate, and HR variability (HRV, an index
for cardiac autonomic activity) were subsequently quantified
and compared to corresponding measurements using Food
and Drug Administration-approved sensors. In [22], Bartula
et al. proposed a camera-based monitoring system to reliably
measure respiration rate without any body contact using low-
cost cameras. In [13], Smith et al. developed a new method for
non-contact breathing measurement, employing photometric
stereo to capture the surface topography of thetorso of an
unconstrained subject. The surface is integrated to calculate
time-dependent volume changes during respiration. In [14],
Takano and Ohta developed non-contact and non-invasive
device, which could measure both the respiratory and pulse
rate simultaneously. The time-lapse image of a part of the
subjects skin was consecutively captured, and the changes in
the average image brightness of the region of interest (ROI)
were measured for 30 s. Data was processed and filtered and
finally using AR spectral analysis two peaks for respiratory
and pulse rate were detected. Another method to measure
human cardiac pulse at a distance is through an infrared
imaging system. Statistical modeling of dynamic thermal data
captured. It works by capturing the information contained in
the thermal signal emitted from major superficial vessels [15],
[36], [37].
However, camera-based vital sign monitoring is challenging
for people with darker skin color [10]. In [11], Kumar et al.
developed a new camera-based vital sign estimation algorithm
which addresses these challenges. DistancePPG proposes a
new method of combining skin-color change signals from
different tracked regions of the face using a weighted aver-
age. Image processing algorithms require high computational
power, which makes camera-based methods power-inefficient
and inappropriate for power-limited systems. Finally, yet
equally important, camera-based systems possess great privacy
and security issues. In the era of internet, cyber security
and privacy are of the highest importance, particularly for
patients [38], [39]. Accordingly, there exists a need to develop
a wireless vital sensing method that does not incorporate any
privacy issues and requires low computational power.
The rest of the paper is organized as follows. First, we
present the system model in Section II. The problem formula-
tion and algorithms used to estimate heart and breathing rate
are discussed in Section III. Following that, the implemen-
tation setup and results are presented in Section IV, while
conclusions and future directions are presented in the last
section.
II. SYSTEM MODEL
To illustrate how the received signal power (amplitude)
varies with the vital signs, let us consider the example from
our experimental setup, where the user sits facing the VLS
system (Figure 1). When the person inhales, his chest expands
and gets closer to the device; and when he exhales, his chest
contracts and gets farther away from the device. As mentioned,
because the signal power and the distance to a reflector are
proportionally related, the VLS system can track a persons
breathing and heartbeats.
Our envisioned VLS-based vitals monitoring system is
depicted in Figure 2. The system mainly consists of 1)
photo-detector as receiver, 2) light source as transmitter, and
3) digital signal processing (DSP) unit and display, which
includes the ADC unit. This initial setup was developed for
proof-of-concept purposes with low cost units. In addition,
due to hardware limitations, we have tested our system in a
controlled setting, i.e., big body movements were avoided.
Where there is two positions, First one is when the human
body is inhaling while the second position is when the human
is exhaling. The ∆d shown in the figure cause a difference
in the received power at the photo-detector. So for the case
when the human is exhaling air the distance covered by the
light wave is (dexhale), while in case of inhaling the distance
dinhale is less by (∆d).
Fig. 2. System Model where the vitals of human body is monitored using
VLS.
A. Theory of Operation
Visible Light Communication (VLC) has been studied in-
tensively in the context of indoor communications [24], [40],
one of the most used and adopted channel models for the
line-of-sight (LOS) channel model is Lambertian model [41].
We will adopt the Lambertian model for the received signal
power-distance relation as follow:
Pr(t) =
(n + 1)ARPt
2π[d(t)]γ
cosn
(φ) cos(θ), ∀θ < φ1/2, (1)
where Pt is the transmitter power and AR is the optical
detector size. φ and θ are irradiance and incidence angles,
respectively. In addition, φ1/2 is the semi-angle at half-power

4. of the LED (which is half of the FOV of the light source),
and n is the order of the Lambertian model and is given by
n = −
ln(2)
ln(cos φ1/2)
. (2)
In our case the light source and the receiver are static and at
same height, therefore we can have
θ = φ, (3)
where 0 < θ < φ1/2. Using (3), (1) can be further simplified
as
Pr(t) =
(n + 1)AR
2π[d(t)]γ
cosn+1
(θ). (4)
Finally, in order to derive Pr(t) in terms of d(t), we further
simplify (4) by defining a constant K as:
K =
(n + 1)AR cosn+1
(θ)
2π
. (5)
The received power model can be further simplified as:
Pr(t) = K[d(t)]−γ
. (6)
where d(t) is the distance between the transmitter and the
receiver. The path-loss exponent γ depends on the environment
conditions, such as reflectiveness of materials, humidity, etc.
Typically, the range of path-loss exponent lies between 1
and 5. Notice that for any values of K and γ, the received
power changes with the distance, as expected. The longer
the distance between the transmitter (light source, e.g. LED)
and receiver (photo-detector), the lower the received power is.
Most importantly, variations in the distance (d(t)) with certain
periodicity result in similar variations in the received power
with the same periodicity. In other words, the above equation
shows that one can identify variations in d(t) due to breathing
(inhaling and exhaling) and heartbeats, by measuring the
resulting variations in the amplitude (power) of the received
(reflected) signal. Such relation between the received signal
power and distance serves as the foundation of our proposed
system. Consequently, the periodicity of variations in the
received power are due to small physical movements of human
body (chest). These small movements are used to extract the
rates for breathing and heartbeats.
III. PROBLEM FORMULATION
The basic principle of our proposed system is that an
unmodulated visible light signal (generally ambient light) is
transmitted toward the human body, where it is amplitude-
modulated by the periodic physiological movements and re-
flected back to the receiver. The VLS receiver captures the
reflected signal and processes it to extract the vital sign signal
components as illustrated in Figure 3.
As shown in Figure 3, the high peaks corresponds to the
inhale position while the lower peaks corresponds to the exhale
position as the signal have covered a longer distance, therefore
less power is received. In addition to the breathing peaks, it is
clear that the breathing signal is modulated by the heart beats
and small heart beats fluctuations can be easily noticed.
The main problem here is extracting the breathing and
heart beats frequency tones from a noisy time domain light
signal using DSP algorithms to monitor human vitals, similar
algorithms was used in previous studies [7], [18], [19] .
Assumptions we have in our model
• Human body is almost in a the same position2
during the
measurement.
• The light source power is constant (Not flickering with a
frequency within the range of interest (0.3-4Hz).
• The system starts giving reliable estimations after 30
seconds3
of measurements.
Signal Processing Algorithms
In Figure 4, we introduce the flow diagram of the algorithm
used in our system to estimate both heart and breathing rates.
Because breathing and heartbeats are periodic motions, we
can extract the frequencies (rates) of breathing and heartbeats
by performing a Fourier Transform (a Fast Fourier Transform
(FFT)). In addition, having different periodicities allows per-
forming separate filtering operations. As shown in the flow
diagram in Figure 4, where the same data is processed for
breathing and heart rate individually. We estimate the breathing
(or heart) rate by detecting the highest frequency tone in the
band of normal breathing (or heart) rate for an adult human
(this can be tailored to other age ranges). First, we filter the
raw data received (see Figure 3) from the photo-detector with
an infinite impulse response (IIR) Chebyshev Type-2 band pass
filter (BPF) of appropriate passband range for adult breathing
(or heart) rates. We filter the frequency domain signal around
(5-40) breaths per minute and (50-150) beats per minute for
breathing and heart rates, respectively(see Figure 5). The effect
of filtering the data is shown in Figure 6. After filtering the
data, we multiply each dataset with a Hanning window to
remove the spectral leakage before the FFT process. Since
the FFT works well with infinite data, if we want to use it
accurately with finite data we have to remove the leakage
introduced by bypassing the convolution of the frequency
domain data with the rectangular window infinite frequency
response as in [42]. After this step, we take the FFT of the
data, and then we detect the highest power tone in the region
of interest (see Figure 7). We carry out these steps on each
dataset and at the end we calculate the average of the breathing
(or heart) rate during the whole measurement.
IV. SIMULATION RESULTS
In Figure 8, we present the hardware setup of our proposed
system to estimate both heart and breathing rates using VLS.
2This assumption can be neglected if a algorithm for removing the limb
motion is used.
3Similar settling time is needed for off-the-shelf vital monitoring devices
as well.

5. Fig. 3. Normalized received raw data before filtering.
Start data
collection
Filter data
using BPF for
Heart rate
Hanning
Windowing
Fourier
Transform
(FFT)
Output Heart and
Breathing Rates
Find
Dominant
Frequency
Filter data
using BPF for
Breathing rate
Hanning
Windowing
Fourier
Transform
(FFT)
Find
Dominant
Frequency
Fig. 4. Flow diagram to show how the vitals of human body is monitored
using the Visible Light source power variation.
A. Setup and Hardware used
We have prepared our initial experimental setup by using a
Thorlabs PDA100A photo-detector [43], a RaspberryPi minia-
ture computer [44], an ADC (PiPlate) circuit [45], an off-the-
shelf fixed power LED light source, and display unit. On the
other hand, the contact based (Off-the-Shelf) devices used to
compare our system performance is Spire stone [46], Kardia
for ECG measurements [47] and a Pulse Oximeter as shown
0 0.05 0.1 0.15 0.2 0.25
Normalized Frequency (×π rad/sample)
-60
-50
-40
-30
-20
-10
0
Magnitude(dB)
Breathing rate BPF
Heart rate BPF
Fig. 5. The filter frequency response for the Butterworth infinite impulse
response (IIR) band pass filter used in the Breathing and Heart rate estimation
algorithm.
TABLE I
ACCURACY OF OUR SYSTEM COMPARED TO SOA.
VLS Off-the-Shelf Accuracy (%)
Heart (bpm) 76.0 80.7 94.2
Breathing (bpm) 14.6 15.1 96.7
in Figure 9.
B. Results and Comparisons
In this section, we discuss the results we got to compare
our system with the SOA devices. In Table 8, we present the
results of our system and Off-the-Shelf devices values and the
accuracy of the measurement taken by our system compared
to the Off-the-Shelf devices.
Finally, using the setup and algorithm mentioned, we have
obtained vitals monitoring and compared with the low-cost

6. 20 22 24 26 28 30
Time (Seconds)
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
NormalizedFilteredSignal(a.u.)
Heart filtered data
Breathing filtered data
Fig. 6. The effect of filtering the data received from the photo detector.
70 80 90 100 110
Heart rate (beats per mintute)
0
0.5
1
NormalizedFFTMagnitude(a.u.)
5 10 15 20 25
Breathing rate (beats per mintute)
0
0.5
1
Peak at Heart rate
Peak at Breathing rate
Fig. 7. The data after FFT in the range of normal breathing and heart rate.
off-the-shelf contact-based devices (shown in Figure 9) to
determine the accuracy of our results. It is worth mentioning
that the off-the-shelf devices [46] are assumed to be 100%
accurate. The results are based on average value of 15 mea-
surements. We have observed more than 94% accuracy as
shown in Table I.
V. CONCLUSIONS AND FUTURE OPPORTUNITIES
In this paper, we proposed the idea of using VLS in the
wireless vitals monitoring sensing. By using off-the-shelf light
source and photo-detector, we were able to collect the reflected
visible light signal from the target human chest. Then, by
applying simple filtering to the signal, we estimate the heart
and breathing frequency components by using some basic
signal processing algorithms (FFT, Windowing). Finally, in
order to validate the results obtained by the implemented
system, we compare our proposed system results with the
vitals results obtained from off-the-shelf devices for vitals
measurements. Our basic system was able to extract breathing
rate information with very high accuracy (more than 95%).
Fig. 8. The hardware setup of the VLS based Vital monitoring system using
a RaspberryPi and Photodetector (PDA 100A).
Fig. 9. The contact based (Off-the-Shelf) devices to measure heart and
breathing rates.
A. Continuing Work
Improving and updating the software and hardware used
in the current proposed system will attract a lot of interest,
because there is a lot of daily life promising applications for
wireless vitals monitoring with a secure and low cost VLS-
based system.
First direction that need to be considered is improving the
used hardware, by testing different light sources (transmitter),
lens (receiver), and the reflective material on the human
body. These additions will improve the signal to noise ratio
received at the photo-detector with will improve the overall
performance of the system. Moreover, using diversity methods
and adding more photo-detectors and light sources will also
improve the received signal quality make it less prone to body
movements as in [48].
Secondly, utilizing more complex signal processing algo-
rithms will give more accurate results in case of different
lighting conditions and body movements by adaptive filtering
of the unwanted parts of the received signal. In addition,
replacing the existing approach to find the frequency peak

7. estimation from the basic FFT algorithm to more optimized
algorithms like RELAX algorithm, which was adopted in RF
based vitals estimation in [49].
Finally, in order to introduce a medical device to the medical
community, it is important to test this device on different range
of humans (age, weight, height, gender). Therefore, a final
phase to improve the user interface of the system use to help
collecting these measurements.
ACKNOWLEDGMENT
This work was done with the help of current and previous
students of Wireless Communication and Sensing Research
Lab (WCSRL) in OSU ECE department: Caleb G. Teague,
Habeeb Idrees, and special thanks to Amit Kachroo for his
valuable comments and suggestions to improve this paper.
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