Smart Homes that Monitor Breathing and Heart Rate
Fadel Adib Hongzi Mao Zachary Kabelac Dina Katabi Robert C. Miller
Massachusetts Institute of Technology
32 Vassar Street, Cambridge, MA 02139{fadel,hongzi,zek,dk,rcm}@mit.edu
ABSTRACTThe evolution of ubiquitous sensing technologies has led tointelligent environments that can monitor and react to ourdaily activities, such as adapting our heating and cooling systems, responding to our gestures, and monitoring our elderly.
In this paper, we ask whether it is possible for smart environments to monitor our vital signs remotely, without instrumenting our bodies. We introduce Vital-Radio, a wireless sensing technology that monitors breathing and heart rate
without body contact. Vital-Radio exploits the fact that wireless signals are affected by motion in the environment, including chest movements due to inhaling and exhaling andskin vibrations due to heartbeats. We describe the operationof Vital-Radio and demonstrate through a user study that it
can track users’ breathing and heart rates with a median accuracy of 99%, even when users are 8 meters away from thedevice, or in a different room. Furthermore, it can monitor thevital signs of multiple people simultaneously. We envisionthat Vital-Radio can enable smart homes that monitor people’s vital signs without body instrumentation, and actively
contribute to their inhabitants’ well-being.

Author Keywords Wireless; Vital Signs; Breathing; Smart
Homes; Seeing Through Walls; Well-being
Categories and Subject Descriptors H.5.2. Information
Interfaces and Presentation: User Interfaces - Input devices
and strategies. C.2.2. Network Architecture and Design:
Wireless Communication.
INTRODUCTION

The past few years have witnessed a surge of interest in ubiquitous health monitoring [22, 25]. Today, we see smart homesthat continuously monitor temperature and air quality anduse this information to improve the comfort of their inhabitants [46, 32]. As health-monitoring technologies advancefurther, we envision that future smart homes would not only
monitor our environment, but also monitor our vital signals,
like breathing and heartbeats. They may use this information
to enhance our health-awareness, answering questions like“Do my breathing and heart rates reﬂect a healthy lifestyle?”
They may also help address some of our concerns by answering questions like “Does my child breathe normally during sleep?” or “Does my elderly parent experience irregular
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http://dx.doi.org/10.1145/2702123.2702200(a) Inhale Motion(b) Exhale Motion
Figure 1—Chest Motion Changes the Signal Reﬂection Time. (a)shows that when the person inhales, his chest expands and becomescloser to the antenna, hence decreasing the time it takes the signal
to reﬂect back to the device. (b) shows that when the person exhales, his chest contracts and moves away from the antenna, hence
the distance between the chest and the antenna increases, causing an
increase in the reﬂection time.

heartbeats?” Furthermore, if non-intrusive in-home continuous monitoring of breathing and heartbeats existed, it would
enable healthcare professionals to study how these signals
correlate with our stress level and evolve with time and age,which could have a major impact on our healthcare system.
Unfortunately, typical technologies for tracking vital signals
require body contact, and most of them are intrusive. Specifically, today’s breath monitoring sensors are inconvenient:
they require the person to attach a nasal probe [19], wear achest band [43], or lie on a special mattress [3]. Some heartrate monitoring technologies are equally cumbersome since
they require their users to wear a chest strap [18], or placea pulse oximeter on their ﬁnger [21]. The more comfortable
technologies such as wristbands do not capture breathing andhave lower accuracy for heart rate monitoring [12]. Additionally, there is a section of the population for whom wearable
sensors are undesirable. For example, the elderly typicallyfeel encumbered or ashamed by wearable devices [20, 37],
and those with dementia may forget to wear them. Children
may remove them and lose them, and infants may develop
skin irritation from wearable sensors [40].
In this paper, we ask whether it’s possible for smart homes
to monitor our vital signs remotely – i.e., without requiring
any physical contact with our bodies. While past research
has investigated the feasibility of sensing breathing and heart
rate without direct contact with the body [17, 16, 15, 34, 27,
48, 14], the proposed methods are more appropriate for controlled settings but unsuitable for smart homes: They fail inthe presence of multiple users or extraneous motion. They
typically require the user to lie still on a bed facing the device.
Furthermore, they are accurate only when they are withinclose proximity to the user’s chest.

We introduce Vital-Radio, a new input device for tracking
breathing and heartbeats without physical contact with theperson’s body. Vital-Radio works correctly in the presenceof multiple users in the environment and can track the vital
signs of the present users simultaneously. Also, Vital-Radio
does not require the user to face the device or be aware of
its presence. In fact, the user can be sleeping, watching TV,
typing on her laptop, or checking her phone. Furthermore,
Vital-Radio can accurately track a user’s breathing and heart
rate even if she is 8 meters away from the device, or in a different room.
Vital-Radio works by using wireless signals to monitor theminute movements due to inhaling, exhaling, and heartbeats.
Speciﬁcally, it transmits a low-power wireless signal andmeasures the time it takes for the signal to reﬂect back to
the device. The reﬂection time depends on the distance of thereﬂector to the device, and changes as the reﬂector moves.
Fig. 1 illustrates the impact of breathing on the signal’s reﬂection time. When the person inhales, his chest expands
and moves forward, reducing the reﬂection time. In contrast,
when the person exhales, his chest contracts moving awayfrom the device, hence increasing the reﬂection time. Generally, even when the person is not directly facing our device,
the wireless signal traverses his body and his vital signs cause
periodic changes in the signal’s reﬂection time. Vital-Radiomeasures these changes and analyzes them to extract breathing and heartbeats.
A key feature of Vital-Radio is its ability to monitor the vital
signs of multiple people and operate robustly without requiring the users to lie still. The main challenge in delivering thisfeature is that any motion in the environment can affect thewireless signal and hence interferes with tracking breathingor heartbeats. Past work addresses this challenge by requiring that only one person be present in front of the device andthat the person remains still. In contrast, Vital-Radio recognizes that one can address this problem by building on recent
technologies that localize users using wireless signals [6].
Speciﬁcally, Vital-Radio ﬁrst localizes each user in the environment, then zooms in on the signal reﬂected from each user
and analyzes variations in his reﬂection to extract his breathing and heart rate. By isolating a user’s reﬂection, Vital-Radioalso eliminates other sources of interference including noiseor extraneous motion in the environment, which may otherwise mask the minute variations due to the user’s vital signs.
This enables Vital-Radio to monitor multiple users’ breathing
and heart rates, and to operate at distances up to 8 m from theuser or even from behind a wall.
We built a real-time prototype of Vital-Radio and validated
its capabilities by conducting experiments with 14 subjects.
For baselines, we use FDA-approved breathing and heart ratemonitors; these include chest straps for monitoring the inhaleexhale motion and pulse oximeters placed on the subject’s
ﬁnger to monitor their heart rate. In our benchmark evaluation, we ask the users to wear the baseline monitors, while
Vital-Radio monitors them remotely without any body contact. We compare the output of Vital-Radio with the groundtruth from the FDA-approved baselines, demonstrating that

Vital-Radio accurately tracks breathing patterns and heartbeats. Over more than 200 two-minute experiments, our results show that:• Vital-Radio can accurately track a person’s breathing andheart rate without body contact, even when the user is up
to 8 meters away from the device, or behind a wall.
• Vital-Radio’s median accuracy for breathing is 99.3% (error of 0.09 breath/minute) and for heart rate is 98.5% (0.95
beat/minute) when the person is 1 m away from the device. The accuracy decreases to 98.7% (error of 0.15
breath/minute) and 98.3% (1.1 beat/minute) when the person is 8 m away from the device.
• In an area that spans 8 m×5 m, Vital-Radio can monitor thevital signs of up to three individuals with the same accuracy
as for one person.
We also perform activity-focused experiments to explore
Vital-Radio’s monitoring capabilities.
Speciﬁcally, wedemonstrate that Vital-Radio can accurately measure users’
breathing and heart rates while they are typing on their computer or using their cell phones. We also demonstrate that
Vital-Radio can track sharp changes in vital signs. Speciﬁcally, we perform experiments where users are asked to exercise, and show how Vital-Radio accurately tracks the change
in breathing and heart rates after exercising.
We believe Vital-Radio takes a signiﬁcant step toward enabling smart homes that allow people to monitor their vital
signals, and that its capabilities can have a signiﬁcant impact
on our health awareness and our health-care system.
RELATED WORKThe desire for non-contact monitoring of vital signs has occupied researchers since the late 70’s [29]. Early work presented a proof of concept that the wireless signal is affectedby movements of the chest. In these experiments, the personlies still on a bed and the sensor is placed only 3 cm awayfrom the apex of the heart. The results are qualitative with noevaluation of accuracy.
Subsequently, military research explored the potential ofbuilding radars that can detect human presence through walls
or under rubble by relying on the fact that breathing impacts
wireless signals [42, 47, 26, 45]. Speciﬁcally, because wireless signals traverse obstacles, they could be used to sense thechest movements of a trapped victim through rubble or enable
SWAT teams to sense movement from behind an obstacle andavoid being ambushed. However, since these systems target
the military, they typically transmit at excessive power anduse military-reserved spectrum bands [47, 45], which is not
feasible for consumer devices. More importantly, this line ofwork generally focused on the detection of users by sensing
motion due to their vital signs rather than estimating or monitoring the vital signs themselves.
Recently, the mounting interest in technologies for well-being
has led researchers to investigate non-contact methods for analyzing vital signs. Current research on this topic can be divided into two areas: vision-based techniques and wirelesssystems. Speciﬁcally, advances in image processing allowed
researchers to amplify visual patterns in video feeds (such ascolor changes due to blood ﬂow) to detect breathing and heartrate [8, 44]; however, such video-based techniques require theuser to face the camera and do not work when he/she turns
around or is outside the camera’s ﬁeld of view.
Similarly, advances in wireless transmission systems and signal processing have enabled researchers to detect and analyze
human vital signs. Past proposals use one of the following
techniques: Doppler radar [17, 16, 15], WiFi [34, 27], or
ultra-wideband radar [48, 14, 7]. The key challenge in using wireless signals to extract vital signs is that any motion in
the environment affects the signal. Since breathing and heartbeats are minute movements, they can be easily masked byinterference from any other source of movement in the environment. Furthermore, the presence of multiple users – even
if none of them moves – prevents these systems from operating correctly since the wireless signal will be affected by the
combination of their vital signs, making it hard to disentangle
the vital signs of each individual. Past proposals deal with thisproblem by ensuring that there is only one source of motionin the environment: namely, the vital signs of the monitoredindividual. Hence, their experimental setup has one person,
who typically lies still in close proximity to the device [17,16, 15, 34, 27, 48, 14, 7, 4].
In contrast to these past systems, Vital-Radio has an intrinsic mechanism that enables it to separate different sourcesof motion in the environment. To do so, Vital-Radio builds
on state-of-the-art wireless localization techniques [6], which
can identify the distance between the device and different
moving objects. Vital-Radio, however, uses these methods
to disentangle the incoming signals based on distance, rather
than estimate the actual location. This allows it to separate
signals reﬂected off different bodies and body parts. It thenanalyzes their motion independently to estimate the breathing
and heart rate of potentially multiple individuals.
CONTEXT AND SCOPE

We envision that Vital-Radio can be deployed in a smart home
to monitor its inhabitants’ breathing and heart rates, without
body instrumentation. The device can monitor multiple users’
vital signs simultaneously, even if some of them are occluded
from the device by a wall or a piece of furniture. A single device can monitor users’ vital signs at distances up to 8 meters,
and hence may be used to cover a studio or a small apartment. One can cover a larger home by deploying multiple
Vital-Radio devices in the environment.

                       

Figure 2—Separating Reﬂectors into Different Buckets. VitalRadio uses a radar technique called FMCW to separate the reﬂections arriving from objects into different buckets depending on the
distance between these objects and the device.

laptop, reading a newspaper, or sleeping. Vital-Radio can use
all of these intervals to monitor a user’s vital signs, and track
how they vary throughout the day.
THEORY OF OPERATION

Vital-Radio transmits a low power wireless signal and measures the time it takes its signal to travel to the human bodyand reﬂect back to its antennas. Knowing that wireless signals travel at the speed of light, we can use the reﬂection time
to compute the distance from the device to the human body.
This distance varies slightly and periodically as the user inhales and exhales and his heart beats. Vital-Radio captures
these minute changes in distance and uses them to extract theuser’s vital signs.
However, natural environments have a large number of reﬂectors, such as walls and furniture as well as multiple users
whose bodies all reﬂect the wireless signal. To address theseissues, Vital-Radio’s operation consists of three steps:
1. Isolate reﬂections from different users and eliminate reﬂections off furniture and static objects.
2. For each user, identify the signal variations that are due tobreathing and heartbeats, and separate them from variations
due to body or limb motion.
3. Analyze signal variations to extract breathing and heart
rates.
In what follows, we describe how these steps enable us tomonitor users’ vital signs using Vital-Radio.
Step 1: Isolate Reﬂections from Different Users and Elim-

Vital-Radio’s algorithms run continuously, separating signals
from different users, then analyzing the signal from each user
independently to measure his/her vital signs. However, whena user walks (or performs a large body motion), the chest motion is mainly impacted by the walk and no longer representative of the breathing and heart rate.1 At home, there are
typically sufﬁcient intervals when a user is quasi-static; theseinclude scenarios where the user is watching TV, typing on ainate Reﬂections off Furniture and Walls

1
The vast majority of vital signs monitors, including chest bands
that monitor breathing and pulse oximeters that monitor heart rate,cannot provide accurate estimates when the user walks or moves
a major limb [35, 28, 11]. To prevent such motion from causing
errors in its vital-signs estimates, Vital-Radio automatically detects
periods during which the user is quasi-static and computes estimates
only during such intervals.

To isolate signals reﬂected off different objects, Vital-Radio
uses a radar technique called FMCW (Frequency Modulated
Carrier Waves). We refer the reader to [6] for a detailed description of how FMCW works. A key property of FMCW
that we exploit in this paper is that it enables separating thereﬂections from different objects into buckets based on their

To understand the operation of Vital-Radio, let us consider
the scenario in Fig. 2, where the device is placed behind thewall of a room that has two humans and a table. When VitalRadio transmits a wireless signal, part of that signal reﬂects
off the wall; the other part traverses the wall, reﬂects off thehumans and the table inside the room, and then traverses thewall back to the device.

reﬂection times. Since wireless signals travel at the speed oflight, signals reﬂected off objects at different distances would
fall into different buckets.

Our implementation of FMCW follows the system in [6],where the resolution of FMCW buckets is about 8 cm. Thishas two implications:• Reﬂections from two objects that are separated by at least
8 cm would fall into different buckets. Hence, two usersthat are few feet apart would naturally fall into different
buckets. For example, in Fig. 2, the wall, Bob, the table,
and Alice are at different distances from our device, andhence FMCW isolates the signals reﬂected from each ofthese entities into different buckets, allowing us to focus
on each of them separately.
• Using FMCW as a ﬁlter also allows us to isolate some of
the limb motion from chest movements due to breathing
and heartbeats. For example, the signal reﬂected off theuser’s feet will be in a different bucket from that reﬂected
off the user’s chest. Thus, having the user move his feet(in place) does not interfere with Vital-Radio’s ability to
extract the breathing and heart rate.
After bucketing the reﬂections based on the reﬂector’s distance, Vital-Radio eliminates reﬂections off static objects likewalls and furniture. Speciﬁcally, since static objects don’t
move, their reﬂections don’t change over time, and hence canbe eliminated by subtracting consecutive time measurements.
At the end of this step, Vital-Radio would have eliminated
all signal reﬂections from static objects (e.g., walls and furniture), and is left with reﬂections off moving objects separated
into buckets.2
Step 2: Identifying Reﬂections Involving Breathing andHeart Rate

After Vital-Radio isolates reﬂections from different moving
users into separate buckets, it proceeds by analyzing each ofthese buckets to identify breathing and heart rate. For example, in Fig. 2, we would like to identify whether the user inbucket 2 is quasi-static and his motion is dominated by his vital signs, or whether he is walking around or moving a limb.
To do that, Vital-Radio zooms in on the signal reﬂection
which it isolated in the corresponding bucket. This wirelessreﬂection is a wave; the phase of the wave is related to the
distance traveled by the signal as follows [39]:d(t)φ(t) = 2π,(1)
λ2While unlikely, it is possible that multiple users are at the same
distance from the device and hence fall into the same bucket. To
deal with such cases, one may deploy multiple devices so that if two
users are at the same distance with respect to one device, they are atdifferent distances with respect to another device.

1.5
Phase (in radians)

However, in contrast to past work on localization, which uses
FMCW to sense the amount of power arriving from different
distances to localize the users, Vital-Radio uses the FMCW
technique as a ﬁlter –i.e., it uses it to isolate the reﬂected signals arriving from different distances in the environment into
different buckets, before it proceeds to analyze the signals ineach of these buckets to extract the vital signs (step 2 below).

  

1
0.5
0
-0.5
-1
-1.5


0

0.2

0.4

0.6

0.8 1 1.2 1.4
Time (in minutes)

1.6

1.8

2

Figure 3—Phase variation due to vital signs. The ﬁgure shows
the variations in phase due to breathing and heartbeats, where peaks
and valleys in the phase correspond to exhale and inhale motions
respectively; also, zooming in on the signal allows us to observe theheartbeats modulated on top of the breathing motion.

where λ is the wavelength of the transmitted signal, and d(t)is the traveled distance from the device to the reﬂector andback to the device. The above equation shows that one canidentify variations in d(t) due to inhaling, exhaling, and heartbeats, by measuring the resulting variations in the phase of thereﬂected signal.
To illustrate how the phase varies with vital signs, let us consider the example in Fig. 1, where a user sits facing the device.
When the person inhales, his chest expands and gets closer tothe device; and when he exhales, his chest contracts and gets
further away from the device. Because the phase and the distance to a reﬂector are linearly related, Vital-Radio can track aperson’s breathing. Fig. 3 shows the phase of the captured reﬂection as a function of time. Speciﬁcally, a peak in the phasecorresponds to an exhale (highest distance from the device),and a valley in the phase corresponds to an inhale (smallest
distance from the device). We note that our implementation
uses a wavelength λ around 4.5 cm. According to the above
equation, sub-centimeter variations in the chest distance due
to breathing cause sub-radian variations in the phase, which
is what we observe in the ﬁgure.
Similarly, a person’s heartbeats cause minute movements ofdifferent parts of his body. Speciﬁcally, the physiologicalphenomenon that allows Vital-Radio to extract heart rate fromsignal reﬂections is ballistocardiography (BCG). BCG refers
to movements of the body synchronous with the heartbeat due
to ventricular pump activity [36]. Past work has documented
BCG jitters from the head, torso, buttock, etc. [5, 8]. Periodic
jitters cause periodic variations in the wireless signal allowing us to capture the heart rate. These movements translate
to smaller ﬂuctuations on top of the breathing motion in thewireless reﬂection as we can see from local peaks in Fig. 3.
Note that the periodicity of breathing and heartbeats is independent of the user’s orientation. For example, if the user
has his back to the device, the valleys become peaks and viceversa, but the same periodicity persists.
Still, an important question to answer is: what happens whena person moves around or moves a limb, and how can VitalRadio distinguish such motions from breathing and heart-300

Breathing
Limb Motion
0.5

0.6

0.7
0.8
0.9
Time (in minutes)

1

1.1

1.2

FFT Magnitude

Phase (in radians)

3
2
1
0
-1
-2
-3
0.4

Peak atBreathing Rate

200
100
0

0

10
20
30
40
50
Breathing Rate (breaths per minute)

60

Figure 4—Limb motion affects vital sign monitoring. The ﬁgureshows the subject breathing until right before the 1 minute markwhere he waves his hand. The device eliminates time intervals whensuch motion happens.

Figure 5—Output of Fourier Transform for Breathing. The ﬁgure shows the output of the FFT performed on the phase of the signalof Fig. 3. The FFT exhibits a peak around 10 breaths/minute, providing a coarse estimate of the breathing rate.

beats? To help answer this question, we show in Fig. 4 ascenario where the user waves his hand before the one minutemark resulting in aperiodic phase variations of the signal.

Step 3: Extracting Breathing and Heart Rate
Breathing Rate Extraction

To deal with such scenarios, Vital-Radio exploits that motion due to vital signs is periodic, while body or limb motion is aperiodic. It uses this property to identify intervalsof time where a user’s whole body moves or where she performs large limb movements and discards them so that they
do not create errors in estimating vital signs. To achieve this,Vital-Radio operates on time windows (30 seconds in our implementation). For each window, it measures the periodicity of the signal. If the periodicity is above a threshold, it
determines that the dominant motion is breathing and heart
rate; otherwise, it discards the window. A typical approach tomeasure a signal’s periodicity is evaluating the sharpness of
its Fourier transform (or FFT) [10]. Hence, we perform an
FFT on each window, choose the FFT’s peak frequency, anddetermine whether the peak’s value is sufﬁciently higher than
the average power in the remaining frequencies.3
This metric allows us to maintain intervals where a user
does not perform large limb movements, including scenarios
where the user types on her laptop or checks her phone. Thisis because, while these movements are indeed aperiodic, they
do not mask the breathing or the heart rate since their power
does not overwhelm the repetitive movements due to our vital signs.4 Additionally, in some of these scenarios, the user’s
hands are stretched out to the laptop and away from his chestas he is typing. As a result, the major part of his typing motion falls into a separate FMCW bucket than the user’s chest.
Naturally, because the human body is connected, hand movements would still result in muscle stretches and minor shoulder jitters that are close to the user’s chest; however, becausesuch movements are weak and aperiodic, they are diluted at
the output of the FFT. In contrast, periodic movements due tovital signs are enforced in the FFT operation, which results inmaintaining intervals of such quasi-static scenarios.
The above steps allow us to ﬁlter out extraneous motion andfocus on time windows where the dominant motion for eachuser is the breathing and heart rate. In the following section,
we show how Vital-Radio extracts breathing and heart ratefrom these intervals.
3
In our implementation, we choose this peak to be at least 5× above
the average power of the remaining frequencies.
4
Mathematically, these signals would appear as “white noise” in low
frequencies, and are ﬁltered out in Step 3 of Vital-Radio’s operation.

Because breathing is a periodic motion, we can extract thefrequency (rate) of breathing by performing a Fourier transform (an FFT). The peak at the output of the FFT will correspond to the dominant frequency, which in our case is thebreathing rate. Speciﬁcally, we perform an FFT of the phasesignal in Fig. 3 over a 30 second window and plot the output
in Fig. 5. The peak of this signal gives us an initial estimateof the person’s breathing rate.
However, simply taking the peak of the FFT does not provide
an accurate estimate of breathing rate. Speciﬁcally, the frequency resolution of an FFT is 1/window size. For a window
size of 30 seconds, the resolution of our breath rate estimate is≈ 0.033Hz, i.e., 2 breaths/minute. Note that a larger window
size provides better resolution, but is less capable of tracking
changes in breathing rate. To obtain a more precise measurement, we exploit a well-known property in signal processing
which states that: if the signal contains a single dominant frequency, then that frequency can be accurately measured by
performing a linear regression on the phase of the complex
time-domain signal [33]. Hence, we perform an additional
optimization step, whereby we ﬁlter the output of the FFT,
keeping only the peak and its two adjacent bins; this ﬁltering
allows us to eliminate noise caused by extraneous and nonperiodic movements. Then, we perform an inverse FFT toobtain a complex time-domain signal s (t). The phase of s (t)will be linear and its slope will correspond to the breathing
frequency, i.e., the breathing rate. Mathematically, we cancompute an accurate estimate of the breathing rate (in termsof breaths per minute) from the following equation:slope{∠s (t)},(2)
Estimate = 60 ×2π
where the factor of 60 transforms this frequency from Hz (i.e.,
1/second) to breaths/minute.
Heart Rate Extraction

Similar to breathing, the heartbeat signal is periodic, and ismodulated on top of the breathing signal, as shown in Fig. 3.
However, the breathing signal is orders of magnitude stronger
than the heartbeat. This leads to a classical problem in FFT’s,
where a strong signal at a given frequency leaks into otherfrequencies (i.e., leaks into nearby bins at the output of theFFT) and could mask a weaker signal at a nearby frequency.
To mitigate this leakage, we ﬁlter the frequency domain signal around [40-200] beats per minute; this allows us to ﬁlter

FFT Magnitude

4

Maximum but not a Peak

3

Maximum Peakat Heart Rate   

2
1
0

0

25

50
75 100 125 150 175
Heart Rate (beats per minute)200  

225

Figure 6—Output of Fourier Transform for Heart Rate. The
ﬁgure shows the output of the FFT after applying a hanning window
and ﬁltering between [40-200] beats/minute. The highest peak (i.e.,“local maximum”) provides a coarse estimate of the heart rate.

out breathing, which is typically between 8 and 16 breaths per
minute [41]5 as well as high frequency noise (which is higher
than 200 beats per minute).
We plot the output of this obtained frequency domain signal
in Fig. 6, and pick the maximum peak of this output as thefrequency that corresponds to the heart rate. Note that we donot simply pick the absolute maximum of the FFT, because
this absolute maximum is typically the ﬁrst bin after ﬁltering(i.e., around 40 beats/minute), and is due to the leakage fromthe breathing. In contrast, in this example, the peak occurs at
66 beats/minute.
Similar to breathing, simply taking the peak of the FFT leads
to poor resolution. To obtain a more precise estimate of theheart rate, we take an inverse FFT of the signal in the FFT
bin corresponding to the heart rate peak and the two adjacent
FFT bins. We then regress on the phase of this signal using
equation 2. After this regression step, the obtained heart rate
is 66.7 beats/minute, whereas the ground truth heart rate obtained from a pulse oximeter is around 66.5 beats/minute.
Finally, we note that for computing heart rate, we use an FFT
window of 10 seconds only. This window is long enough tocapture the periodicity of heartbeats but it is short enough toquickly react to an increase/decrease in heart rate. Also notethat the FFT is computed over overlapping windows that areshifted by 30ms, hence providing a new estimate every 30ms.
IMPLEMENTATION

Our implementation consists of the following components:
Hardware: We reproduced a state-of-the-art FMCW radio designed by past work on wireless localization [6]. The device
generates a signal that sweeps from 5.46 GHz to 7.25 GHz every 2.5 milliseconds, transmitting sub-mW power. These parameters are chosen in [6] such that the transmission system
is compliant with FCC regulations for consumer electronics.
The FMCW radio connects to a computer over Ethernet. The
received signal is sampled (digitized) and transmitted over the
Ethernet to the computer for real-time processing.
Software: We implement the signal processing algorithms described in the previous sections in C++. The code runs in
realtime, plotting on the screen the breathing and heart rate
as function of time and at the same time logs them to a ﬁle.
The code operates on shifted overlapping FFT windows and5
In fact, this ﬁltering allows us to ﬁlter out the breathing signal andits ﬁrst harmonic.

   

(a) Setup.

(b) Antennas.

Figure 7—Experimental Setup. (a) shows a user sitting about 2.5m
away from Vital-Radio’s antennas; the user also wears a chest strap
and a pulse oximeter, which are connected to the Alice PDx for obtaining ground truth measurements. (b) shows one of Vital-Radio’s
antennas placed next to a quarter.

generates new estimates every 30ms. The output also shows
user motion –i.e., the code tags every 30ms window to showwhether the user is quasi-static or performing a major motion.
EXPERIMENTAL EVALUATION

Participants: To evaluate the performance of Vital-Radio werecruited 14 participants (3 females). These participants were
between 21 and 55 years old (μ = 31.4), weighed between
52 and 95 kg (μ = 78.3), and were between 164 and 187 cm
tall (μ = 175). During the experiments, the subjects wore
their daily attire, including shirts, T-shirts, hoodies, and jackets with different fabric materials.
Ground Truth: To determine Vital-Radio’s accuracy, we compare its output against the Alice PDx [1], an FDA approved
device for monitoring breathing and heart rate. The AlicePDx is equipped with a chest band and a pulse oximeter. The
chest band is strapped around each subject’s chest to monitor
breathing, and the pulse oximeter is placed on his/her ﬁnger
to monitor heart rate during the experiment.
Experimental Environment: We perform our experiments in astandard ofﬁce building; the interior walls are standard double dry walls supported by metal frames with sheet rock on
top. The evaluation environment contains ofﬁce furniture including desks, chairs, couches, and computers.
Throughout the experiments, Vital-Radio’s antennas areplaced on a table, about 3 feet above the ground as shownin Fig. 7. The user sits at some distance from these antennas
and wears the Alice PDx’s chest band and pulse oximeter toobtain the ground truth measurements as shown in the ﬁgure.
In our evaluation, we vary the distance and orientation of theuser with respect to Vital-Radio to determine its accuracy indifferent scenarios as we show in the next section.
Core Experiment: Accuracy Versus Distance

We would like to validate Vital-Radio’s ability to monitor a
subject’s breathing and heart rate at different distances fromour device. In this experiment, we place the device in the corner of a large room, whose ﬂoor plan is shown in Fig. 8. The
device’s antennas are pointed toward the center of the room toensure that they capture motion inside that room. We ask thesubject to sit on a chair at marked locations whose distances
range from 1 m to 8 m away from the device. In each experi-

Heart Accuracy (%)

     


100

Median98.5 98.7 98.7 98.7

90th Percentile
99 98.8 98.5 98.3

90%

80
60
40
20
0
1

100

Median90th Percentile
99.3 99.4 99.3 99.1 99.2 98.6 99.2 98.7

90%

80
60
40
20
0
1

2
3
4
5
6
7
Distance from device (in meters)

ment, the subject sits on a chair facing Vital-Radio’s antennas
and wears the Alice PDx, as shown in Fig. 7.
We run a total of 112 experiments, where we ask each of the
14 subjects to sit at the marked locations from 1m to 8 m.6
Each experiment lasts for two minutes, during which the usersits facing the device in each of these locations. We extract
the breathing and heart rate in real-time using Vital-Radio andlog these vital signs using the AlicePDx. During each two
minute experiment, Vital-Radio outputs a vital sign estimate
every 30 milliseconds, leading to a total of 448,000 measurements across all experiments and all locations.
Based on the ground truth measurements using the Alice PDx, the subjects’ breathing rates range from 5 to 23
breaths/minute, while their heart rates vary from 53 to 115
beats/minute. These rates span the range of adult breathing
and heart rates [41, 31].7
Breathing Rate Accuracy

We compare the output of Vital-Radio with that of the Alice PDx, and plot the median and 90th percentile accuracyof breathing as a function of distance from 1 to 8 meters in
Fig. 9. The ﬁgure shows that our median accuracy is 99.3%
at 1 m and remains as high as 98.7% at 8 m from the device.
It also shows that our 90th percentile accuracy is higher than
90% across all these distances.
6

We limit the experiments to distances of 8 m because the localization accuracy of FMCW-based systems for consumer applications
drops beyond this range [6].
7
We note that one of our subject has a signiﬁcantly high heart rateof 115 beats/minute. We conﬁrmed with the subject that this value,
which was measured with the Alice PDx, is compatible with his
medical records. Also one of our subjects has a low breathing rateof 5 breaths/minute, as measured by the Alice PDx. This subject
practices yoga on a daily basis.

8100

BreathingHeart Rate
99.1 98.7 97.7 97.6 96.7 96.6

97.4 97.1

90%

80
60
40
20
0

8

Figure 9—Breathing Accuracy vs Distance. The ﬁgure shows
Vital-Radio’s breathing accuracy versus its distance to the subject.

2
3
4
5
6
7
Distance from device (in meters)

Figure 10—Heart Rate Accuracy vs Distance. The ﬁgure shows
Vital-Radio’s heart rate accuracy versus its distance to the subject.
Median Accuracy (%)

Breathing Accuracy (%)

Figure 8—Testbed. The ﬁgure shows a layout of our experimental setup, marking the location of Vital-Radio in navy blue, and thedifferent locations where our monitored subjects sat down in red.

front

left
back
Orientation

right

Figure 11—Accuracy versus Orientation. The ﬁgure shows VitalRadio’s median accuracy for breathing and heart rate for a user sitting 4 m from the device and facing different directions.
Heart Rate Accuracy

We plot the median and 90th percentile accuracy of heart rate
as a function of distance from 1 to 8 meters in Fig. 10. The
ﬁgure shows that our median accuracy is 98.5% at 1 meterand drops to 98.3% at 8 meters from the device. It also showsthat our 90th percentile accuracy remains higher than 90%,even with the subject is 8 m away from the device.
Accuracy in Various Scenarios
Accuracy versus Orientation

To validate that Vital-Radio operates correctly even whensubjects do not directly face the device, we run experiments
where we ask our subjects to orient themselves in different directions with respect to the device. Speciﬁcally, we ask eachsubject to sit at the 4 m distance from Vital-Radio and we run
experiments in four different orientations: subject faces thedevice, subject has his back to the device, and the subject isfacing left or right (perpendicular) to the device.
We plot the median accuracies for breathing and heart rate forthese four different orientations in Fig. 11. The ﬁgure showsthat, indeed, when the user faces the device, the median accuracy of breathing and heart rate is highest (99.1% and 98.7%
respectively). However, this accuracy only slightly drops byat most 3% across all the different orientations. Note thatthe device can detect chest motion even when that motion isperpendicular. This is because when one inhales, his chest
expands in all directions, and Vital-Radio can detect a chestside expansion though it is minute.8
Next, we validate that Vital-Radio does not require the user
to be along a straight line facing the antenna. Speciﬁcally, we
8Such expansion is no smaller than variations due to heartbeats.

Through-Wall Accuracy

In order to test the ability of Vital-Radio to measure user’s
vital signs even when they are in a different room, we run a
set of through-wall experiments where the device is placedin a different room than our subjects. Speciﬁcally, we use
the experimental setup in Fig. 8. The device is kept in thelarger room, while the subject sits in an adjacent room behinda wall. The subject faces the device and is about 4 m from it.
Across all experiments, our median accuracies are 99.2% and90.1% respectively for breathing and heart rate. These results
indicate that the breathing rate remains almost the same bothin the presence and absence of the wall (at the same distanceof 4 m). However, the median heart rate accuracy drops dueto the fact that the wall attenuates the heart rate signal significantly (which was already a very minute signal), hence, reducing our signal-to-noise ratio. Still, the heart rate accuracy
remains around 90% even in such through-wall scenarios.
Multi-User Accuracy

We are interested in evaluating Vital-Radio’s accuracy for
multi-user vital sign monitoring. Hence, we perform controlled experiments, where we ask three of our users to sit ona chair at the 2 m, 4 m, and 6 m marks in Fig. 8. In each experiment, Vital-Radio determines that there are 3 users, eachat his respective distance from the device, and outputs the vital signs of each; however, the baseline (AlicePDx) can only
monitor a single user at any point in time. Hence, to evaluate accuracy, we ﬁrst connect the baseline to the ﬁrst user andcompare its output to the output of VitalRadio for the user atthat distance and for that moment. Then, we move the baseline to the remaining users in succession.
We run 20 experiments with different sets of subjects and plot
the accuracies in Fig. 12. The ﬁgure shows that Vital-Radio’s
breathing and heart rate monitoring accuracy is around 98%
for all three users. Note also that the median accuracy ofthe nearest user is higher than that of the further two users
because of the increase in distance between these users andthe device. These results verify that Vital-Radio can monitor
multiple users’ vital signs, and that its monitoring accuracy
for multiple users is the same as that for a single user.
Next, we would like to conﬁrm that Vital-Radio can accurately capture the vital signs of a quasi-static user while otherusers are moving in the environment. In principle, VitalRadio should still operate correctly since FMCW separates
reﬂections from different users based on their distance to the9This result is expected since Vital-Radio uses log-periodic antennas
whose directionality is around 150◦ .

Median Accuracy (%)

place the antennas at the center of the room, and ask users to
sit at a distance of 4 m from the antennas and at angles ranging from −90◦ to +90◦ with respect to the pointing directionof the antennas. We perform 20 one-minute experiments withdifferent subjects at different angles. The results show that
Vital-Radio can capture the user’s vital signs as long as she
is at an angle between −75◦ and +75◦ with respect to theantenna’s pointing direction. Speciﬁcally, the median accuracy is above 98% when the user is on a straight line withrespect to the antenna, and decreases to 96% at the far edge
(i.e., ±75◦ ).9100

Breathing
99.4 98.7

Heart Rate
98.2 98.7
97.3 98.9

90%

80
60
40
20
0
nearestuser

furtheruser

furthest
user

Figure 12—Multi-User Accuracy. The ﬁgure shows Vital-Radio’s
median accuracy in monitoring the vital signs of 3 users simultaneously. The users are sitting at 2 m, 4 m, and 6 m from the device.

device. Hence, we run experiments where we ask one of our
subjects to sit on a chair at the 3 m mark in Fig. 8 while asking
another subject to walk around in the room. Over 20 experiments with different subjects, the median accuracy of breathing and heart rate remains above 98% for the monitored user
as long as the moving user is at a distance of at least 1.5 m
away from him. This accuracy drops below 75% when themoving user gets closer than 1 m to the monitored person.
This is because when the two users are closer than 1 m, thereﬂections off their bodies interfere with each other, preventing Vital-Radio from isolating the signal variations due to themonitored user’s vital signs.
Activity-Focused Experiments
Daily In-Place Activities

We would like to evaluate Vital-Radio’s accuracy in monitoring users as they perform in-place day-to-day activities, suchas typing on their laptops, watching TV, or sleeping. Thus,
we divided the subjects into two groups: one interacting with
their laptops and another interacting with their smartphones.
In each experiment, we ask the subject to sit at 4 m from thedevice and naturally use his/her phone or laptop. Each experiment lasts for 5 minutes; the user reports at the end theactivities he/she performed with their laptop or phone. The
reports show that the users performed various tasks ranging
from checking and responding to their emails to reading news
on the web or logging on to Facebook or Instagram. Some
users were texting using their phones and one user worked ona problem set on his laptop.
Throughout the experiments where our subjects used theirphones, the median accuracies for breathing and heart rate
were 99.4% and 98.9% respectively. These accuracies
slightly dropped to 99.3% and 98.7% when our subjects
were using their laptops. This minor drop in accuracy is
expected because using a laptop typically involves slightly
larger movements than using a phone, leading to a slight reduction in Vital-Radio’s accuracy. Note, however, that theseaccuracies are almost the same as those when subjects weresitting still at the same distance with respect to our antenna.
Hence, Vital-Radio was able to monitor users’ breathing andheart rate as they perform day-to-day activities that do not
require them to move around their apartments.
Exercising and Health-AwarenessHeart rate recovery – which corresponds to how fast a person’s heart rate decreases after exercising – is an important metric for determining how healthy a person’s heart is.
Speciﬁcally, a stronger heart has a fast heart rate recovery, andHeart Rate
(Beats per Minute)110

Reference

Vital-Radio

100
90
80
70
60
50

0

20

40
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80
Time (in Seconds)

100

120

Figure 13—Tracking Heart Rate after Exercising. The ﬁgureshows how Vital-Radio’ can accurately and in real-time track auser’s heart rate as it decreases after exercising.

that recovery rate is a predictor of mortality [13, 24]. Hence,
an important way in which Vital-Radio may contribute to asmart home inhabitants’ well-being is by accurately measuring the heart rate after users exercise.
To evaluate this capability, we evaluate the accuracy of VitalRadio’s real-time vital sign monitoring after asking our subjects to exercise. Speciﬁcally, each of the subjects jumps ropefor 2 minutes then sits down on a chair, 4 m away from thedevice, and breathes normally. During these experiments, we
also ask our subjects to wear the Alice PDx chest band andoximeter to obtain ground truth measurements for their vital signs. While both Vital-Radio and Alice PDx cannot accurately measure heart rate during the excessive motion of
jumping, they can both measure the vital signs when a usersits down after exercising.
Fig. 13 overlays the heart rate estimated by Vital-Radio (inred) on top of the the ground-truth heart rate of a subject asmonitored by the pulse oximeter (in black), throughout a twominute period after the subject stops exercising. The ﬁgureshows that Vital-Radio can effectively track the variations in
the heart rate. Note that, throughout this two-minute period,
there’s a downward trend in the heart rate (from about 93
beats/minute to around 70 beats/minute), which is expected
since the subject is in a resting state after exercising. Also,
note that throughout this period, the heart rate varies continuously about that trend, and that both the ground truth andVital-Radio are able to capture these variations.
Across the experiments with multiple subjects, Vital-Radio’s
median accuracy in measuring breathing and heart rate is
99.4% and 99% respectively, and the 90th percentile is 91.7%
and 96.8%. These ﬁgures are similar to the accuracy achievedin our previous experiments, where subjects were fully rested,
indicating that Vital-Radio can indeed capture our vital signs
and track them accurately even as they vary.
LIMITATIONSIn this section, we elaborate on the limitations of Vital-Radio:
1. Minimum Separation between Users: Vital-Radio uses
FMCW to separate reﬂections from different users before
extracting the per-user vitals. For ideal point reﬂectors,
FMCW can separate reﬂections from two objects if they
are at least C/2B apart [6], where B is the bandwidth andC is the speed of light. For Vital-Radio, this translates to atheoretical minimum separation of 8 cm. However, because

a human is not a point reﬂector, our experiments show thata separation of 1–2 m is needed for high accuracy.
2. Monitoring Range: Since Vital-Radio is a wireless system,
it requires a minimum signal-to-noise ratio (SNR) to extract the signal from the noise, and this SNR bounds its
range and accuracy. Speciﬁcally, the maximum distance atwhich Vital-Radio detects users is 8m. This is because the
SNR drops with user distance from the device.
3. Quasi-static Requirement: Our implementation measuresthe vital signs only for quasi-static users (e.g., typing,watching TV). This is because signal variations due to fullbody motion would otherwise overwhelm the small variations due to vital signs, and prevent Vital-Radio from capturing the minute movements.
4. Non-human Motion: Vital-Radio uses FMCW to separate
reﬂections from different objects in space; hence, it canseparate the reﬂection of various moving objects (e.g., humans, fans, pets). It then analyzes the reﬂections of eachmoving object to detect breathing. Since the periodicity ofbreathing is much lower than fans, the device never confuses a fan as a human. Even if the device confuses a fan
for a human, it will not affect the vital signs of the realhumans since their signals are separated from the fans by
FMCW. However, it may still identify the presence of a pet
and output its breathing and heart rate assuming it is another user in the environment.
CONCLUSION AND FUTURE OPPORTUNITIESThe HCI community has signiﬁcant literature on the use ofphysiological sensing for various applications [38, 30, 9, 23].
In particular, HCI researchers have used physiological sensing to evaluate user experience including emotional reactions, stress levels, cognitive performance, and user engagement. But, a key concern with past sensors (e.g., oximeters,
EEG, FNIRs, GSR) is that they require direct contact with theuser’s body, and hence may affect a user’s response. In contrast, Vital-Radio doesn’t require users to to be aware of its
presence, and hence doesn’t interfere with user experience.
Additionally, Vital-Radio enables new interface and interaction capabilities. For example, it may be incorporated into
user interfaces to adapt to a user without requiring him towear sensors. Also, it can enable environments to adapt themusic or lighting by sensing the user’s vital signs and inferring his mood. Further, a user walking up to a Vital-Radioenabled kiosk in an unfamiliar location (such as an airport)
might receive customized assistance based on his stress level.
Beyond these applications, we believe that Vital-Radio can
impact a wide array of areas in HCI including quantiﬁed self,
smart homes, elderly care, personal health and well-being,
and mobile emotional sensing.10
Acknowledgements – The authors thank the members of the NETMIT group for their feedback, and the reviewers for their insightfulcomments. This research is supported by NSF. The Microsoft Research PhD Fellowship has supported Fadel Adib. We thank members of the MIT Center for Wireless Networks and Mobile Computing: Amazon.com, Cisco, Google, Intel, MediaTek, Microsoft, andTelefonica for their interest and support.
10

While we built our radio for ﬂexible development/control, FMCW
radios are available on the market [2] and can be used for the above
applications once augmented with our algorithm in software.

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