Abstract and Figures

Remote Photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this paper, we review the development of the field since its emergence in 2008, classify existing approaches for rPPG, and derive a framework that provides an overview of modular steps. Based on this framework, practitioners can use the classification to orchestrate algorithms to an rPPG approach that suits their specific needs. Researchers can use the reviewed and classified algorithms as a starting point to improve particular features of an rPPG algorithm.
Content may be subject to copyright.
Received Month dd, yyyy; accepted August 23, 2016
E-mail: raymond.chiong@newcastle.edu.au
Front. Comput. Sci. ACCEPTED VERSION
DOI 10.1007/s11704-016-6243-6
Philipp V. ROUAST, Marc T.P. ADAM, Raymond CHIONG, David CORNFORTH, Ewa LUX:
Remote heart rate measurement using low-cost RGB face video: a technical literature review
Remote heart rate measurement using low-cost RGB face video: a
technical literature review
Philipp V. ROUAST1, Marc T.P. ADAM2, Raymond CHIONG(*)2, David CORNFORTH2,
Ewa LUX1
1 Institute of Information Systems and Marketing, Karlsruhe Institute of Technology, 76133 Karlsruhe,
2 School of Design, Communication and Information Technology, The University of Newcastle,
Callaghan, NSW 2308, Australia
© Higher Education Press and Springer-Ver l ag B e r li n H e i de l be r g 2016
Abstract Remote photoplethysmography (rPPG)
allows remote measurement of the heart rate using
low-cost RGB imaging equipment. In this study, we
review the development of the field of rPPG since its
emergence in 2008. We a lso classify existing rPPG
approaches and derive a framework that provides an
overview of modular steps. Based on this framework,
practitioners can use our classification to design
algorithms for an rPPG approach that suits their
specific needs. Researchers can use the reviewed and
classified algorithms as a starting point to improve
particular features of an rPPG algorithm.
Keywords Affective computing, Heart rate
measurement, Remote, Non-contact, Camera-based,
1 Introduction
As a source of information about a subject’s physical
and affective state, heart rate measurement (HRM) is of
interest to researchers, medical practitioners, and retail
users alike. A classical application of HRM is for
monitoring in a hospital environment. However,
recently, access to HR data has been necessary for
applications related to personal fitness [1], electronic
commerce [2,3], financial trading [4], and corporate
technostress [5].
A measured HR is derived from a volumetric
measurement (plethysmogram) of the heart as the
number of contractions per minute. Typ icall y, HR M is
conducted using methods that require skin contact. In
the case of electrocardiograms (ECG), this contact is
necessary to measure electrical changes on the skin.
The type of photoplethysmogram (PPG) available on
some smart watches uses skin contact to obtain a
plethysmogram optically. Although they are
noninvasive, these techniques are obtrusive in that they
require contact with the human skin, which can be
detrimental to subjects with sensitive skin (e.g.,
neonates). It can also be irritating (e.g., for subjects
having to wear a fitness tracker) or distracting (e.g.,
when worn in a professional environment). In these
example scenarios, using a less obtrusive, contactless
means of measurement would be beneficial.
During the last decade, considerable research has
been published on HRMs that do not require skin
contact. The developed techniques use a color model
based on red, green, and blue (RGB) imaging to acquire
a signal from a distance of up to several meters. These
techniques are thus commonly referred to as remote
photoplethysmography (rPPG) because of their
similarity to traditional PPG. Research has shown that
reliable HRM can be achieved using low-cost,
consumer-grade digital cameras and ambient light
sources. The proposed methods capture the subject’s
head on video (e.g., using a webcam), from which the
plethysmographic signal is recovered using several
image processing techniques and transformations.
Two main appro aches have emerge d from existin g
studies on rPPG: 1) HRM based on periodic variation
of the subject’s skin color, and 2) HRM based on
periodic head movement. Both of those observable
phenomena are caused by the human cardiac cycle and
thus allow researchers to infer an HRM from an
estimated plethysmographic signal.
Since rPPG was first proposed in 2008 [6], the focus
has shifted from demonstrating feasibility in optimal,
lab-like conditions to a variety of more complex
algorithms for realistic scenarios. The existing review
studies in this field, such as [79], provide a theoretical
background and overview of the field. However, none
of them focuses entirely on low-cost cameras nor
provides a structured classification of existing
approaches. Therefore, our contributions are as follows:
1) to provide an overview of research conducted in
this field;
2) to present a technical account of the typical
components of rPPG algorithms and identify the
main challenges; and
3) to classify published studies by their choice of
algorithm and contributions to the field.
Finally, we also provide suggestions for future research
in the field of rPPG.
2 Research methodology
A clear consensus concerning the name of the field that
we discuss in this study has yet to emerge. While
researching, we came across 15 different terms used by
different authors. These typically employ lexical
combinations that begin with such words as remote,
non-contact, camera-based, video-based,
contactless, contact-free, imaging and end with
such terms as photoplethysmography, heart rate
measurement, heart rate estimation, heart rate
monitoring, as well as various abbreviations thereof.
We cho se to use the term “remote
photoplethysmography” (rPPG) because it is by far the
most frequently used (ca. 50%) and is an original name
[6] for this class of algorithms.
In the process of identifying a wide range of relevant
published studies, we used previously listed terms to
conduct searches in Google Scholar. To this search field
we added studies that have cited the two seminal
studies on rPPG [6,10] by reverse-searching citations.
Because we review studies on rPPG that used
low-cost face video, we include only studies whose
goal was to obtain HRM using videos of subject faces.
Recording equipment must be of commercial grade.
Therefore, those studies that used professional
equipment such as high-speed cameras were excluded.
As of this writing, we found 35 publications that match
our criteria. Fig. 1 provides an overview of the
publication count by year.
Fig. 1 Number of studies by year
3 Background
Phenomena exploited in rPPG are closely related to the
cardiac cycle. During each cycle, blood is moved from
the heart to the head through the carotid arteries. We
will see that this periodic inflow of blood affects both
the optical properties of facial skin and the mechanical
movement of the head, enabling researchers to measure
HR remotely.
The interplay of light and living tissue is complex, as
many processes such as scattering, absorption, and
reflection are at play. Research has shown that
reflection of light is dependent on, among other factors,
2008 2010 2011 2012 2013 2014 2015
blood volume change and blood vessel wall movement
[11,12]. Given suitable illumination, changes in light
reflected from facial skin are thus observable, as the
blood flow and variation of blood volume follow the
cardiac cycle. Traditionally, dedicated light sources
with red or near-infrared wavelengths [11] have been
used to obtain a (contact) photoplethysmogram.
However, recent research has shown that ambient light
can be sufficient to obtain a plethysmographic signal [6]
(as illustrated in Fig. 2a).
Fig. 2 Illustration of phenomena used in rPPG
More recently, some research has focused on
remotely capturing the mechanical impact of blood
flowing in through the carotid arteries at either side of
the head [13]. The idea of exploiting the Newtonian
reaction of the human body to the displacement of
blood dates back to the 1930s [14]. This approach [13]
considers the head-neck system and the trunk as a
sequence of stacked inverted pendulums and surmises
that the opposite reaction to blood inflow causes a
displacement of the head by approximately 5 mm
(illustrated in Fig. 2b). Of the two approaches, that
based on skin color variation, being the original, has
been discussed in many more studies.
4 Early work and recent development
Hertzman and Spealman first noted in 1937 that the
variation in light transmission of a finger could be
detected by a photoelectric cell [15]. The formative
period of rPPG research began in 2008 with Verkruysse
and colleagues first showing that video recordings of a
subject’s face under ambient light contain a signal
sufficiently rich to measure the HR [6]. They asked
volunteers to sit motionless while their faces were
recorded using inexpensive consumer cameras from a
distance of 1-2 m. Fig. 3 illustrates the typical setup of
such studies.
Fig. 3 Typical setup of an rPPG application
Ve rk r u ys s e a t a l . u s e d color recordings of different
quality. For example, a resolution of 640 x 480, which
is a standard graphic mode of the video graphics array
(VGA), and a frame rate of 30 frames per second (fps)
were used. In these recordings, the region of interest
(ROI) was manually selected. From the pixels
contained in the ROI, the raw signal was computed per
frame as the mean value of each of the RGB color
channels. To determine power spectral density of the
signal, Ve rkruysse et al. used the fast Fourier transform
(FFT) algorithm. They showed that the signal for the
green channel contains the strongest plethysmographic
signal, clearly indicating the fundamental HR frequency,
up to its fourth harmonic. This is consistent with the
fact that hemoglobin absorbs green light better than it
does red and blue.
Paving the way for future research was the first study
with the explicit goal of measuring HR using video
recorded with a standard laptop webcam [10]. This
study by Poh et al. used a face detector to track a
subject’s face frame by frame, with a box containing
the subject’s face as the ROI and a moving window of
30 s to achieve a continuous measurement. Improving
on this approach in [6], all three channels of RGB
information were used. Blind source separation (BSS)
estimated the plethysmographic signal as a linear
combination of all three raw signals. The parameters for
this combination were estimated using independent
component analysis (ICA). However, Poh et al. always
chose the second component produced by ICA as the
Ambient light Up to several meters Subject
plethysmographic signal, a shortcoming they later
addressed in an improved version of their algorithm
[16]. The HR was estimated as the frequency with the
highest response after an FFT.
With the general feasibility of rPPG being
established, an increasing number of publications have
been produced in subsequent years, as shown in Fig. 11).
Initial contributions include comparing alternative
methods for BSS and different selections regarding
color channels [17,18], as well as adding temporal
filters before BSS is performed [16,17]. An approach
using an ROI and neural-network-based skin detection
proposed by [19] allows for more accurate
measurement. Another study [20] compared various
linear and nonlinear techniques for BSS and found that
Laplacian eigenmap produces the best results.
The plethysmographic signal in a subject’s face can
be visualized by decomposing the video sequence into
different spatial frequency bands and then magnifying a
desired frequency band using bandpass filtering [21].
When this process is applied to facial videos, slight
temporal changes are detectable. This shows that HR
and individual heart beats can be extracted from the
amplified signal [22].
A funda men tally different approach used to obtain a
raw signal was presented by Balakrishnan et al. in [13].
Instead of relying on color change, this study
demonstrated the possibility of extracting a
plethysmographic signal from the periodic motion of
the subject’s head, which occurs because of the influx
of blood to the head. Balakrishnan et al. tracked an
array of feature points in the subject’s face frame by
frame, recording the longitudinal trajectories. After
performing temporal filtering to remove unwanted
frequencies, they used BSS to obtain a sufficiently
strong plethysmographic signal to estimate the HR. One
weakness of this approach is the fact of signal loss
during bigger motions. Two additional studies explored
this approach. One [23] showed that a single tracking
point can provide sufficient information for HRM. The
other [24] achieved an improved performance by
replacing the FFT with discrete cosine transform (DCT)
in the estimation step.
1) The lower number of publications in 2015 may be attributed to
publication and indexing lag.
Until then, the research on rPPG remained in an early
stage. Although accurate measurements were shown to
be possible using two signal sources, this was
accomplished under mostly controlled conditions using
stationary subjects. In more recent research, the focus
has been on more realistic settings containing naturally
moving or exercising subjects and more challenging
illumination. The two recently explored problems are
reducing noise from subject motion and addressing low
signal strength (e.g., resulting from illumination and
dark skin tone).
A group at P hilips Research [25] addressed the
problem of moving subjects with respect to the light
source. They argued that an optimal fixed combination
of bandpassed RGB channel signals can be found based
on a ratio of normalized color signals when assuming
standardized skin, thus eliminating noise derived
from specular reflection. A defic ien cy of this approach
was that it excluded BSS from the algorithm’s design.
The researchers then further formalized and improved
their approach [26] by proposing a combination with
BSS techniques.
As other researchers [27] have found, the choice of
ROI has a major influence on the quality of the
plethysmographic signal, as not all areas in the face
exhibit the same signal quality. The most recent studies
have focused on more intelligent ROI selection and
tracking to achieve motion robustness. Detection of
facial landmark points is typically the basis for a more
detailed ROI (e.g., to define [2832] and track [29,31]
custom ROIs). An approach by Feng et al. [33] found
an array of points in the subject’s face that can be
subsequently tracked in order to update the ROI on the
subject’s forehead. Consistent with the findings of [27],
Feng et al. later improved their algorithm to use the
area of the cheeks [34]. Further reductions in noise
were made possible by the so-called adaptive bandpass
filter adopted by some authors [3235], the cut-off
frequencies for which were based on past HR estimates.
Custom additional filtering steps introduced by some
authors also aimed at reducing noise (e.g., [29] used an
adaptive filter to reduce noise from illumination
changes using background illumination as a reference).
Further recent developments include variations in the
number of used raw signals, such as the inclusion of
cyan and orange frequencies [30,36]. In a different
approach [31], the facial region was divided into many
small ROIs that yielded an array of signals from the
green channel, each of which was later combined using
a weighted average based on a goodness metric.
Similarly, the researchers in [37] stochastically selected
an array of points and combined them using an
importance-weighted Monte Carlo approach. The use of
BSS, followed by component selection, have recently
been optimized using machine learning techniques
Despite the fact that these recent improvements allow
rPPG algorithms to be applied to more realistic
situations, virtually all studies have continued to focus
on proof of concept using pre-recorded videos.
Although one study [40] presented concepts for
real-time applications, only one other work reported
data from a real-time rPPG application [41].
Signal-to-noise ratios and error rates have typically
been reported, but comparing different approaches is
difficult. This is because most authors have tended to
create their own test scenarios using a variety of
cameras and often have not specified the algorithms
used for compression, thus making reproduction
difficult. An exception to this is a study that
benchmarked rPPG algorithms using videos from a
publicly available database [29]. However, no
consistent practice has yet been adopted.
5 rPPG algorithm classification
We g ive a ge neral c lass ification o f e xistin g rP PG
approaches based on the type of signal (color or
motion). We then propose a general algorithm
framework (see Fig. 4) and classify the chosen
approaches accordingly. An overview of the
corresponding classifications is given in Table 1.
This framework is based on the biological measuring
chain [42]. We subdivide a typical rPPG algorithm into
three key steps: (i) extraction of the raw signal from
several video frames, (ii) estimation of the
plethysmographic signal, and (iii) HR estimation. Each
of these steps has several components that may be
subject to various approaches or can be skipped as in
existing studies.
Fig. 4 Generalized rPPG algorithm framework
The majority of studies (91%) used facial color
variation as the raw signal for rPPG. This periodic color
variation occurs as the skin’s light absorption changes
in accordance with the cardiac cycle. Through the use
of an RGB camera, these slight color variations can be
remotely registered. The remaining 9% of studies were
based on periodic head movements, which can likewise
be monitored using remote imaging. These head
movements represent an equal and opposite reaction to
blood being pumped to the head through the aorta with
each cardiac cycle. In the following subsection, we
describe how we use our proposed general framework
to classify all studies, while highlighting those methods
that are based on color variation.
5.1 Signal extraction
ROI detection. Because the rPPG algorithms we
consider are based on the human face, ROI detection is
necessary to determine the bounds of the face in a video
frame. This information is typically an intermediate
step from which a more accurate ROI is later defined.
In some of the literature, especially in earlier studies in
which the subject was asked to sit motionless (e.g.,
[6,13,17]), the bounds of the face were selected
manually from one of the first frames.
The most frequently used method is the algorithm of
Viola and Jones [43], which is a
machine-learning-based approach that uses a cascade of
simple features to classify faces. The popularity of this
approach is partially due to its availability in the
OpenCV computer vision library, which many authors
have used to implement their rPPG algorithms. A
bounding box of the face is returned when using the
Viola-Jones algorithm.
As an alternative to face detection, Lee et al. [19]
proposed using an algorithm to detect skin regions.
Skin-like pixels were selected using a
neural-network-based classifier. Additional areas such
as the neck and arms may be included in the ROI in this
manner. The drawback of using only skin detection is
the presence of additional noise when objects are
present that have color similar to the skin, as Lee et al.
themselves acknowledged. Therefore, some studies
(e.g., [25]) used skin selection within the bounds of the
face given by the Viola-Jones algorithm.
Recent approaches dealing with subject motion
require more detailed information about face location.
Facial landmark points provide the basis for more
detailed ROI definitions as well as ROI tracking. Using
active appearance models (AAM) [44], which is a
statistical model of the human face in which appearance
is matched to the given video frame, results in a set of
coordinates of known facial landmarks. After face
detection using the Viola-Jones algorithm, [28] and [32]
included this step in their rPPG algorithm. Three other
algorithms for facial landmark detection have been used:
[29] applied discriminative response map fitting
(DRMF) [45] after face detection; [31] used an
algorithm for deformable model fitting; and [46] and
[30] used an algorithm that combines a
regression-based approach with a probabilistic
face-shape model [47]. The last two studies directly
used facial landmark detection without prior face
ROI definition. The ROI is the area within a video
frame that contains pixels providing the raw signal for
the algorithm. Utilizing information from Viola-Jones
or manual face detection, researchers have the option of
simply using the given bounding box of the face as the
ROI [6,17,18,39,48,49]. Some authors also selected an
experimentally determined fixed subset of the bounding
box. As the bounding box from the Viola-Jones
algorithm typically includes background pixels on
either side, a common method is to include 60% of its
width [10,16,20,38]. Other studies [13,24,50] used
different experimentally obtained subsets of the
bounding box as the ROI. Two notable subsets of the
bounding box that may be determined solely from the
bounding box or additionally by the coordinates of the
eyes are the forehead [6,17,33] and cheeks [34], having
been identified as promising regions [27].
Researchers working with facial landmark points
used these to define more exact and robust ROIs. Using
nine landmark points, [29] defined a region that
includes the cheeks and no background pixels, similar
to [30] and [36], which defined a region that includes
the forehead and the area below the eyes.
Another recent approach involves defining multiple
ROIs and generating one RGB signal each for
subsequent analysis. For example, [28] and [32] used
landmark points to define several ROIs representing
regions of the face. The approaches of [27,31,35,37,51]
are more rigorous, each using a large array of small
ROIs. These studies select a subset of available ROIs
using a criterion of signal quality, thus yielding a
dynamic ROI.
ROI tracking. Noise caused by subject motion may
render the signal useless for rPPG. Thus, the goal of
ROI tracking is to ensure that the pixels contained in
the ROI belong to a skin region invariant to subject
motion. Some earlier studies that assumed the subject
was stationary did not use tracking, particularly when
manual ROI detection was involved [6,17].
A straightforward metho d to achieve ROI track ing is
to simply re-detect the ROI for every frame. Two-thirds
of the authors achieved ROI tracking using this method
(Table 1). However, drawbacks exist with this method.
Because the bounding box returned by the frequently
used Viola-Jones object detector is not very exact, ROIs
based on its fluctuating output may in turn cause
unwanted noise. Considering computational complexity,
it is obviously suboptimal to re-run ROI detection for
every frame, especially if real-time applications are
Through use of a set of tracking points or objects and
a tracking algorithm, the location of the ROI can be
updated frame by frame without having to re-detect the
ROI. The good-features-to-track algorithm [52], used
by the authors of [29] and [31] as tracking points,
returns the most prominent corners within the ROI.
Using the Kanade-Lucas-Tomas i (K LT) featur e tr ack er
based on [53], the authors estimated an affine transform
to update the ROI based on subject motion. Similarly,
[33] and [34] used the KLT tracking algorithm based on
the points identified using the speeded-up robust
features (SURF) [54] algorithm. The authors in [19]
used kernel-based object tracking [55] to update the
location of the skin regions included in their ROI. For
each ROI corresponding to a single pixel, [35] used
tracking-by-detection with kernels (CSK) [56] to
compensate for rigid motion and an optical flow
algorithm proposed by Farnebäck [57] to compensate
for non-rigid motion.
Raw signal extraction. The raw signal is extracted
from a video frame by frame according to the ROI
position. For color-based methods, this yields series
!"#$% for the color channels & ' () * +* ,-. Values are
calculated by averaging the respective color channel of
all pixels contained in the ROI of the frame at time $.
This is known as spatial pooling and has the purpose of
averaging out camera noise contained in single pixels.
The number of ROIs and selection of channels for
which this step is performed vary across studies. In the
case of very small ROIs, the image can be
downsampled to avoid noise [35]. To visualize temporal
changes, [21] first decomposed an image into different
spatial frequency bands without explicitly extracting
single values per frame. They referred to this approach
as localized spatial pooling.
Extraction of the raw signal for methods based on
head motion requires selecting tracking points within
the ROI. All three author teams that worked with this
type of method used the good-features-to-track
algorithm [52]. Although [13] and [24] used an array of
tracking points, [23] used only the best identified point.
Using the KLT tracking algorithm, the authors
computed the trajectory of each point. The raw signal
then consisted of series ."*/#$% for tracking point &
and axis 0. Whereas [13] and [24] used just the vertical
axis, [23] used both the vertical and horizontal axes.
Tab le 1 gives the number of series and the signal (in
brackets) to which they correspond (e.g., 3 (RGB) for
the three channels of red, green, and blue). The table
also gives the number of ROIs (e.g., n x 1 (y) denoting
n tracking points for the y axis).
5.2 Signal estimation
Filtering. Despite ROI tracking, the raw signal may
still contain unwanted noise, which depends on subject
motion, illumination changes, and other factors. Using
information about the frequencies of these expected
noise sources and the range of feasible HR frequencies,
researchers typically apply one or more digital filters to
the raw signal. The goal is to increase the signal-to
noise ratio and thus improve the quality of the
estimated plethysmographic signal. Given a raw signal
consisting of multiple series, the filters are normally
applied to each series before dimensionality reduction.
However, some authors apply filters after
dimensionality reduction, whereas some do so both
before and after. In Table 1, (1)indicates filtering
before and (2) filtering after dimensionality
Because the level of raw signals (e.g., color space
value or pixel trajectory) has no meaning when
assessing periodicity, a common first step is to
centralize or normalize the raw signals. Centralizing is
a process in which the mean 12 is subtracted from a
signal 3. Normalization additionally divides the signal
by its standard deviation 42.
Both unwanted high and low frequency noise can be
eliminated using bandpass filtering. This requires an
assumption regarding the band of frequencies that is
feasible for human HR. A common choice of band is
[0.7 Hz, 4 Hz] [10,16,29,34], which corresponds to an
HR between 42 and 240 beats per minute (bpm).
Additional methods that remove unwanted high and
low frequency noise include the moving average filter
and the detrending method. The moving average filter
is a rolling window that averages a given number of
values, thus representing a low-pass equivalent. The
detrending method [58] is based on a smoothness priors
approach and represents a simple and efficient means of
removing the long-running trend from a signal. It can
be seen as a high-pass equivalent. In Fig. 5, we give a
simple example of the removal of low- and
high-frequency noise from the green channel obtained
from a subject’s forehead.
Fig. 5 Exemplary values from a simple rPPG application
using only the green channel
A novel comp onent in recent publications is an adaptive
bandpass [3335] that dynamically changes the cutoff
frequencies based on previously estimated HR, thus
guiding the algorithm to produce consistent HR
estimates. Some authors have experimented with
additional noise reduction by eliminating outliers in the
signals. For example, [29] eliminated the noisiest
segments in a considered signal as measured by
standard deviation. Similar approaches were followed
by Wei et al. [20], who eliminated outliers in the signal,
and Wang e t al. [35], who pruned spatially by excluding
non-skin pixels and outliers with respect to the color
space. To address noise caused by illumination changes
(e.g., playing a movie on a screen), [29] used the
background illumination as reference and applied an
adaptive filter to remove illumination noise from the
Dimensionality reduction. Most authors used a raw
signal that consists of more than one single series (e.g.,
signals corresponding to the RGB channels). It is
assumed that the raw signals contain a one dimensional
plethysmographic signal 5 $ , which can be
represented as a linear combination of these raw signals
using a weighted sum. Estimating the weights for this
combination has proven difficult and is one of the most
debated issues in the literature on rPPG.
The first approach proposed by Poh et al. [10] used
an algorithm for BSS to determine the optimal
combination of raw signals. They chose ICA, which
separates the raw signals into independent,
non-Gaussian signals. In their original rPPG algorithm,
Poh et al. determined that the second component
produced by ICA is typically the most periodic one,
which seems to correspond to the plethysmographic
signal 5#$%. Several other authors adopted this method
[18,28,48]. Theoretically, however, the order that ICA
components appear in is random, which is why Poh et
al. later introduced a selection criterion in the improved
version of their rPPG algorithm [16]. This criterion
chooses the component with the highest peak in the
frequency power spectrum (i.e., a component with a
high periodicity [16,30,36,50]). Another related
criterion chooses the highest periodicity according to
the percentage of spectral power accounted for by the
first harmonic [13,23,24]. The authors in [33] used
correlation with the reference sine function to
determine the best component. A second popular
algorithm for BSS, first used by [17] and later by
[13,24,35], is the principal component analysis (PCA),
which separates raw signals into linearly uncorrelated
components and orders them based on variance.
Criteria used for component selection in ICA can be
equally applied to components produced by PCA.
Machine learning was also used by [38] to select the
most appropriate component produced by ICA.
149 151 153
Raw signal
−0.4 0.0 0.4
Detrended signal
−0.2 0.0 0.2
Time [s]
Smoothed signal
Signal estimation
Heart rate estimation
50 100 150 200 250
02 4 6 8
HR [bpm]
Smoothed signal
Pixel intensitiesNormalized pixel intensitiesMagnitude
Tab le 1 Classification of published rPPG algorithms
Signal extraction
Signal estimation
Heart rate
Dim. red.
3 (RGB)
Centralize, Bandpass
First showing feasibility of rPPG/Data processed
3 (RGB)
(1) Normalize
Using face detection, BSS with ICA, HR
estimation/Fixed component selection after ICA
2 (RG/
(1) Bandpass
Comparing ROI types and BSS techniques/No
automatic process
3 (RGB)
(1) Detrend+Normalize
(2) MA + Bandpass
Improvement over [10] with temporal filtering and
intelligent component selection
3 (RGB),
1 (G)
(1) Normalize
Feasibility using smartphones as video source and for
computation/No real-time measurement in mobile apps
Visual ize and amplify sm all t empo ral ch anges/Only
visualization of the pulse
n x 3
(1) Bandpass
FFT, Peak
Use signal amplification proposed in [21] to measure
the HR
3 (RGB)
Propose skin detection and tracking/Possibly
additional noise from areas similar to skin
3 (RGB)
(2) Eliminate outliers +
MA + Bandpass
Comparing BSS methods/No filter before BSS
n x 1 (y)
(1) Bandpass
FFT, Peak
First proposing an approach based on head
motion/Prone to noise from larger motions
2 (x, y)
(1) Normalize +
Based on horizontal and vertical trajectory of one
pt./Prone to noise from larger motions
VJ +
3 (RGB)
(1) Normalize +
Fixed signal combination based on normalized
skin/Not taking advantage of BSS
3 (RGB)
Using the STFT for HR estimation/No filtering
VJ +
3 (RGB)
(1) Detrending +
Integration of AAM
3 (RGB)
(1) Lowpass +
Normalize +
Detrending + MA
Compare BSS techniques, find ICA to be most
consistent/Manual face detection
n x 1 (G)
Determining the optimal ROI selection/No actual
HRM conducted
n x 1 (y)
(1) Moving average +
Using DCT instead of FFT for HR estimation/Prone to
noise from larger motions
VJ +
1 (G)
Detrending + MA +
Robustness against motion and illumination
changes/Only using the green channel
3 (RGB)
(1) Detrending +
Using different machine learning methods to extract
HR from features
3 (RGB)
(1) Bandpass
fixed linear
Using SVR to extract the HR from frequency domain
features/No detailed ROI
3 (RGB)
(1) Adaptive bandpass
Motion compensation using tracking and adaptive
VJ +
1 (G)
Outlier removal +
Centralize +
Detrending + Lowpass
FFT, Peak
Using AAM and custom filtering/Relies on a
commercial facial analysis framework, only using the
green channel
2 (RG)
(2) Bandpass
Using fixed signal combination instead of BSS/Manual
face detection, no sliding window
VJ +
3 (RGB)
(1) Normalize +
+ fixed lin.
Improvement over [25] by combining the fixed
dimensionality reduction approach with BSS methods
(1) Detrend +
(2) Bandpass
Extract BVP waveform and systolic and diastolic
2 (RG)
(1) Bandpass
(2) Adaptive bandpass
Improvement over [33] using the cheeks and adaptive
red-green difference
n x 1 (G)
(1) Bandpass
FFT, peak
Increase robustness by tracking an array of small ROIs
n x 3
(1) Spatial pruning +
Exclude least periodic
+ Adaptive bandpass
Improve the signal-to-noise ratio by exploiting spatial
redundancy of the image sensor
(1) Detrend, Normalize
(2) Bandpass
Show that a five band camera leads to better
3 (RGB)
(1) Normalize
(2) Bandpass
Propose a real-time approach, use of LDA
2 (RG)
Erythema transform
Stochastically selected points and Bayesian estimation
1 (G)
Bandpass, Kalman
Real-time application/Only using the green channel
n x 1 (G)
Dynamic ROI automatically adjusting to signal
quality/Only using the green channel
3 (RGB)
(1) Normalize
(2) Bandpass + MA
Assess optimal camera distance from
subject/Non-automated ICA component selection
3 (RGB)
(1) MA + Normalize
(2) Bandpass
Combine HRM with other physiological information
Note: VJ: Viola-Jones algorithm; SK: skin detection; AAM: active appearance model; DRMF: discriminative response map fitting; FLM: facial landmark detection; BB: bounding box;
SBB: subset of bounding box; FH: forehead; RE: re-detection; KBOT: kernel-based object tracking; GFTT: good-features-to-track; KLT: Kanade-Lucas-Tom asi tr ackin g al gori thm;
SURF: speeded-up robust features; CSK: tracking-by-detection with kernels; IR: illumination rectification; MA: moving average; NRME: non-rigid motion elimination; LSP: localized
spatial pooling; GRD: green-red difference; STFT: short time Fourier transform; ML: machine learning; SVM: support vector machine; SURF: speeded-up robust features
Similarly, [39] used support vector regression (SVR) to
extract the plethysmographic signal !(#) from a set of
features in the frequency domain. Linear discriminant
analysis (LDA) was used by [40] to reduce
dimensionality. They constructed class values from the
red channel and built the data from the other two
In contrast to the algorithmically determined choice
of weights through BSS, using fixed weights has been
proposed by several authors. Although [19] determined
fixed weights using a brute force technique, others
derived weights from models of skin illumination.
Under the assumptions of a standardized skin color, [25]
proposed a theoretically motion robust method that uses
all three RGB color channels to build two orthogonal
color difference signals. These are then combined to
yield the estimate of !(#). The authors in [25] later
acknowledged several limitations of their method and
proposed combining it with BSS to support component
selection [26]. Similarly, [34] derived an adaptive
green-red difference (GRD) from a model of the skin
and its relationship to the plethysmographic signal. This
GRD is their estimate of !(#).
A model of light interaction with the human skin,
which involves a temporal quotient of raw signal values,
was used by [49] to derive a different estimator for the
plethysmographic signal. In [37], raw color series from
single pixels were transformed using a custom
erythema transform and used to estimate PPG by
Bayesian estimation. Performance of nonlinear BSS
techniques were compared by [20]. They found that
Laplacian eigenmap performed best based on their data.
5.3 Heart rate estimation
Frequency analysis. Given an estimate ! # of the
plethysmographic signal, the HR frequency can be
estimated using frequency analysis. For this purpose,
this signal, which contains a distinct periodicity, is
converted to the frequency domain using a discrete
Fourier transform. The preferred algorithm by most
authors (Table 1) is the FFT. Exceptions are [24], which
used the DCT; [29], which used Welch’s me thod for
density estimation; and [50], which used the short-time
Fourier transform (STFT). In the frequency domain, the
frequency corresponding to the index with the highest
spectral power is chosen as an estimate for the HR
frequency. The intuition for this step is given in Fig. 5.
Peak detection. Using individual peaks, extracting
more information such as HR variability from the
inter-beat intervals is possible. To refine the signal for
peak detection, the signal is usually interpolated using a
cubic spline function [16,30,36]. The peaks can then be
easily identified using a moving window, as they are
the maxima within the signal.
6 Applications
Many promising application areas of rPPG algorithms
(such as medicine and personal fitness) are frequently
referred to in the reviewed literature. To date , existing
applications range from simple experiments to
assessing algorithm accuracy in controlled conditions.
Researchers typically collect their own data (i.e., a
video recording of the subject’s face and corresponding
ground truth measurement) using an established HRM
method such as PPG or ECG. Thus, virtually all
published work on rPPG is based on offline
computations. An online application of an rPPG
algorithm in an economic scenario was used in a lab
experiment in [63].
Most studies employed between 10 and 20 subjects,
optimally of both genders, various ages, and skin colors.
Subjects typically sat at a desk and were given
instructions to remain motionless (e.g.,
[10,16,17,28,48]) or move in a natural manner (e.g.,
[20,29,38,39]) while performing a task on a computer.
Recent studies (e.g., [25,32,38,50,64]) also tested
accuracy using exercising subjects. The camera was
mounted on a location 0.5–3 m from the subject, who
was illuminated with a mix of ambient light. Cameras
used in the experiments varied from built-in and
external webcams, smartphones, and point-and-shoot
cameras to digital single-lens reflex cameras and
mirrorless models. Recorded at between 10 and 30 fps,
videos were saved in compressed or uncompressed
form in various resolutions (from VGA to 720p
resolution). Experiment durations varied from 20 s to
over 10 min.
Tab le 2 Algorithm applications and reported accuracies
Note: S: still; N: natural movement; E: exercising
As argued in Section 4, because of the lack of a
widely used database of face videos and corresponding
ground truth data, a comparison of reported results is
rather difficult, especially given the multitude of
parameters, with different choices potentially biasing
the experimental results. Nevertheless, Table 2 provides
a summary of application data from reviewed studies in
which the number of subjects, baseline methods,
motion instructions, and root mean square errors
(RMSE) have been reported. If no standard case or
average is given, we report the average of reported
values. Again, these should be interpreted with care as a
direct comparison between studies is not possible. As
expected, when considering studies that tested different
motion scenarios, errors increase when subject
activities increase. Given a normal HR of 70 bpm, we
can see that the reported RMSE is significantly less
than 10% in most cases. On average, the reported errors
are higher than those reported for some contact PPG
[67]. However, the recent progress, especially
considering motion and illumination robustness, is
7 Conclusion and research implications
In this study, we addressed the growing phenomenon of
rPPG. We provided a systematic literature review
describing the research conducted in this field up to the
present. We discussed the seminal work that the current
rPPG literature is largely based on and gave an
overview of the field’s development over the last
decade. We showed that a body of literature has
increased over time as research has progressed and
interest grown in rPPG for HRM. We described the
more recent advances in rPPG based on skin color and
head movement. We also included a technical
description of the different physical attributes and
software algorithms for rPPG. For the first time,
published literature was tabulated based on choice of
algorithm and contribution to the field. We also
identified the main challenges in this field of research
and provided suggestions on future research.
The two main challenges currently investigated in
rPPG were identified as: increasing algorithm
robustness with respect to subject noise and addressing
low signal strength due to illumination and skin types.
Many of these challenges have been effectively
addressed, but the explored use cases are mostly remote
from realistic real-world scenarios. Specifically, future
rPPG algorithms must focus on a trade-off between the
amount of processed information and algorithm
complexity, because real-time applications will place a
constraint on computation time.
Clearly, conducting remote HRM using low cost
video equipment is possible, and previous studies show
the increasing sophistication of rPPG. rPPG has a wide
range of applications and the rise in publications over
the period of our survey indicates an increasing interest
in reliable rPPG algorithms. This is attributable to the
demand for contactless HRM solutions in the medical,
professional, and consumer sectors.
Acknowledgements This work was supported by a fellowship
within the FITweltweit programme of the German Academic
Exchange Service (DAAD).
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Philipp V. Rouast holds a BSc degree in
Industrial Engineering from Karlsruhe
Institute of Technology, G erma ny. As
part of his MSc, he is working on a
real-time application of Remote
Photoplethysmography at the University of Newcastle,
Australia. His research interests include machine
learning, computer vision and data analytics.
Marc T. P. Adam is a senior lecturer in
information technology at the
University of Newcastle, Australia. He
received a Diploma in Computer
Science from the University of Applied
Sciences Würzburg, Germany, and a PhD in Economics
of Information Systems from Karlsruhe Institute of
Tec hnolo gy, Germany. In his research, he investigates
the interplay of cognitive and affective processes of
human users in electronic commerce.
Raymond Chiong is a senior lecturer at the University
of Newcastle, Australia. He is also a guest research
professor with the Center for Modern
Information Management at Huazhong
University of Science and Technology,
China, and a visiting scholar with the
Department of Automation, Tsinghua
University, China. He obtained his PhD
degree from the University of Melbourne, Australia,
and an MSc degree from the University of Birmingham,
UK. His research interests include optimization,
machine learning and data analytics. He was the
Editor-in-Chief of the Interdisciplinary Journal of
Information, Knowledge, and Management. Currently,
he is an Editor of Engineering Applications of Artificial
Intelligence and an Associate Editor of the IEEE
Computational Intelligence Magazine. To date, he has
produced over 120 refereed publications.
David Cornforth received the BSc
degree in Electrical and Electronic
Engineering from Nottingham Trent
University, UK, in 1982, and the PhD
degree in Computer Science from the
University of Nottingham, UK, in 1994. He has been an
educator and researcher at Charles Sturt University, the
University of New South Wales, and now at the
University of Newcastle, Australia. He has also been a
research scientist at the Commonwealth Scientific and
Industrial Research Organisation (CSIRO), Newcastle,
Australia. His research interests are in health
information systems, pattern recognition, artificial
intelligence, multi-agent simulation, and optimisation.
Ewa Lux is a PhD candidate at the
Institute of Information Systems and
Marketing, Karlsruhe Institute of
Tec hnolo gy in Ge rmany. She received a
Bachelor degree in Business
Information Systems from Baden-Wuerttemberg
Cooperative State University in cooperation with the
German Central Bank, and a Master degree in
Information Engineering and Management from
Karlsruhe Institute of Technology, Germany. Her
research interest focuses on emotional processes of
economic decision making in electronic markets.
... HR fluctuates depending on people's physical activity as well as mental state. It can also be indicative of a person's emotional state and reaction to external stimuli [1,2]. For instance, while playing video games or watching a movie, the HR can indicate how much a person is enjoying or is engrossed in the activity. ...
... Proportional to the volume of blood flowing through the tissues, a part of the light is absorbed by the tissues and the rest is reflected. By monitoring the amount of reflected light, the Blood Volume Pulse (BVP) signal is extracted, from which the HR, SpO 2 , and BP are computed [1,2]. Although using an oximeter usually does not cause discomfort in the patient, it is not readily available to all for a quick measurement. ...
... These movements are usually present under realistic conditions and are therefore unavoidable. To combat these issues, researchers must employ different techniques in order to filter out noises from the BVP signal due to light intensity changes and motion in the rPPG videos [1,2,6]. ...
Remote Photoplethysmography (rPPG) is a fast, effective, inexpensive and convenient method for collecting biometric data as it enables vital signs estimation using face videos. Remote contactless medical service provisioning has proven to be a dire necessity during the COVID-19 pandemic. We propose an end-to-end framework to measure people's vital signs including Heart Rate (HR), Heart Rate Variability (HRV), Oxygen Saturation (SpO2) and Blood Pressure (BP) based on the rPPG methodology from the video of a user's face captured with a smartphone camera. We extract face landmarks with a deep learning-based neural network model in real-time. Multiple face patches also called Region-of-Interests (RoIs) are extracted by using the predicted face landmarks. Several filters are applied to reduce the noise from the RoIs in the extracted cardiac signals called Blood Volume Pulse (BVP) signal. We trained and validated machine learning models using two public rPPG datasets namely the TokyoTech rPPG and the Pulse Rate Detection (PURE) datasets, on which our models achieved the following Mean Absolute Errors (MAE): a) for HR, 1.73 and 3.95 Beats-Per-Minute (bpm) respectively, b) for HRV, 18.55 and 25.03 ms respectively, and c) for SpO2, a MAE of 1.64 on the PURE dataset. We validated our end-to-end rPPG framework, ReViSe, in real life environment, and thereby created the Video-HR dataset. Our HR estimation model achieved a MAE of 2.49 bpm on this dataset. Since no publicly available rPPG datasets existed for BP measurement with face videos, we used a dataset with signals from fingertip sensor to train our model and also created our own video dataset, Video-BP. On our Video-BP dataset, our BP estimation model achieved a MAE of 6.7 mmHg for Systolic Blood Pressure (SBP), and a MAE of 9.6 mmHg for Diastolic Blood Pressure (DBP).
... 9 These sensors need to be in direct physical contact with human skin, which may cause skin infections, injury, or harmful reactions to patients, especially premature infants, the elderly, or patients with fragile burn skin. 10 Moreover, there is a risk that babies connected to the monitor through wires will be entangled or strangled. 11 These instruments are also not suitable for long-term monitoring, because they may cause discomfort, irritation, and cumulative risk of fungal and bacterial infections. ...
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We propose a method to perform simultaneous measurements of percutaneous arterial oxygen saturation (SpO 2), tissue oxygen saturation (StO 2), pulse rate (PR), and respiratory rate (RR) in real-time, using a digital red–green–blue (RGB) camera. Concentrations of oxygenated hemoglobin (C HbO), deoxygenated hemoglobin (C HbR), total hemoglobin (C HbT), and StO 2 were estimated from videos of the human face using a method based on a tissue-like light transport model of the skin. The photoplethysmogram (PPG) signals are extracted from the temporal fluctuations in C HbO, C HbR, and C HbT using a finite impulse response (FIR) filter (low and high cut-off frequencies of 0.7 and 3 Hz, respectively). The PR is calculated from the PPG signal for C HbT. The ratio of pulse wave amplitude for C HbO and that for C HbR are associated with the reference value of SpO 2 measured by a commercially available pulse oximeter, which provides an empirical formula to estimate SpO 2 from videos. The respiration-dependent oscillation in C HbT was extracted from another FIR filter (low and high cut-off frequencies of 0.05 and 0.5 Hz, respectively) and used to calculate the RR. In vivo experiments with human volunteers while varying the fraction of inspired oxygen were performed to evaluate the comparability of the proposed method with commercially available devices. The Bland–Altman analysis showed that the mean bias for PR, RR, SpO 2, and StO 2 were -1.4 (bpm), -1.2(rpm), 0.5 (%), and -3.0 (%), respectively. The precisions for PR, RR, Sp O 2, and StO 2 were ±3.1 (bpm), ±3.5 (rpm), ±4.3 (%), and ±4.8 (%), respectively. The resulting precision and RMSE for StO 2 were pretty close to the clinical accuracy requirement. The accuracy of the RR is considered a little less accurate than clinical requirements. This is the first demonstration of a low-cost RGB camera-based method for contactless simultaneous measurements of the heart rate, percutaneous arterial oxygen saturation, and tissue oxygen saturation in real-time.
Point-of-care remote photoplethysmography (rPPG) devices that utilize low-cost RGB cameras have drawn considerable attention due to their convenience in contactless and non-invasive vital signs monitoring. In rPPG, sufficient lighting conditions are essential for obtaining accurate diagnostics by observing the complete signal morphology. The effects of illuminance intensity and light source settings play a significant role in rPPG assessment quality, and it was previously observed that different lighting schemes result in different signal quality and morphology. This study presents a quantitative empirical analysis where the quality and morphology of rPPG signals were assessed under different light settings. Participants’ faces were exposed to the white LED spotlight, first when the sources were installed directly behind the video camera, and then when the sources were installed in a cross-polarized scheme. Hence, the effect of specular reflectance on rPPG signals could be observed in an increasing projection. The signal qualities were analyzed in each intensity level using a signal-to-noise (SNR) ratio metric. In 3 of 7 participants, placing the video camera on the same level as the light source led to signal quality loss of up to 3 dB for the range 30–60 Lux. In addition, two fundamental morphological features were analyzed, and the derivative-related feature was found to be increasing with illuminance intensity in 6 of 7 participants.
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Over the last few years, a rich amount of research has been conducted on remote vital sign monitoring of the human body. Remote photoplethysmography (rPPG) is a camera-based, unobtrusive technology that allows continuous monitoring of changes in vital signs and thereby helps to diagnose and treat diseases earlier in an effective manner. Recent advances in computer vision and its extensive applications have led to rPPG being in high demand. This paper specifically presents a survey on different remote photoplethysmography methods and investigates all facets of heart rate analysis. We explore the investigation of the challenges of the video-based rPPG method and extend it to the recent advancements in the literature. We discuss the gap within the literature and suggestions for future directions.
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Current methods of measuring heart rate (HR) and oxygen levels (SPO2) require physical contact, are individualised, and for accurate oxygen levels may also require a blood test. No-touch or non-invasive technologies are not currently commercially available for use in healthcare settings. To date, there has been no assessment of a system that measures HR and SPO2 using commercial off-the-shelf camera technology that utilises R, G, B, and IR data. Moreover, no formal remote photoplethysmography studies have been performed in real-life scenarios with participants at home with different demographic characteristics. This novel study addresses all these objectives by developing, optimising, and evaluating a system that measures the HR and SPO2 of 40 participants. HR and SPO2 are determined by measuring the frequencies from different wavelength band regions using FFT and radiometric measurements after pre-processing face regions of interest (forehead, lips, and cheeks) from colour, IR, and depth data. Detrending, interpolating, hamming, and normalising the signal with FastICA produced the lowest RMSE of 7.8 for HR with the r-correlation value of 0.85 and RMSE 2.3 for SPO2. This novel system could be used in several critical care settings, including in care homes and in hospitals and prompt clinical intervention as required.
Objective: This study proposes an U-net shaped Deep Neural Network (DNN) model to extract remote photoplethysmography (rPPG) signals from skin color signals to estimate Pulse Rate (PR). Approach: Three input window sizes are used into the DNN: 256 samples (5.12 s), 512 samples (10.24 s), and 1024 (20.48 s). A data argumentation algorithm based on interpolation is also used here to artificially increase the number of training samples. Main results: The proposed model outperformed a prior-knowledge rPPG method by using input signals with window of 256 and 512 samples. Also, it was found that the data augmentation procedure only increased the performance for window of 1024 samples. The trained model achieved a Mean Absolute Error (MAE) of 3.97 Beats per Minute (BPM) and Root Mean Squared Error (RMSE) of 6.47 BPM, for the 256 samples window, and MAE of 3.00 BPM and RMSE of 5.45 BPM for the window of 512 samples. On the other hand, the prior-knowledge rPPG method got a MAE of 8.04 BPM and RMSE of 16.63 BPM for the window of 256 samples, and MAE of 3.49 BPM and RMSE of 7.92 BPM for the window of 512. For the longest window (1024 samples), the concordance of the predicted PRs from the DNNs and the true PRs was higher when applying the data augmentation procedure. Significance: These results demonstrate a big potential of this technique for PR estimation, showing that the DNN proposed here may generate reliable rPPG signals even with short window lengths (5.12 s and 10.24 s), suggesting that it needs less data for a faster rPPG measurement and PR estimation.
Conference Paper
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In recent years researchers have presented a number of new methods for recovering physiological parameters using just low-cost digital cameras and image processing. The ubiquity of digital cameras presents the possibility for many new, low-cost applications of vital sign monitoring. In this paper we present a review of the work on remote photoplethysmographic (PPG) imaging using digital cameras. This review specifically focuses on the state-of-the-art in PPG imaging where: 1) measures beyond pulse rate are evaluated, 2) non-ideal conditions (e.g., the presence of motion artifacts) are explored, and 3) use cases in relevant environments are demonstrated. We discuss gaps within the literature and future challenges for the research community. To aid in the continuing advancement of PPG imaging research, we are making available a website with the references collected for this review as well as information on available code and datasets of interest. It is our hope that this website will become a valuable resource for the PPG imaging community. The site can be found at: http://web.mit.edu/~djmcduff/www/ remote-physiology.html.
Conference Paper
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Recent studies have provided strong evidence that photoplethysmographic imaging (PPGi) techniques can serve for measuring the heart rate from video recordings. However, in the works on PPGi technology so far, motion artifacts, which limit the accuracy, were unavoidable. In this work, we present an exploration of our assumption that for a particular model of camera, there is an optimal measurement distance, which ensures the minimal influence of artifacts in PPGi measurement.We conducted experiments using two cameras of different types that are commonly integrated with modern consumer electronic devices. First, we demonstrated that the both types of cameras are applicable for PPGi-based non-contact measurement. We used the both cameras simultaneously to record the face regions of 10 subjects and then extracted the information about their heart rates from each of the recordings. To verify the results obtained by PPGi method, we compared them with those measured using a “gold standard” technique. We then explored the relation between the camera-face distance and the measurement error. For each kind of camera, we determined the optimal face-camera distance that ensured reducing the error from motion artifacts to the possible minimum.
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p>A novel method for remote heart rate sensing via standard broadband video is proposed. Points are stochastically sampled from the cheek region and tracked throughout the video, producing a set of skin erythema time series. From these observations, a photoplethysmogram (PPG) is estimated via Bayesian minimization, with the required posterior probability estimated through an importanceweighted Monte Carlo approach. From the estimated PPG, an estimated heart rate is produced through frequency domain analysis. Results indicate improved accuracy over current state of the art methods.</p
Conference Paper
Heart rate measurements contain valuable information about a person’s affective state. There is a wide range of application domains for heart rate-based measures in information systems. To date, heart rate is typically measured using skin contact methods, where users must wear a measuring device. A non-contact and easy to use mobile approach, allowing heart rate measurements without interfering with the users’ natural environment, could prove to be a valuable NeuroIS tool. Hence, our two research objectives are (1) to develop an application for mobile devices that allows for non-contact, real-time heart rate measurement and (2) to evaluate this application in an IS context by benchmarking the results of our approach against established measurements. The proposed algorithm is based on non-contact photoplethysmography and hence takes advantage of slight skin color variations that occurs periodically with the user’s pulse.
Camera-based remote photoplethysmography (rPPG) is a technique that can be used to measure vital signs contactlessly. In order to optimize the extraction of photoplethysmographic signals from video sequences, we investigate the spatial dependence of the photoplethysmographic signal. For an evaluation of the suitability of various regions of interest for rPPG measurements, we conducted a study on 20 healthy subjects. We analysed the videos using a refined pulse amplitude mapping approach. Our results show that the signal-to-noise ratio of rPPG signals can be improved by limiting the region of interest to certain regions of the face.