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Motion limitations of non-contact photoplethysmography due to the optical and topological properties of skin

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Non-contact photoplethysmography (PPG) provides multiple benefits over in-contact methods, but is not as tolerant to motion due to the lack of mechanical coupling between the subject and sensor. One limitation of non-contact photoplethysmography is discussed here, specifically looking at the topology and optical variations of the skin and how this impacts upon the ability to extract a photoplethysmogram when a subject moves horizontally across the field of view of the detector (a panning motion). When this occurs it is shown that whilst the general relationships between the speed of traversal, detection area and resultant signal quality can be found, the quality of signal in each individual case is determined by the properties of the area of skin chosen.
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Motion limitations of non-contact photoplethysmography due to the optical and topological
properties of skin
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2016 Physiol. Meas. 37 N27
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N27
Physiological Measurement
Motion limitations of non-contact
photoplethysmography due to the optical
and topological properties of skin
MJButler, JACrowe, BRHayes-Gill and PIRodmell
Electrical Systems and Optics Research Division, Faculty of Engineering,
University of Nottingham, Nottingham, NG7 2RD, UK
E-mail: matthew.butler@nottingham.ac.uk
Received 7 December 2015, revised 11 February 2016
Accepted for publication 2 March 2016
Published 21 April 2016
Abstract
Non-contact photoplethysmography (PPG) provides multiple benets
over in-contact methods, but is not as tolerant to motion due to the lack of
mechanical coupling between the subject and sensor. One limitation of non-
contact photoplethysmography is discussed here, specically looking at the
topology and optical variations of the skin and how this impacts upon the
ability to extract a photoplethysmogram when a subject moves horizontally
across the eld of view of the detector (a panning motion). When this occurs
it is shown that whilst the general relationships between the speed of traversal,
detection area and resultant signal quality can be found, the quality of signal
in each individual case is determined by the properties of the area of skin
chosen.
Keywords: heart rate, motion artefact, non-contact, panning,
photoplethysmography (PPG)
(Some guresmay appear in colour only in the online journal)
1. Introduction
Photoplethysmography (PPG) is a well-known technique for extracting cardiac-synchronous
pulsatile signals from subjects from which, for example, the heart-rate can be derived (Hayes
and Smith 2001, Allen 2007, Grubb etal 2014). Two primary advantages of in-contact PPG
M J Butler et al
Motion limitations (PPG): optical and topological properties of skin
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are the instrumentations low cost, and the relative resilience to motion artefacts due to the
physical contact between the subject and the source/sensor that suppresses relative motion.
Non-contact (remote) photoplethysmography has recently become more popular
(Humphreys 2007, Verkruysse etal 2008, Poh etal 2010, Kamshilin etal 2011) due to its
comfort and convenience and minimisation of infection risk in medical applications.
Although it is possible to design a non-contact PPG sensor with a single element (Cennini
et al 2010), cameras can achieve the same functionality. An advantage of a camera (i.e. a
multi-pixel array) is that analyses of multiple locations can be taken simultaneously; to either
detect a PPG from multiple subjects (Poh etal 2010), or analyse the distribution of the PPG
signal over an area on a single subject (Humphreys 2007, Verkruysse etal 2008, Kamshilin
etal 2011). One example of where imaging is used to detect PPGs at different locations is
when analysing the quality of the blood supply (Kamshilin etal 2011) that would be time-
consuming if a single sensor were to be continuously repositioned, and is a necessity when
contact with the skin is not possible (such as with burn patients).
In all forms of photoplethysmography, but particularly when remote with no mechanical
coupling, motion artefacts can corrupt the signal such that the pulsatile waveform is irrecover-
able (although detecting the average heart-rate from a long sectionof a recording may still
be possible (Poh etal 2010)). A, perhaps larger, concern is that an artefact may be falsely
detected as a valid PPG pulse. Hence, a greater understanding of the underlying causes of
motion artefacts are required to better inform design decisions to reduce their effects. This
paper discusses the effects of one such motion, namely panning (whereby the camera and
subject move horizontally with respect to each other but their separation remains constant),
and the limitations that it creates in detecting the PPG in a single element, either alone or as
part of an array.
Whilst the PPG is known to be wavelength dependent, it is the intention of this paper to
primarily explore the effects of motion, components of which will exist for all wavelengths,
to a varying extent.
2. Methods
A camera was used in this study to emulate a single element sensor whose size and posi-
tion can be dynamically adjusted after the measurements have taken place. This allowed for
multiple congurations to be tested on each dataset in order to make fair and quantiable
comparisons.
The camera used was a PCO PixelFly VGA, monochromatic 12-bit CCD scientic camera
(PCO 2009), operated with a resolution of
×640 480
pixels and at 50 frames per second (fps).
The camera was positioned perpendicular to the surface of the subject-under-test, such that
100 mm × 75 mm of skin was visible within the frame (7500 mm2). Figure1 illustrates the
hardware set-up. Subjects were illuminated using the lighting within the room, essentially
daylight supplemented by uorescent lighting. The frame-rate was locked at 50 fps so that the
mains lighting would appear at a constant intensity for the duration of the recording (the 50 Hz
electrical supply results in 100 Hz optical pulsations). All video captures were saved as raw
(loss-less) multi-image tiff les preserving the 12-bit pixel-depth.
2.1. Data collection
Six participants were recruited for the experiment (four male, two female, of mixed ethnicity,
all older than 18 years), and the recordings (photographs and video) were repeated three times.
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Six regions of the skin were investigated. All participants, after being informed about what
data was to be collected from them and the reasons for the study itself, consented to having
anonymised data collected, analysed and published. The study was approved by the University
of Nottinghams Research Ethics Committee (reference number: 2014140).
2.2. Processing techniques
To extract a PPG from the data, a rectangular region of interest (ROI) was selected. The ROI
size and position could be modied as a function of time in order to simulate panning. A single
value was extracted from each frame of the video by averaging the monochromatic pixel val-
ues within the chosen window; these values were then processed and plotted as a time-varying
signal. An example of a PPG from one subject with no simulated panning motion is shown in
gure2.
In order to characterize the effect of a panning motion accurately, 10 s recordings, of
500 frames, were taken with the participants hand held stationary (as far as was possible).
Articial motion was then digitally introduced by moving the ROI linearly across a single
frame of the video (the rst frame) at a chosen speed; a single frame was chosen, instead of all
frames in the video, to prevent the inclusion of the PPG. A simple linear motion was chosen
as this would allow for direct comparison between the spatial frequencies of the skin surface
and the temporal frequencies of typical heart-rates.
Figure 1. Experimental set-up showing the relative location of the camera to the subject.
Figure 2. Two example PPGs obtained from the palm (b); one from the full frame, the
other from the indicated ROI in (a). A 0.8 Hz to 8.0 Hz band-pass lter was used to
improve clarity for illustrative purposes (c). (a) Sample frame from video. (b) Raw pixel
data (12-bit). (c) Filtered PPG (0.8 Hz8.0 Hz BPF).
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Under these controlled conditions, two main components existed in the signal: the PPG,
whose frequency and phase was assumed to be constant throughout the frame, and the changes
in intensity within the image as a result of the panning motion meaning that different regions
Figure 3. The variation in reection of the palm, due to color and topology. The 200
pixels used in the average (dashed line) were taken from above and below the original
pixel.
Figure 4. (DC removed) Spatial frequency components of the palm showing that lower
frequencies (due to the macro structure, as stated earlier) dominate. Most higher spatial
frequencies have magnitudes below the amplitude of the measured PPG.
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of skin lled the ROI. This assumption, regarding the phase of the PPG, was based on the
lack of an observed phase shift over the recorded video frames, and that at 50 frames per
second with a 100 mm frame width, a phase shift would only be visible if the pulse-wave-
velocity (PWV) was slower than 5 ms1 whereas typically, PWVs exceed this (Koivistoinen
etal 2007). Due to the non-homogeneous nature of the skin, the variation due to the motion
was not likely to be negligible.
3. Results
For an ROI of a single pixel scanned spatially (for a single image), or temporally (over all
video frames), the output represents the monochromatic intensity variation of the skin at its
highest spatial resolution. This is illustrated in gure3 (solid line). It can be clearly seen that
any artefacts amplitude for any signicant motion would be greater than the obtained PPG
amplitude (gure 2(c)).
3.1. Subject composition and topology
The slow changes (low spatial frequencies) visible in gure3 are due to the macro structure
of the hand that is its general curvature and physical make-up. The higher spatial frequencies
are due to the micro structures of the skin; the skins cellular structure, its variation in pig-
mentation, and vascular networks beneath the surface will all contribute. This illustrates the
difference in amplitude between a PPG (as obtained earlier, see gure2(c)) and the variation
in reected intensity from the skin due to position. It is clear that the amplitude of the spatial
variations would dominate if any motion occurred.
Figure 5. A demonstration of the effect of ROI traversal velocity on spatial frequency
components. The shaded region represents the proportion of motion artefacts that
exceed the PPG amplitude and are within the given heart-rate range. Note that compared
to gure4, this graphs x-axis represents a temporal frequency: cycles per second (Hz).
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3.2. Additional components
When remotely detecting PPGs from participants, motion can manifest itself in two different
but related ways. Firstly, random movement that occurs naturally during activity will cause
the established motion artefact by adding an uncorrelated signal to that which is detected.
However, a second component may exist that is correlated to the PPG: the optical ballis-
tocardiogram (Ratan 2004). This signal is created when the pulsations of the heart cause a
mechanical effect on the body due to the redistribution of blood, effectively displacing regions
being tested by small (but potentially detectable) amounts; the mechanical effect is known as
the ballistocardiogram (BCG) and is itself a method of extracting vital signs (Alametsä etal
2008). When optically measuring the skin, this displacement can manifest itself as a pulsatile
signal similar to the PPG. It is evident in gure3, for example, that a BCG could be produced
with very little movement by observing a single pixel (the two points A and B are spaced apart
in distance by less than 2 mm, yet have a difference in intensity that is an order of magnitude
greater than the PPG). This BCG could, depending on the movement, either add or subtract to
the actual photoplethysmographic signal. The large positive change in intensity from point A
to point B can be negated by looking at (and averaging) multiple neighbouring pixels which
have the opposite gradient. Whilst ballistocardiographic effects are not considered further in
this document, an important message is that a clean PPG signal may have its origins in bal-
listocardiographic motion.
3.3. Effect of ROI velocity and area
The spatial frequency of the skin surface is not relevant when there is no motion present as the
ROI will encompass the same pixels. However, as the motion (speed) is increased, the spatial
variations (spatial frequency) are perceived as a temporal frequency scaled by the speed. For
example, with a traversal velocity of 1 mm s1, the graph in gure4 could be interpreted as
a temporal FFT; i.e. the horizontal cycles per mm would become cycles per second (Hz).
If the traversal velocity were to double, the frequency components would scale by the same
amount (a component originally at x Hz would stretch to 2x Hz, etc), moving the more domi-
nant low-frequency components into a typical heart-rate range (gure 5).
From this analysis, it is clear that the lower spatial frequency components (being more
dominant) have a more signicant effect on the PPG than the higher spatial frequency comp-
onents. Increasing the ROI area and averaging the contained pixel values creates a simple low-
pass spatial lter that removes the higher spatial frequency components (see gure4). When
motion is present, this lter appears as an equivalent temporal lter.
To reduce the effect of motion artefacts, therefore, either the area of the region under test
must be increased, or the motion speed decreased. Although the latter is not often controllable, a
relationship between the required area of the ROI for a desired PPG-to-artefact (signal-to-noise)
ratio, and the motion velocity (for a panning motion) can be established. However, as each cam-
era model (and type) will have different optical and electrical (noise) characteristics, using an
absolute method of characterising the effect of motion on a signal quality would not provide a
reproducible result. Because of this, a relative method of signal characterisation was used.
To compare the current sample (of a specic area and speed) and the reference sample
(the theoretical best area and speed), a standard correlation technique (Pearson) was deployed.
In this case, the best case scenario for obtaining a PPG (no motion, maximum possible area)
was correlated with the baseline signal, but with a variation in one or both of the ROI area and
traversal velocity. A direct comparison was then determined between the effects of the area and
the ROI traversal velocity on the quality of the PPG as described by the correlation coefcient.
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Three features of the plot in gure6 are of particular interest. Firstly, the correlation in the
area dimension shows that a reduced ROI area results in a lower signal quality, as expected.
It must be noted in this example, however, that the correlation values that exists when there is
no motion present (speed = 0 mm s1) still reduce for smaller areas. This is most likely due to
the fact that a small amount of motion was present in the recordings.
Secondly, not surprisingly, increasing the motion velocity for any given ROI area decreases
the signal correlation. Although the relationship between the speed and the correlation is more
complex than the previous relationship, the general trend follows a quadratic law (gure 7).
The ripples that are visible in gure 6 are due to the spatial frequency components in the
sample not being uniformly distributed. In other words, some temporal frequency components
that exist at a certain motion velocity have a detrimental effect on the PPG quality; hence local
troughs. Conversely, some have similar properties to a PPG signal, effectively and erroneously
enhancing the calculated quality (the local peaks). A change in ROI area affects the quality
predictably, however, due to the random nature of the skins texture and structure, the relation-
ship is less well dened for changes in velocity.
Finally, for a given correlation coefcient value, a relationship between the ROI area and
velocity can be constructed. Initially, it would be reasonable to think that to compensate for
an increase in ROI velocity; its area could be increased by a proportionate amount. However,
there are clear limits; increasing the area of detection (for any region on a body) cannot inde-
nitely improve the PPG quality, since eventually, the larger structures of the body such as skin
creases and the edge of the body will dominate and the quality will no longer increase. If, for
example, due to an increase in area, the ROI were to extend near to or beyond the edge of
the subjects skin (where the optical normal will diverge from the cameras until the skin is no
longer completely within the ROI), the quality will decrease.
3.4. ROI area-velocity relationship
For a given ROI area (A), the quality Q is negatively proportional to the square of the traversal
velocity (V ): ∝−Q V 2. This is clearly visible in gure7. Although less clear in the gures,
Figure 6. Correlation map of ROI speed versus area for the palm. ROI area axis
increases quadratically.
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for a given traversal velocity, the quality is inversely (and negatively) proportional to the ROI
area: ∝−
Q A
1
(this is obtained from the coefcients of the curves in gure7).
Thus an equation(1) can be constructed to link the area, velocity and resulting quality.
Despite being linked to the correlation coefcient mentioned earlier, a negative quality value
(Q) has no meaning and represents zero quality.
=−×
Q
kV
A
1
2
(1)
where k is a constant dependant of the system.
With the assumption that the ability to extract a PPG from a signal containing artefacts is
limited by the PPG-to-artefact ratio, the above equationcan be used to calculate a relative area
required to reduce a motion artefact by a xed amount.
For example, in gure7, if a quality (correlation) of 0.8 is deemed satisfactory with an ROI
velocity of 0.25 mm s1 and area 42 mm2, then to counter a decrease in PPG-to-artefact ratio
when the velocity increases by a factor of 2 to 0.5 mm s1, the area would have to be increased
to 168 mm2 (=42 mm
×2
22
). If the velocity were to increase again by the same factor
(to 1.0 mm s1), then the area would need to be increased to 676 mm2 (=168 mm
×2
22
);
although this is not possible to achieve with the current set-up.
3.5. ROI location
All previous results have concentrated on a single region: the palm. Within the experiment, six
regions were investigated. The following sectionanalyses the difference in artefact suscepti-
bility between the six regions.
In gure8, points where the spatial frequency responses intersect the mean PPG ampl-
itude were determined and their frequencies are summarised in table1. The frequencies of
occurrence are a good indication of how good the area is in relation to the artefacts that are
produced. For example, regions with a low mean intersection frequency allow for faster ROI
traversal velocities before the dominant low spatial frequency components overlap the typi-
cal heart-rate region (see gure5). A low standard deviation of spatial frequencies represents
Figure 7. Illustration of the relationship between the ROI traversal velocity and
the resulting deterioration of the PPG as a function of ROI area. Equationsfor the
highlighted lines (v is traversal velocity): (top) 1.000.27 v2; (middle) 0.950.75 v2;
(bottom) 0.902.10 v2.
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a more reliable region to extract a PPG from when multiple participants are studied; i.e. the
variation between participants is minimal.
The position of each of the six regions in table1 can be easily explained by the topology
of the skin. The region with the largest mean and standard deviation (the cheek) also has the
largest curvature due to its relatively small area. Both the ventral and dorsal forearms score
best with low means and standard deviations. The arm has relatively little curvature (along
its length) resulting in much lower spatial frequencies.
The ability to extract a PPG signal from the skin is also dependent on several factors relat-
ing to the blood ow beneath the surface of the skin. These include capillary density and blood
perfusion which can vary between different regions. Regions such as the forehead, cheeks
and palms provide larger PPG signal amplitudes compared to other areas enabling a higher
PPG-to-artefact ratio (Hertzman 1938). Unfortunately, according to this research, neither of
the forearm sites (ventral and dorsal) provide particularly strong PPGs. The forehead and
palm however, do provide stronger signals, and so despite being regions that allow for larger
artefacts to be generated, are often chosen for experiments.
Figure 8. Comparison of low spatial-frequency components from six different locations
on the body (six participants, three repetitions). Horizontal limit lines (dashed) show
a common, arbitrary, magnitude reference. The 18 vertical lines in each plot indicate
the spatial frequencies where the amplitude rst falls below this limit line. Lines are
smoothed to improve clarity.
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4. Discussion
This analysis has only looked at one of many types of motion; panning. Other types of
motion such as rotation (about an axis perpendicular to the cameras viewing direction) have
an entirely different effect on the PPG and artefacts. Research by Cennini etal (2010), for
example, looked at using multiple wavelengths to eliminate artefacts caused by such a rota-
tion. Their results showed that repetitive rotational movements with frequencies that do not
overlap the PPGs can be suppressed using two wavelengths, where both channels contain
artefact components, but one has a reduced PPG component due to the relative absorption of
tissue and blood (the artefact presented had a peak frequency of 2.2 Hz, whilst the PPGs was
at 1.2 Hz). It is believed that if the area of detection were to move as described in this paper
instead of remaining still, then the magnitude of the artefacts in the same frequency range as
the PPG would be considerably higher due to non-uniform optical variations of the skin. For
non-contact single-sensor designs, this is potentially the largest artefact-related problem that
must be overcome. If motion tracking techniques were to be used to compensate for large
panning motions, a similar rotation would occur as the skins surface normal (the direction
perpendicular to the skin surface) will deviate from the cameras direction; to which a multi-
wavelength method could then be applied.
Digitally compensating for movement, however, requires a redundancy in the captured
images such that the subject (and more specically, the ROI) can move around the frame
without going out-of-shot. As a result, using the same resolution when movement is known
to exist requires a smaller ROI. This in turn increases the susceptibility to motion artefacts
as has been shown. Unless the tracking algorithms are pixel-perfect, jitter that occurs as
images are moved between pixel boundaries can and will add additional artefacts to the signal
as illustrated in gure3.
5. Conclusion
The physical and optical properties of the surface of the skin are such that the quality of the
PPG is related to the relative traversal velocity between the camera and the ROI on the subject;
the lower spatial frequency components of the skin having a greater impact on the corruption
of the PPG signals than the higher.
If small repetitive motions are present, such that ROIs track along intensity gradients, mul-
tiple detection sites would yield artefacts with differing polarities and amplitudes. Thus, for
the same movement direction, sites aligned to opposing intensity gradients produce signals
with opposing polarities; this enables the possibility of detecting whether a signal is, or con-
tains, an artefact.
The size of the region of interest also has a noticeable effect on the detected signal. For
small ROI areas, the reected light intensity varies considerably due the magnitude of the
optical variations of the skin. If larger regions were chosen, whereby the intensities within the
Table 1. Mean and standard deviations of frequency intersection points from each
region.
Spatial
frequency
(cycles m1) Cheek
Hand
(Dorsal) Palm Forehead
Forearm
(Ventral)
Forearm
(Dorsal)
Mean 63.67 60.92 52.81 45.44 35.85 34.46
STD 19.95 13.92 15.34 7.54 4.84 2.88
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ROI are averaged, areas with opposing gradients would cancel and thus reduce the effect of
the erroneous signal. The larger the region, the higher the PPG-to-artefact ratio. This argument
can apply to either small random motions (artefacts), or to ballistocardiographic effects;
either way, the quality of the real PPG can be increased with a larger ROI. However, this
improvement is bounded as subjects skin areas are limited and chosen regions will vary
between people in both size and topology.
It is hypothesised that if a subject were positioned further from the camera with no other
parameters altered, the subject would have to move further to inuence the content of the ROI,
due to perspective effects. If there was no electrical or optical noise present, this would reduce
the generated motion artefacts.
Acknowledgments
The research was funded by the Engineering and Physical Sciences Research Council
(EPSRC, grant number EP/K503101/1), and supported via a Cooperative Awards in Science
and Engineering (CASE) scheme with Tioga Ltd (Derby, UK).
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... The PI was calculated as discussed by Ballaji et al. [26], which is PI (%) = (AC/DC) × 100. The (P-P) ave interval was the distance between two sequential PPG peaks, which represents a completed heart cycle [34][35][36]. , which in this case is 1.88 s. ...
... The PI was calculated as discussed by Ballaji et al. [26], which is PI (%) = (AC/DC) × 100. The (P-P)ave interval was the distance between two sequential PPG peaks, which represents a completed heart cycle [34][35][36]. The temperature sensitivity of FBG1 was higher than FBG2 because FBG1 was embedded and in contact with the epoxy whereas FBG2 was encased inside a stainless-steel tube, as shown in Figure 1. ...
... An alternative is to incorporate the sensor into an insole or textile sock to provide the CRT, PPG and contact pressure in a comfortable and unobtrusive way. This method can also reduce motion artefacts [36] since the sensor will have longer-term contact with the skin. Subsequently, during normal walking, the skin blanching and refilling process can be generated under the foot at every step. ...
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... HR can be extracted using a low-cost color camera, such as a webcam, by processing a sequence of recorded video images. However, the camera approach is subjective to skin-tones [13], motion artifacts [14,15] and lighting condition [16,17]. Accordingly, significant efforts are investigated on imagingbased robust methods for PPG, such as algorithmic development [18][19][20] and exploration of non-visible light waves [21]. ...
... By inspecting Equations (12), (15) and (19), the FT of s BB Di f f (τ j , t) with respect to t is approximated as, ...
... Plugging Equation (A7) into (A2), the spectral representation of interest is obtained and consequently the derivation of Equation (15) completes. ...
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... We decided to track the selected area of interest, which has been previously used [30][31][32][33] . Second, since this tracking algorithm can result in image jittering 34 , we used double tracking by combining the Viola-Jones algorithm 35 with the point detector method [36][37][38] . Here, the face detector automatically detected and extracted the face area as a bounding box, which was then magnified by 10% to cover the entire face. ...
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... Bunlardan farklı olarak ağırlık kaldırma veya kol kaslarının aktif bir şekilde kullanıldığı egzersizlerde sensör ve ölçüm bölgesi (yani bileğin cilt yüzeyi) arasındaki temas ve sıkıştırma kuvvetinin dalga formunu ve dolayısıyla PPG sinyalinin kalitesini önemli ölçüde etkilediği bilinmektedir. (17-34-35) Ayrıca, cilt rengi daha önce PPG sinyallerinin özelliklerini ve dolayısıyla algoritma performansını etkileyen bir faktör olarak tespit edilmiştir (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36). Lakin PPG teknolojisinin kullandığı yeşil ışığın en iyi ölçümleri beyaz tenlilerde verdiği bilinmektedir.(37) ...
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Bilekten Kalp Atım Hızı Ölçümü Yapan Giyilebilir Teknolojiler ve Spor
... However, due to the c-shape of the rings, a rotation to one of the three other orientations always presented with an improved PPG. This is the same effect as a PPG measured in contact and non-contact where the PPG amplitude is greatly diminished in non-contact [33]. Hence, there are several causes of a varying AC and DC PPG components occurring that can further disturb the PI. ...
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Incorrect endotracheal tube (ETT) cuff inflation pressure causes significant problems for intubated patients. The technical development and first in vivo use of a smart ETT for measurements at the cuff-trachea interface during mechanical ventilation are described. The intra-tracheal multiplexed sensing (iTraXS) ETT contains integrated optical fibre sensors to measure contact pressure and blood perfusion. The device is tested during mechanical ventilation in a porcine model (N=6). For contact pressure, signals were obtained in all 30 measurements. For perfusion, data could be obtained in all 33 measurements. In the 3 cases where the cuff was inflated to an artificially high-level, blood occlusion is observed.
... Therefore, our findings cannot be generalized to clinical populations or different age groups. Furthermore, although not always the case [32], skin tone has been found to affect PPG signals [33] but was not accounted for in this study. Finally, our study design did not assess device acceptability, which should be considered if these devices were to be used in community-based interventions. ...
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Background Accurate, continuous heart rate measurements are important for health assessment, physical activity, and sporting performance, and the integration of heart rate measurements into wearable devices has extended its accessibility. Although the use of photoplethysmography technology is not new, the available data relating to the validity of measurement are limited, and the range of activities being performed is often restricted to one exercise domain and/or limited intensities. Objective The primary objective of this study was to assess the validity of the Polar OH1 and Fitbit Charge 3 devices for measuring heart rate during rest, light, moderate, vigorous, and sprint-type exercise. Methods A total of 20 healthy adults (9 female; height: mean 1.73 [SD 0.1] m; body mass: mean 71.6 [SD 11.0] kg; and age: mean 40 [SD 10] years) volunteered and provided written informed consent to participate in the study consisting of 2 trials. Trial 1 was split into 3 components: 15-minute sedentary activities, 10-minute cycling on a bicycle ergometer, and incremental exercise test to exhaustion on a motorized treadmill (18-42 minutes). Trial 2 was split into 2 components: 4 × 15-second maximal sprints on a cycle ergometer and 4 × 30- to 50-m sprints on a nonmotorized resistance treadmill. Data from the 3 devices were time-aligned, and the validity of Polar OH1 and Fitbit Charge 3 was assessed against Polar H10 (criterion device). Validity was evaluated using the Bland and Altman analysis, Pearson moment correlation coefficient, and mean absolute percentage error. Results Overall, there was a very good correlation between the Polar OH1 and Polar H10 devices (r=0.95), with a mean bias of −1 beats·min-1 and limits of agreement of −20 to 19 beats·min-1. The Fitbit Charge 3 device underestimated heart rate by 7 beats·min-1 compared with Polar H10, with a limit of agreement of −46 to 33 beats·min-1 and poor correlation (r=0.8). The mean absolute percentage error for both devices was deemed acceptable (<5%). Polar OH1 performed well across each phase of trial 1; however, validity was worse for trial 2 activities. Fitbit Charge 3 performed well only during rest and nonsprint-based treadmill activities. Conclusions Compared with our criterion device, Polar OH1 was accurate at assessing heart rate, but the accuracy of Fitbit Charge 3 was generally poor. Polar OH1 performed worse during trial 2 compared with the activities in trial 1, and the validity of the Fitbit Charge 3 device was particularly poor during our cycling exercises.
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