A Calibration-free Blood Pressure Measurement on a Scale: Concept and

Challenges

Christian Wiede

1 a

, Carolin Wuerich

1 b

and Anton Grabmaier

1,2 c

Fraunhofer IMS, Finkenstrasse 61, Duisburg, Germany

University Duisburg-Essen, Bismarckstrasse 81, Duisburg, Germany

Keywords:

Calibration-free Blood Pressure Measurement, Artiﬁcial Intelligence, Feature Extraction, Body Scale, PPG,

ECG, BCG.

Abstract:

Two health parameters are most relevant for self-monitoring of hypertension: blood pressure and body weight.

Blood pressure is normally measured with a blood pressure cuff, whereas body weight can be measured with

a simple body scale. If it is possible to integrate blood pressure measurement into easy-to-use body scales,

patients will beneﬁt from simpler use and lower overall price. The aim of this work is to develop a body

scale with which blood pressure can be measured without calibration and without the need for additional

devices. This can be realised by considering surrogate parameters for blood pressure. Starting from sensors

such as electrodes, photo diodes and pressure transducers, various biosignals such as ECG, BCG, PPG or

bioimpedance are extracted from the sole of the foot. The signal is reduced to morphological features which

serve as input to a neural network for blood pressure determination. The integrated artiﬁcial intelligence

(AI) is to be implemented in an energy-efﬁcient way on an embedded system. In addition, the energy-efﬁcient

implementation ensures battery operation for several months with daily use. Besides the concept, the strengths,

weaknesses, threats and opportunities of this concept are examined in detail within the framework of a SWOT

analysis. This includes considerations of hardware, software, data and user experience.

1 INTRODUCTION

High blood pressure is one of the main causes of coro-

nary heart disease, stroke and kidney failure. In the

EU alone, more than 103 million people suffer from

high blood pressure, which is a quarter of the popula-

tion (European Commission, 2021). Excess weight in

particular increases the risk of developing high blood

pressure and the severity of possible secondary dis-

eases. Therefore, it is very important to measure

blood pressure regularly and above all in correlation

with weight. In this way, appropriate countermea-

sures, such as the dosage of medication, can be ini-

tiated in time. Weight is usually recorded by a body

scale which is easy to handle. Blood pressure, on the

other hand, has to be measured with a blood pressure

cuff.

The idea of this work is to extend the simplicity of

a scale by the functionality of a blood pressure mea-

https://orcid.org/0000-0002-2511-4659

https://orcid.org/0000-0003-0917-2696

https://orcid.org/0000-0002-4882-4223

surement and thus to avoid the complex measurement

process by means of a blood pressure cuff. Thereby,

the particular challenge is to develop a calibration-

free blood pressure measurement method. Existing

solutions for blood pressure measurement via a body

scale require regular calibration with a blood pressure

cuff. This is due to the individual physiological char-

acteristics of the cardiovascular system of each per-

son, which can change due to ageing and other in-

ﬂuences. This is to be addressed by implementing

machine learning methods based on biosignals such

as ECG, PPG, BCG or bio-impedance. Furthermore,

an energy-efﬁcient implementation is necessary to en-

sure battery operation.

This paper is structured as follows: Section 2 re-

views state-of-the-art methods for determining blood

pressure. In the third section, we present our con-

cept for determining blood pressure on a body scale.

Based on this, a discussion of challenges and risks of

this approach is given in section 4. In section 5, we

summarise the results and give an outlook on further

steps.

208

Wiede, C., Wuerich, C. and Grabmaier, A.

A Calibration-free Blood Pressure Measurement on a Scale: Concept and Challenges.

DOI: 10.5220/0010873100003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS, pages 208-214

ISBN: 978-989-758-552-4; ISSN: 2184-4305

2 RELATED WORK

Measuring blood pressure is necessary to draw con-

clusions about the condition and diseases of the car-

diovascular system. There are different ways of mea-

suring it. The most accurate method is the invasive

method using an inserted catheter with a pressure sen-

sor (Pielmus et al., 2021). In addition, invasive blood

pressure measurement is characterised by continu-

ous measurement, so that it is the method of choice

during high risk surgeries and in the intensive care

unit. However, there is a risk of bleeding or infection,

which limits its use especially for daily usage.

The indirect measurement of arterial blood pres-

sure, which is commonly used in everyday life, is car-

ried out by measuring the pressure on the upper arm

using a sphygmomanometer (Riva-Rocci method).

By increasing and decreasing the pressure in the cuff,

the blood pressure can be determined auscultatorily,

palpatorily or oscillatorily. The measurement is less

accurate than the invasive measurement and is not

continuous. On the other hand, it is fast, inexpen-

sive and without great risks. Nevertheless, regular

measurements are perceived as burdensome for pa-

tients. This class of devices also includes blood pres-

sure measurements on the wrist or ﬁngers.

New approaches in research focus at the pho-

toplethysmogram (PPG) more closely (Paviglianiti

et al., 2020; Yan et al., 2019; Kachuee et al., 2017)

and are less stressful to wear or even completely con-

tactless (Nakano et al., 2018; Murakami et al., 2015;

Fan et al., 2020; Jeong and Finkelstein, 2016). A

distinction can be made between time-of-ﬂight mea-

surements and feature-based methods (Shin and Park,

2012). The analysis of the pulse transit time (PTT),

which means the time delay of a pulse wave, and the

pulse arrival time (PAT), which is the time between

the electrical excitation of the heart and the arrival of

the pulse wave, are two common methods for cufﬂess

blood pressure measurement. PPG is used to measure

the propagation time of the pulse wave between two

skin sites or the temporal shift between ECG and PPG

signal. A relationship between these values and blood

pressure can be established via a regression analysis

(Shin and Park, 2012; Oreggia et al., 2015).

Few works (Carek et al., 2019; Martin et al.,

2016; Shin and Park, 2012) investigate and imple-

ment standing blood pressure measurement systems

based on a combination of ECG, a ballistocardiogram

(BCG) for recording the heartbeat or a PPG sensor,

whereby all signals are measured at the foot. Shin

and Park performed a synchronised averaging of the

ECG signal based on the corresponding BCG peak lo-

cation in order to reduce the inﬂuence of electromyo-

gram (EMG) noise from leg muscles. The subsequent

blood pressure estimation is based only on the tem-

poral difference between the R-peak from ECG and

the J-peak from BCG, where the correlation is estab-

lished by a linear regression. Carek et al. and Mar-

tin et al., on the other hand, use PPG and BCG to

calculate a delay. However, the drawback of these

approaches is that their blood pressure determination

methods are all based on an (regular) individual cal-

ibration of the regression curve to the speciﬁc char-

acteristics (e.g. age, height, vascular stiffness) of the

patient. They are therefore cumbersome to use and

require maintenance. A single pulse delay value can-

not provide enough information to determine systolic

and diastolic blood pressure.

Yet, (Paviglianiti et al., 2020; Kachuee et al.,

2017) show that it is possible to develop an univer-

sal model based on ECG and PPG that does not re-

quire individual calibration. Their models consider

morphological signal features as well as the temporal

relationship between PPG and ECG. Various studies

(Sun et al., 2016; Singla et al., 2019; Lin et al., 2017)

show that morphological PPG features can improve

prediction accuracy compared to delay-based features

only. While PTT and PAT exhibit a strong correla-

tion with blood pressure, a single PPG signal can be

sufﬁcient for blood pressure estimation (Rundo et al.,

2018; Xing and Sun, 2016). It has to be noted that

their signals are measured at the wrist, ﬁnger or up-

per body, where ECG and PPG can be derived with

higher quality than at the foot.

In parallel to the scientiﬁc publications, it was in-

vestigated which products and patents exist. There are

scales, e.g. from the Withings company (Buard et al.,

2016), which can determine blood pressure via a PAT

measurement. However, all currently marketed scales

require a calibration procedure and are therefore com-

plicated to operate.

3 METHOD

3.1 Overview

In this section we present our idea of a calibration-

free blood pressure measurement via a body scale and

present the different steps of the processing. Figure 1

shows the single steps graphically. The process be-

gins with the selection of suitable sensors, a compu-

tation unit, the embedded system, its electrical wiring

and its placement and integration into the scale body.

From these sensors, synchronised biosignals such as

ECG, BCG or PPG are subsequently extracted. Based

on these time and frequency signals, special morpho-

A Calibration-free Blood Pressure Measurement on a Scale: Concept and Challenges

209

Figure 1: Overview of the single processing steps. From different physical sensors (pressure transducers, photo diodes and

electrodes) biosignals such as ECG, BCG, PPG or bioimpedance are extracted. Based on them morphological features are

extracted and the blood pressured is determined by a neural network. This is accompanied by an energy-efﬁcient implemen-

tation.

logical features are extracted. Subsequently, the blood

pressure is regressed by means of machine learning,

whereby information such as age and body height

should as well be taken into account. The ﬁnal step

is the energy-efﬁcient implementation on the selected

embedded system.

3.2 Mechanical Design & Signal

Acquisition

The development starts with the design of the me-

chanical structure, the selection of sensors, their

placement and the selection of an embedded system.

Since neither related work nor our previous expe-

rience provide information on exactly which feature

of which biosignal will be most effective when mea-

sured at the foot, the ﬁrst step is to collect informa-

tion from as many different sensors as possible on the

feet. In further consideration and after preliminary

tests, the number of sensors should be reduced to the

necessary level. The sensors we consider are pressure

transducers for weight and BCG, PPG modules con-

sisting of LEDs and photo diodes and electrodes for

the ECG and the impedance cardiogram.

The placement and installation of the sensors

proved challenging in preliminary tests. Especially

with the electrodes and PPG modules, the foot should

rest completely on the measuring point without an air

gap. At the same time, the sensors must be attached

in such a way that they do not exert pressure that in-

terrupts blood ﬂow in underlying vessels. Due to the

arch of the foot, the inside is not suitable for mea-

surements. In contrast, integrating the sensors on the

outside of the sole allows for a robust measurement.

For this purpose, a measurement prototype was made

from PETG material using a 3D printer, see ﬁgure 2.

The plate has a size of 31 cm times 11.5 cm. This

allows different foot shapes and sizes to be measured

up to shoe size EU 48.5 or US 14.

Figure 2: CAD model and 3D print of the measurement

prototype with electrode and PPG module.

The individual sensor modules or preampliﬁers

can be connected directly to any microcontroller via

protocols such as I

C. Hereby, the selection of a suit-

able embedded system plays a crucial role. On the

one hand, it should be sufﬁciently powerful to be able

to determine blood pressure from various biosignals.

On the other hand, the lowest possible energy con-

sumption should ensure long-term battery operation.

Another important point to consider is that the embed-

ded system should be integrated into the scale body at

a later date. For the ﬁrst iteration, an Arduino Nano

(ATmega328) was used, which requires a power con-

sumption of 19 mA and weighs 7 g. This design deci-

sion is preliminary and can be corrected in subsequent

iterations. Energy-efﬁcient processing is essential and

is considered separately in section 3.5.

BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing

210

3.3 Biosignal Generation

In total, at least the following four biosignals should

be extracted: BCG, ECG, PPG and ICG (impedance

cardiogram). From these, secondary parameters such

as respiration rate, HRV or bioimpedance can be de-

rived directly. Thereby, the challenge is that these

biosignals are usually measured on the upper body

(e.g. ECG) or the arms (e.g. PPG). At the feet, on the

other side, the signal quality is signiﬁcantly reduced.

Between heart and feet lies the entire abdominal cav-

ity as well as the lower extremities. This leads on the

one hand to a reduced SNR of the useful signal and

on the other hand to the coupling of bioartifacts from

the corresponding body parts. In the following, the

biosignals are discussed individually:

The ECG is derived via the electrodes on the left

and right foot. Further electro-muscular activities e.g.

of the leg muscles are coupled into the signal. This

is because people automatically tense and relax their

muscles to stand upright and keep balance. Figure

3 shows the difference in signal quality between the

ECG derived from the upper body and from the feet.

The ECG is hardly recognisable from this raw sig-

nal. Synchronised averaging of the ECG signal based

on the corresponding PPG peak location (similar to

(Shin and Park, 2012)) or a signal ﬁltering based on

a wavelet-transform should therefore be used to im-

prove the signal. For the R-peak detection the possi-

ble temporal position can further be narrowed down

by considering the other biosignals.

Figure 3: ECG signals simultaneously derived between the

right and left foot (blue) and between feet and hand as ref-

erence (red).

Considering the mechanical attachment from the

previous section, the PPG can be derived stably, see

ﬁgure 4. Therefore, only a band pass ﬁltering is

needed for pre-processing. Note that PPG signals

originate from different depths of the foot and thus

maps multiple pulse waves slightly shifted in time.

This leads to a smoothing of the overall curve.

The pressure sensors in the feet of the scale record

the body movement over time, in addition to the

Figure 4: PPG signal measured at the foot. Placed on the

outside of the sole of the foot, it provides a clear signal.

weight of the person. Besides balancing movements

for maintaining the upright position, the mechanical

activity of the heart and the pulse wave can be mea-

sured as well. In accordance with Newton’s third law,

each application of force produces a counter-force of

equal magnitude. The measured mechanical impulses

can thereby temporally map the state of the heart and

blood vessels and complement to the two previous

methods. The main interfering factors are attenua-

tion due to the distance from the heart as well as cou-

pling muscle movements. In order to separate bal-

ancing movements from the impulses caused by heart

contractions, the differences in the signals of the four

sensors can be analysed.

In ICG, voltage changes are measured when a

small measuring current is introduced. From this,

the impedance and its changes over time caused by

heartbeats and blood volume changes can be recorded

directly. In addition to recording the hemodynamic

parameters such as stroke volume and cardiac out-

put, it is also possible to make statements about the

anatomy (e.g. tissue composition). A derivation of

detailed hemodynamic parameters via the feet repre-

sents a novelty in this context, so that only assump-

tions can be made about the feasibility. A low signal

amplitude is expected and a coupling of artifacts from

the rest of the body.

As can already be seen in ﬁgure 1, the individual

biosignals correlate strongly with each other or are

shifted in time relative to each other, from which a

difference in transit time can be determined. These

interdependent inﬂuences are analysed subsequently.

3.4 Blood Pressure Determination

The determination of the blood pressure can be per-

formed, as described above, via a time delay mea-

surement (PTT or PAT) or the observation of the PPG

signal morphology. For a ﬁnal evaluation, both ap-

proaches should be combined. The PAT can be deter-

mined from the time difference between the R-wave

of the ECG and the incoming pulse wave. While

PAT alone does not allow any adaptation to or con-

A Calibration-free Blood Pressure Measurement on a Scale: Concept and Challenges

211

clusions about the individual physiology, we will ad-

ditionally analyse the morphology of the recorded sig-

nal courses. The morphology describes the entirety of

the biosignal (e.g. times, amplitudes, slopes, shapes),

which is difﬁcult to capture in a model-based way, but

describe important properties (e.g. vascular stiffness).

In combination with ML methods, the blood pressure

should thus be determined independently of the per-

son and without calibration.

After the biosignals have been extracted, the ques-

tion arises as to which information in the individ-

ual signals contains a contribution to the information

about the blood pressure. Such features are, for ex-

ample, the PAT, the diastolic width at 50 % of maxi-

mum or the rising area of systole. In addition, it must

be noted that numerous features contain the same or

similar information and are strongly correlated. How-

ever, the aim is to generate as few meaningful features

as possible in order to keep the size of the subsequent

neural network as small as possible. For this purpose,

all features are subjected to a Sequential Forward Se-

lection, which belongs to the wrapper methods of fea-

ture selection. First, all features are trained individu-

ally with a neural network of one input each to deter-

mine the blood pressure. The feature with the small-

est error is kept. Afterwards, all remaining features

are paired with the already selected feature and fed

to a neural network with two inputs. This process is

continued iteratively until all features have been se-

lected. Now, we know exactly which feature combi-

nation will achieve which accuracy.

Additional information can be included such as

age, gender and height to improve prediction accuracy

(Kim et al., 2006; Luo et al., 2019; Lu and Dai, 2018).

These values are already retrieved by commercially

available body scales and are therefore easily acces-

sible. Since only a small number of data points can

be recorded in this experimental setup, transfer learn-

ing techniques will be used. There are databases with

biosignals (Saeed et al., 2002; Johnson et al., 2016),

which were recorded in intensive care units and also

show continuous blood pressure values. These large

datasets form a more diverse data source than just our

own data. Based on this datasets, a transfer function

is modelled, which transforms the signals recorded on

the upper body from the dataset into the virtual biosig-

nals on the feet.

3.5 Energy-efﬁcient Implementation

Energy-efﬁcient implementation is essential to keep

power consumption low and thus enable battery op-

eration on the body scale. In order to guarantee this,

a trade-off between accuracy and complexity of the

neural network must be found. The size of the neural

network, together with the number of neurons, deci-

sively determines the computational effort, since there

is a full interconnection between the individual lay-

ers, which has a multiplicative effect. In addition,

the number of features is formative for the number

of inputs of the neuronal network. The smaller the in-

put layer, the fewer neurons are needed in subsequent

layers. Furthermore, the type of selected features and

their complexity is a major inﬂuencing factor on com-

putational cost. Another factor is the sampling rate

at which the signals are recorded, processed and dis-

played.

In addition to accuracy, the calculation effort

should also be included as a selection criterion in Se-

quential Forward Selection. This ensures that the cal-

culation effort of the individual features is also taken

into account. The speciﬁc weighting of these two pa-

rameters is to be carried out iteratively. In order to

reduce layers and neurons, pruning is applied to the

trained neural network. This removes the layers and

neurons with only a small contribution to the out-

put. The neural network is implemented in AIfES

(Fraunhofer IMS, 2021), an open source framework

for embedded systems. It is implemented directly in

C, which is fast and executable on any small micro-

controller. Furthermore, there is no need for an op-

erating system, which saves overhead. The ﬂoating

point values are to be quantised to an integer value

(e.g. I32 or I8). Since only additions are carried out

and no FPU, there is potential for energy savings. At

times when no measurement is taking place, the Ar-

duino is to be put into sleep mode with hardly any

energy being consumed.

4 DISCUSSION

In the following, the previously presented concept

is evaluated and classiﬁed within the framework of

a SWOT analysis. A compact presentation can be

found in table 1.

Strengths:

• One strength is the reduction of two devices (body

scale, blood pressure cuff) into a single combined

device, reducing hardware as well as costs and

measuring time for the user.

• The calibration-free measurement ensures easy

handling by the user.

• The ease of use (just standing on the scale) com-

pared to the measurement requirements of the

blood pressure cuff reduces the risk of incorrect

operation.

BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing

212

Table 1: SWOT analysis of a calibration-free scale for blood

pressure determination.

Strengths Weaknesses

Reduction of devices Placement of feet

Calibration-free low signal quality

Easy usage

Higher comfort

Opportunities Threats

Regular measurements Availability of data

More vital parameters Less accuracy

Cost reduction Transferability

Circulation disorders

Exclusion of persons

• The overall result is increased comfort for the

user.

Weaknesses:

• Precise placement of the feet is required so that

the sensors have contact with the foot. This can

be circumvented by placing markers on the top of

the scale.

• The signal quality for some signals, e.g. ECG, is

signiﬁcantly reduced in comparison to attachment

to the upper body.

Opportunities:

• The ease of use and increased comfort allow for

more regular measurements and thus a more ac-

curate picture of the state of health.

• In addition to blood pressure, there is the potential

to directly measure other vital parameters such as

heart rate, respiration rate, oxygen saturation or

blood glucose level.

• It is expected that there will be a cost reduction

due to fewer devices and cheaper sensors.

Threats:

• A major risk is the low availability of data. In

particular, a large diversity in the training data is

important to prevent bias. E.g., it can be expected

that age has a very strong correlation with blood

pressure. It must be ensured that such external

factors do not dominate the predictions and that

all possible cases are represented correctly in the

model.

• There is a possibility that the blood pressure pre-

dicted by the scale is less accurate than the blood

pressure cuff. However, since cuff-based methods

also exhibit a relatively high error, reference mea-

surements should be taken with a more accurate

and continuous system for evaluation.

• Another risk is the applicability of transfer learn-

ing. Since some publicly available data is col-

lected on the basis of intensive care patients, the

transferability must be checked in a dedicated

manner.

• In case of blood circulation disorders, there is a

risk that the measured values are inaccurate or

non-existent (where inaccurate readings are worse

than no readings at all). Therefore, a plausibility

check should be implemented.

• In case of employing bioimpedance measure-

ments, due to the partially active measuring prin-

ciple, certain groups of people (e.g. wearers of

pacemakers) may have to be excluded from use.

5 CONCLUSION

In order to make the methodology of measuring

blood pressure easier and to integrate it into every-

day life, a concept for measuring blood pressure via

a calibration-free body scale was developed. This

includes the construction of hardware, the selection

of sensors, the biosignal extraction, the determina-

tion of blood pressure via machine learning methods

as well as the energy-efﬁcient implementation. The

calibration-free determination will be based on mor-

phological features of biosignals in combination with

a fully-connected neural net. In addition to the con-

cept, we discussed and classiﬁed the strengths, weak-

nesses, threats and opportunities of the approach.

The next step is to implement the system itself.

In several stages, a procedure for determining blood

pressure with calibration is established. This is then

further developed into a calibration-free method. The

energy-efﬁcient implementation is the ﬁnal step. The

biggest challenges are data availability, transferability

and bias. Further work lies in ensuring the privacy of

the user while respecting the legal requirements and

the sensitivity of the health data collected.

ACKNOWLEDGEMENTS

This work was supported by the Fraunhofer Internal

Programmes under Grant No. SME 610 550.

REFERENCES

Buard, N., Campo, D., Ya, R., Techer, F., Barrochin, P.,

Pallas Areny, R., and Casas Piedraﬁta, J. O. (2016).

Weighing scale with extended functions. EU Patent

EP 3087914A1 by Withings.

Carek, A. M., Jung, H., and Inan, O. T. (2019). A reﬂective

photoplethysmogram array and channel selection al-

A Calibration-free Blood Pressure Measurement on a Scale: Concept and Challenges

213

gorithm for weighing scale based blood pressure mea-

surement. IEEE Sensors Journal, 20(7):3849–3858.

European Commission (2021). 22 % of people in

the eu have high blood pressure. https://ec.

europa.eu/eurostat/de/web/products-eurostat-news/-/

edn-20210929-1.

Fan, X., Ye, Q., Yang, X., and Choudhury, S. D. (2020).

Robust blood pressure estimation using an rgb cam-

era. Journal of Ambient Intelligence and Humanized

Computing, 11(11):4329–4336.

Fraunhofer IMS (2021). AIfES. https://github.com/

Fraunhofer-IMS/AIfES for Arduino.

Jeong, I. C. and Finkelstein, J. (2016). Introducing con-

tactless blood pressure assessment using a high speed

video camera. Journal of medical systems, 40(4):77.

Johnson, A. E., Pollard, T. J., Shen, L., Li-Wei, H. L., Feng,

M., Ghassemi, M., Moody, B., Szolovits, P., Celi,

L. A., and Mark, R. G. (2016). Mimic-iii, a freely ac-

cessible critical care database. Scientiﬁc data, 3(1):1–

Kachuee, M., Kiani, M. M., Mohammadzade, H., and Sha-

bany, M. (2017). Cufﬂess blood pressure estima-

tion algorithms for continuous health-care monitor-

ing. IEEE transactions on bio-medical engineering,

64(4):859–869.

Kim, J. Y., Cho, B. H., Im, S. M., Jeon, M. J., Kim, I. Y., and

Kim, S. I. (2006). Comparative study on artiﬁcial neu-

ral network with multiple regressions for continuous

estimation of blood pressure. In 2005 IEEE Engineer-

ing in Medicine and Biology 27th Annual Conference,

pages 6942–6945. IEEE.

Lin, W.-H., Wang, H., Samuel, O. W., and Li, G. (2017).

Using a new ppg indicator to increase the accuracy of

ptt-based continuous cufﬂess blood pressure estima-

tion. In 2017 39th Annual International Conference

of the IEEE Engineering in Medicine and Biology So-

ciety (EMBC), pages 738–741. IEEE.

Lu, Q. and Dai, E. (2018). A new blood pressure estimation

method based on neural network algorithm model. In

IOP Conference Series: Materials Science and Engi-

neering, volume 382, page 052027. IOP Publishing.

Luo, H., Yang, D., Barszczyk, A., Vempala, N., Wei, J., Wu,

S. J., Zheng, P. P., Fu, G., Lee, K., and Feng, Z.-P.

(2019). Smartphone-based blood pressure measure-

ment using transdermal optical imaging technology.

Circulation. Cardiovascular imaging, 12(8):e008857.

Martin, S. L.-O., Carek, A. M., Kim, C.-S., Ashouri, H.,

Inan, O. T., Hahn, J.-O., and Mukkamala, R. (2016).

Weighing scale-based pulse transit time is a superior

marker of blood pressure than conventional pulse ar-

rival time. Scientiﬁc reports, 6(1):1–8.

Murakami, K., Yoshioka, M., and Ozawa, J. (52015). Non-

contact pulse transit time measurement using imaging

camera, and its relation to blood pressure. In 2015

14th IAPR International Conference on Machine Vi-

sion Applications (MVA), pages 414–417. IEEE.

Nakano, K., Ohnishi, T., Nishidate, I., and Haneishi, H.

(27.01.2018 - 01.02.2018). Noncontact sphygmo-

manometer based on pulse-wave transit time between

the face and hand. In Cot

e, G. L., editor, Optical Diag-

nostics and Sensing XVIII: Toward Point-of-Care Di-

agnostics, page 34. SPIE.

Oreggia, D., Guarino, S., Parisi, A., Pernice, R., Adamo,

G., Mistretta, L., Di Buono, P., Fallica, G., Ferla, G.,

Cino, A., et al. (2015). Physiological parameters mea-

surements in a cardiac cycle via a combo ppg-ecg sys-

tem. In 2015 AEIT International Annual Conference

(AEIT), pages 1–6. IEEE.

Paviglianiti, A., Randazzo, V., Pasero, E., and Vallan, A.

(52020). Noninvasive arterial blood pressure estima-

tion using abpnet and vital-ecg. In 2020 IEEE Interna-

tional Instrumentation and Measurement Technology

Conference (I2MTC), pages 1–5. IEEE.

Pielmus, A.-G., M

uhlstef, J., Bresch, E., Glos, M., Jungen,

C., Mieke, S., Orglmeister, R., Schulze, A., Stender,

B., Voigt, V., et al. (2021). Surrogate based continuous

noninvasive blood pressure measurement. Biomedi-

cal Engineering/Biomedizinische Technik, 66(3):231–

245.

Rundo, F., Ortis, A., Battiato, S., and Conoci, S. (2018).

Advanced bio-inspired system for noninvasive cuff-

less blood pressure estimation from physiological sig-

nal analysis. Computation, 6(3):46.

Saeed, M., Lieu, C., Raber, G., and Mark, R. G. (2002).

Mimic ii: a massive temporal icu patient database to

support research in intelligent patient monitoring. In

Computers in cardiology, pages 641–644. IEEE.

Shin, J. H. and Park, K. S. (2012). Hrv analysis and blood

pressure monitoring on weighing scale using bcg. In

2012 Annual International Conference of the IEEE

Engineering in Medicine and Biology Society, pages

3789–3792. IEEE.

Singla, M., Sistla, P., and Azeemuddin, S. (2019). Cuff-less

blood pressure measurement using supplementary ecg

and ppg features extracted through wavelet transfor-

mation. In 2019 41st Annual International Conference

of the IEEE Engineering in Medicine and Biology So-

ciety (EMBC), pages 4628–4631. IEEE.

Sun, S., Bezemer, R., Long, X., Muehlsteff, J., and Aarts,

R. (2016). Systolic blood pressure estimation using

ppg and ecg during physical exercise. Physiological

measurement, 37(12):2154.

Xing, X. and Sun, M. (2016). Optical blood pressure esti-

mation with photoplethysmography and fft-based neu-

ral networks. Biomedical optics express, 7(8):3007–

3020.

Yan, C., Li, Z., Zhao, W., Hu, J., Jia, D., Wang, H., and

You, T. (2019). Novel deep convolutional neural net-

work for cuff-less blood pressure measurement using

ecg and ppg signals. Annual International Conference

of the IEEE Engineering in Medicine and Biology So-

ciety. IEEE Engineering in Medicine and Biology So-

ciety. Annual International Conference, 2019:1917–

1920.

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