WEARABLE BLOOD PRESSURE MONITORING SYSTEM

Case Study of Multiplatform Applications for Medical Use

Maxime Labat, Guillaume Lopez, Masaki Shuzo, Ichiro Yamada

The University of Tokyo, School of Engineering, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Yasushi Imai, Shintaro Yanagimoto

The University of Tokyo Hospital, Department of Internal Medicine, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan

Keywords:

Wearable physiological sensors, Non-invasive blood pressure monitoring, Mobile healthcare device.

Abstract:

Blood pressure measurement methods used nowadays have considerable drawbacks, as non-invasive measure-

ments are non-continuous while invasive measurements are conﬁned to in-hospital use. In this paper, we ex-

pose our solution of a continuous, non-invasive blood pressure measurement method, using electrocardiogram

(ECG) and photopletysmograph (PPG) as a basis of calculation. We present the two applications we designed

in order to collect, process, display and monitor the gathered information accurately. A mobile application,

using a smartphone connected to a sensor data logging device, is in charge of controlling data acquisition from

wearable sensors, displaying general information and signals for real-time monitoring. A desktop application

is designed to perform more detailed processing and complex analysis on the recorded data and is therefore

aimed at doctors and/or researchers.

1 INTRODUCTION

In an attempt to prevent frequent lifestyle-related dis-

eases, the authorities in charge of health in various

countries are seeking efﬁcient ways to detect this kind

of problems at an early stage. One of the possible so-

lutions is to develop new wearable instruments able

to measure or monitor physiological data during daily

activities. Since blood pressure (BP) is a good indi-

cator for potential cardiovascular disorders and circu-

latory system diseases, it requires constant attention

and an efﬁcient monitoring system.

The methods of measuring blood pressure used

nowadays are usually of two types :

• Though Ambulatory Blood Pressure Monitoring

(ABPM) devices are non-invasive and designed

for home use, they suffer from the non-continuity

of their data. Indeed, blood pressure measurement

with an ABPM device takes dozens of seconds,

and can only be repeated every two minutes at

most. In addition, it is rather uncomfortable and

as it requires users keeping their arm in a deﬁned

position, it tends to be inappropriate for use dur-

ing effort and most daily activities.

• Arterial blood pressure measurement method al-

lows continuous measures and has reliable results,

but is not usable at home and does not allow any

movement, making it unsuitable for most applica-

tions. Furthermore, it introduces a signiﬁcant risk

of infection and injury.

In this context, previous works lead to other ways

of monitoring blood pressure continuously, but with

non-invasive methods such as a calculation based on

pulse and ECG data and using the Pulse Arrival Time

(PAT) value (Espina et al., 2008; Fung et al., 2004;

McCombie et al., 2007). So far, we have developed

and evaluated a new method to estimate accurately

blood pressure from PAT even during physical exer-

cises (Lopez et al., 2010). Based on the encourag-

ing experimental results of our new method, we have

been developing a wearable device capable of record-

ing physiological signals, processing them immedi-

ately to estimate beat to beat blood pressure, trans-

forming it into context adapted understandable infor-

mation, and returning feedback to its user (a patient

or a doctor, for example), while saving it for further

detailed analysis by other potential users (such as doc-

tors or researchers).

The objective of our research is to use this device

156

Labat M., Lopez G., Shuzo M., Yamada I., Imai Y. and Yanagimoto S..

WEARABLE BLOOD PRESSURE MONITORING SYSTEM - Case Study of Multiplatform Applications for Medical Use.

DOI: 10.5220/0003131301560163

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 156-163

ISBN: 978-989-8425-34-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

in combination with applications targeted at speciﬁc

users, and be able to monitor blood pressure in a non-

invasive, continuous way both for medical and daily

life use.

This paper is organized as follows; section 2 in-

troduces our system, explains its target and speciﬁca-

tions and how it differs from usual BP measurement

devices. Section 3 gives more information about the

applications designed to log and analyze the data from

the sensors. Section 4 explains how we adapt the user

interface of our mobile device so that it gives immedi-

ate feedback to its user. Section 5 mentions encoun-

tered issues and improvements ideas that will be re-

alized in a near future and will most probably give a

second dimension to this project.

2 SYSTEM OVERVIEW

In this section, we will try to give a precise, yet con-

cise explanation of this system’s design and the dif-

ferent features it has.

2.1 Design Concept

Our system has been designed as a complete solution

to keep track of blood pressure, from the end-user

(the monitored person) to the people able to under-

stand and process this data (usually doctors following

their patients or researchers trying to gather data for a

study). Using this approach, we decided to create two

distinct applications:

• a mobile application, gathering data from wear-

able sensors, processing this data using simple al-

gorithms, giving immediate feedback to its user

and saving the data to a memory card;

• a desktop application, using the data recorded

earlier for processing using more advanced algo-

rithms and calculation techniques.

2.2 Mobile Device and Sensors

Prototype

To fulﬁl the task of recording physiological data from

sensors, we used a device able to transform analog

ECG and PPG signals into digital data, then store

these data on a memory card while updating the user

interface, in our case an iPod touch

with a dedicated

application.

Conceived so that the iPod touch can be docked

on it, the device communicates with the latter through

iPod touch is a trademark of Apple Inc., registered in

the U.S. and other countries.

Figure 1: Overview of the mobile device.

the serial line of the Dock port. It includes an orig-

inal ECG and PPG sensor unit, capable of recording

data at a 1 kHz sampling rate, necessary condition to

ensure accurate estimation of high blood pressure val-

ues as during physical exercise for example. This unit

has the advantage of being able to synchronize pulse

and ECG signals, ensuring that the time difference be-

tween characteristic points of these signals is correct

with a millisecond accuracy.

The sensors used in our device and shown on ﬁg-

ure 1 are the following:

• ECG signal is captured using a 3-electrode set-up,

each electrode having a distinctive color (Positive,

Negative and Earth electrodes) and connected to

the device through 3 wires, guaranteeing better ro-

bustness to motion artefacts;

• PPG is captured using an ear lobe sensor, con-

nected to the device using one wire; this sensor

measures pulse wave by transmitted-light plethys-

mography principle and using the ear lobe makes

the signal less prone to noise induced by body mo-

tion than frequently used ﬁnger PPG sensor (Mc-

Combie et al., 2008; Muehlsteff et al., 2008).

• acceleration values are captured using a 3-axis ac-

celerometer, again connected to our device using

one wire. Combined to motion recognition algo-

rithms it will enable detecting which activity trig-

gers blood pressure augmentation and which do

not have any effect.

The device itself has a microSD card module, to

which are logged ﬁles containing the sensor data dur-

ing acquisition and can also be used to write other

related ﬁles directly from the iPod touch.

Finally, the prototype has a three-states power

management switch. It is able to chargethe connected

iPod using the embedded battery on one position.

The two other positions are used to select whether

the micro-controller in charge of collecting the sen-

sor data and converting them to their digital format

WEARABLE BLOOD PRESSURE MONITORING SYSTEM - Case Study of Multiplatform Applications for Medical Use

157

will be powered on either manually or through mes-

sages sent on the serial line. This latter option adds

measure control ﬂexibility and saves power when the

device is not acquiring data.

These features make our device different from

other non-invasive BP monitors, as seen when com-

paring the number 1 selling blood pressure monitor in

the U.S., Omron HEM-711AC with our blood pres-

sure monitoring prototype (see table 1).

Table 1: Characteristics comparison between our device

prototype and Omron HEM-711AC.

Our prototype HEM-711AC

Timing Continuous Punctual

Signals Acceleration, Pulse rate

ECG, PPG, HR, BP BP

Sampling ECG, PPG: 1 kHz 1 BP reading

rate HR, BP: 1 beat per 2-3 min.

Weight 250 grams 355 grams

Battery 2 consecutive 1500 readings w/

life hours ’AA’ battery x 4

Memory 4 days 60 readings

Our aim is not only monitoring these physiologi-

cal data, but also being able to prevent any related dis-

order. The design of this device reﬂects this objective,

by keeping the size of the unit as small as possible and

combining it with an easily adaptable terminal.

3 DATA LOGGING AND

ANALYSIS APPLICATIONS

The mobile application is used as a data accumula-

tion application and as a controller for the recording

device. When the data is saved, it can be used by

other applications. The desktop software we designed

is able to process the data and apply various analysis

algorithms.

3.1 Mobile Measurement Control

Application

The Commands tab is the ﬁrst screen you see when

starting the mobile application, and is the ﬁrst of three

function tabs (ﬁgure 2). While the two other tabs

are aimed at visualizing the recorded data, this tab is

mainly used to issue commands to the data recording

device and manage the data ﬁles from previous acqui-

sitions.

The Commands tab is separated into two parts:

one is called WBMD mode and the other is microSD

mode. Both involve interaction with the logging

device, but while the former is used to trigger and

stop acquisition of data, the latter is used for ﬁle man-

agement on the microSD card. The choice between

these two modes is done by triggering a mechanical

switch on the logging device. Two other modes are

available, independently from the previous ones: one

is called normal mode, and is used when the logging

device’s power is controlled by the iPod touch; the

second one, manual mode, is used when the device’s

power is activated manually via a mechanical switch.

Figure 2: The Commands tab.

In WBMD mode, it is possible to start an acquisi-

tion campaign, which can be deﬁned as:

• a single or multiple measurements, each one last-

ing a certain time span;

• in case several measurements are performed, they

are repeated at regular intervals, and stop after the

designated overall duration.

The uSD mode operations involve ﬁle manage-

ment on the device’s memory card. These operations

are mainly:

• writing patient proﬁle information, making each

measurement correspond to a patient (see section

3.2 for more information);

• managing ﬁles on the memory card, i.e. deleting

unnecessary ﬁles or performing a backup of these

ﬁles on the iPod’s internal memory.

3.2 Settings Menu and Patient Proﬁle

As the device can be used in several different situa-

tions, a settings menu was added to enable the user

specifying the measurement parameters easily.

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158

Through this screen, the user can choose a set of

sensors to enable or disable, depending on the type

of information he needs; measurement duration and

repetition options are also available.

Finally, as the data ﬁles recorded on the device

only contain physiological data, it was necessary to

make each measurement correspond to a patient pro-

ﬁle, so that each set of data can be identiﬁed and clas-

siﬁed accordingly to patient characteristics.

In order to achieve this goal, an interface designed

to specify crucial patient information was added in the

Settings menu (ﬁgure 3). Each measurement will trig-

ger the creation of an associated patient proﬁle con-

taining the information ﬁlled in in this interface. They

are then written on the microSD card, using the pro-

cess mentioned in section 3.1.

Figure 3: The Settings and Patient data menus.

Thus, when the ID contained inside the patient

proﬁle is compared to the database of the doctor, it

can be used for diagnostic, whereas the same data

used by a researcher can just give an outline of the

patient without revealing his identity and can be used

for data mining purposes.

3.3 Ofﬂine BP Analysis Application

The desktop application is a way for doctors and re-

searchers to process and review the data recorded

by the mobile device. As our mobile device lacks

some processing power for complex algorithms, and

its screen is not really adapted for reviewing large

data, we prepared an application executable on a per-

sonal computer which is able to import ﬁles and apply

algorithms on them.

In order to facilitate the programming and make the

use of the application easier, we decided to use Qt

framework for interface. Indeed, it is free and cross-

platform. Qt also offers the Qt Creator IDE which

makes interface design quicker and has good perfor-

mance components.

3.3.1 PPG and ECG Signal Display and Analysis

Before the calculation of blood pressure, the ECG

and PPG signals have to undergo some ﬁltering al-

gorithms. Although the data logging device already

includes some band-pass ﬁlters, other noises can ap-

pear on the signals (for instance, noise due to power

lines). We chose to use a simple Fast Fourier Trans-

form (FFT) ﬁlter, for its good computing speed and

zero-phase ﬁltering properties. Once this ﬁltering step

is cleared, the signal is processed by the blood pres-

sure algorithm designed to determine the ECG peaks

and the PPG foot points.

Figure 4 shows the screen displaying the ﬁltered

ECG and PPG graphs with additional marks pinpoint-

ing the results of the blood pressure algorithm calcu-

lation. A zooming function can also help a doctor to

identify eventual cardiac troubles, such as arrhythmia.

It can also help to correct eventual calculation errors

manually.

Figure 4: The ﬁltered ECG and pulse graphs with features

detection result.

3.3.2 Heart Rate Analysis

By determining the position of ECG peaks, it is pos-

sible to calculate heart rate values accurately. In-

deed, heart rate is an interesting indicator for both un-

healthy patients, for example with cardiac disorders,

and healthy people.

Qt is a trademark of Nokia Corporation in Finland

and/or other countries worldwide.

WEARABLE BLOOD PRESSURE MONITORING SYSTEM - Case Study of Multiplatform Applications for Medical Use

159

Additionally, former studies showed that psycho-

logical state changes are reﬂected in the heart rate

variability (HRV) and can be successfully monitored

(Itao et al., 2008). Based on these observations, we

added two graphs showing heart rate and its variations

over time (ﬁgure 5).

Figure 5: The heart rate and HRV visualization.

3.3.3 Blood Pressure Visualization

As our main objective is to monitor blood pressure

efﬁciently, the top feature of the application is to cal-

culate blood pressure on a chosen set of ﬁles, and dis-

play to the user a graph of the computed blood pres-

sure, such as shown on ﬁgure 6. Before displaying the

graph, the application detects obviously incorrect val-

ues and removes them. A moving average algorithm

is then used in order to smoothen the curve and make

the trend variation analysis easier.

Figure 6: Continuous Blood Pressure variation visualiza-

tion.

The parameters used to calculate blood pressure

vary according to various conditions such as blood

vessel thickness, blood ﬂuidity, etc. (Lopez et al.,

2010), it is possible to input reported conventionalBP

results (such as ABPM) into the interface and change

the calculation parameters according to the conven-

tional BP variations.

The aim is mainly to do an initial adjustment to

realize a calibration of the device by determining the

optimal parameters for blood pressure calculation for

each individual.

Once these parameters adjustments are done, the

blood pressure algorithm results seem satisfying, as

the trend of computational blood pressure follows this

of ABPM.

4 REAL-TIME BP MONITORING

In addition to accumulating data for further use, the

mobile application is also used for real-time monitor-

ing. It is therefore possible to get feedback from the

device immediately, by displaying ECG and PPG sig-

nals as well as preliminary results from BP algorithm.

4.1 Quick View of Measured ECG and

PPG

When the acquisition of data starts, the logging device

sends data to the iPod touch so that it can be processed

and displayed to the user. The Graphs tab is a good

way to monitor the current input from the sensors and

see important information at a glance.

This tab features two graphs displaying the raw

values from ECG and pulse signals, so that the user

can verify that the signals have satisfying shapes and

amplitudes for our algorithms to be effective (if it is

not the case, a warning message appears on the graphs

area). These graphs can help adjusting the position of

the sensor for better performance. In addition to that,

users can see their current Heart Rate (HR) and Blood

Pressure (BP) values (both being frequently used by

doctors) as they are calculated using the ECG and

pulse data.

A second-precision clock is also present in order

to synchronize blood pressure variations with even-

tual external events which have no input in our system

(contextual data).

4.2 Short and Long-term Variations

As explained in the previous section, the current

blood pressure is shown on the Graphs tab with a sin-

gle numerical value (ﬁgure 7). In order to detect short

or long-term trends in the blood pressure values, it is

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necessary to represent the blood pressure as graphs.

The BP tab is designed to address this issue.

Figure 7: The Graphs tab.

Two graphs, as shown on ﬁgure 8, are available:

• the short-term graph, displaying only the last 30

seconds of data, can be used to monitor very sud-

den and punctual variation in blood pressure - this

can happen frequently when switching from a lay-

ing position to a standing position for example;

• the long-term graph, displaying the last 10 min-

utes of data, can be used to monitor increasing or

decreasing trends of blood pressure due to other

phenomena, such as sustained effort or use of an-

tihypertensive medications.

In addition, two modes of graph are available: the

”spike” graph shows all raw calculations of blood

pressure, whereas ”moving average” makes trends

more readable by smoothing the curves using a simple

moving average algorithm.

5 ISSUES AND FUTURE

DEVELOPMENTS

This project was designed as a ﬁrst ”proof-of-

concept” to ensure this method of non-invasive blood

pressure measurement was fairly reliable and that

such system could replace actual monitoring devices.

Our ﬁrst experiments seem to validate this assump-

tion. But to go further, some new developments and

features are considered and will unquestionably im-

prove the general response to this system.

Figure 8: The BP tab.

5.1 Network-related Improvements

More than a simple monitoring device, using a mo-

bile application on a smartphone or equivalent has the

advantage of being able to communicate with other

computers through wireless connections. Other de-

vices can be used for monitoring blood pressure, such

as simple micro-controllers (Isais et al., 2003), but

it is possible to make good use of the almost unlim-

ited features and good processing power of the present

smartphones in an health-care context.

One considered feature is to send, directly from

the mobile system, data ﬁles to a health-related

database or application server, in charge of storing

them and, eventually, apply algorithms on them. The

application server would then be able to display in-

formation to interested parties (for the patient, reports

based on the monitored data; for doctors, to be able

to follow up their patient easily without even seeing

them; for researchers, to have complete anonymous

health proﬁles to study)(Lopez et al., 2009).

This networking ability will probably change

drastically the way the current system works: instead

of having the ”complete” proﬁle of a patient written

on the SD card next to the ﬁles, the opposite oper-

ation of sending raw data to the database by relying

on the ID of the patient would be more appropriate.

Thus, each patient would have its own access to the

database to upload his ﬁles, so that they are available

to doctors and researchers who can issue reports of

their diagnostic in return. The existence of the SD

card itself is also questionable, as the data can be sent

in real time to the application server or saved in the

WEARABLE BLOOD PRESSURE MONITORING SYSTEM - Case Study of Multiplatform Applications for Medical Use

161

smartphone’s memory in case of a network failure.

Such a server would be designed to accept multi-

ple health-related informations and can therefore be

used not only as a blood pressure diagnostic tool but

as a whole health analysis helper, preventing a large

set of lifestyle-related disorders.

5.2 Blood Pressure Calculation

Methods

As mentioned in section 3.3.3, the blood pressure

calculation method relies on multiple parameters de-

pending on various physiological factors. As some

of these physiological factors cannot be determined

without thorough examinations, it is necessary to start

from so-called ”reference” parameters issued by com-

putational or empirical methods.

Our goal is, using various data from initial exper-

iments, to determine these reference parameters and

link them to certain patient proﬁles.

For that intent we are currently conducting exper-

iments at the University of Tokyo Hospital. Through

these experiments, we hope to ﬁnd empirical param-

eters for determined age and sex groups, in order to

make our device usable out-of-the-box.

If the results of these experiments, which will be

the subject of a future study, prove this approach to

be unsuccessful, an alternative solution will be set.

This alternative solution may simply consist in mak-

ing a unique initial calibration using ABPM values

measured during a classical cycle-ergometer load test

for each patient (Lopez et al., 2010).

5.3 Use During Daily Activities

Until now, our system’s design is mainly being tested

in an hospital environment as a blood pressure and

heart rate monitoring tool, replacing the system cur-

rently used for this purpose. But the small size of

the wearable device and its adaptation to a popular

and ordinary smartphone makes it potentially usable

for home use, and moreover for use during daily ac-

tivities. Indeed, daily activities have an impact on

blood pressure. Continuous blood pressure measure-

ment can reveal sudden blood pressure variations im-

possible to detect using ABPM.

On top of PPG and ECG sensors, our wearable de-

vice also features a 3-axis acceleration sensor. Using

a movement recognition algorithm detecting which

activity is being done based on the acceleration data,

it is possible to detect which activity triggers blood

pressure augmentation and which do not have any ef-

fect (ﬁgure 9).

Figure 9: Daily activities blood pressure evolution.

5.4 Sensors and Mobile Device Issues

Our preliminary tests using our current mobile proto-

type shed light on some difﬁculties the user can expe-

rience while using this device.

One of the main concern is the weight and the

way to wear this device. Indeed, our prototype fea-

tures a battery and consequent electronics, making

the lightweight iPod touch very bulky. Wearing it as

a necklace proved to be very uncomfortable, and el-

derly people seem to consider it a very large burden.

Furthermore, all sensors are connected through

wires. When the sensors are wore under clothes, the

wires can hamper body movements or provoke alter-

ations of the sensors’ positions, with a consequentrisk

of generating various noises.

We are therefore working on some new prototype

clearing these issues, by introducing wireless wear-

able sensors as suggested by former studies (Kang

et al., 2006), and a lightweight smartphone module

to collect the data.

6 CONCLUSIONS

The system presented in this paper covers all types of

users (patient, doctors and/or researchers) and the use

of a mobile terminal such as a smartphone does not

limit the device to the features found on conventional

ABPM devices. Moreover, it is efﬁcient enough to

validate the calculation model. The ﬁrst experiments

conducted suggest that our wearable blood pressure

monitoring device using PAT method returns reliable

results, and the system is relatively lightweight so that

it used during daily activities. Therefore, if further ex-

periments realized using this system prove it to have

reliable and constant results, and if it is improved so

that the results can be easily sent to a network content

managing server, this system could be a great step for

HEALTHINF 2011 - International Conference on Health Informatics

162

continuous blood pressure monitoring, be it at home

or for an hospital use.

ACKNOWLEDGEMENTS

This research was supported by the research fund for

Core Research for Evolutional Science and Technol-

ogy (CREST), granted by Japan Science and Technol-

ogy agency (JST).

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