A Real-time m-Health Monitoring System: An Integrated Solution

Combining the Use of Several Wearable Sensors and Mobile Devices

Salvatore Naddeo

, Laura Verde

, Manolo Forastiere

, Giuseppe De Pietro

and Giovanna Sannino

Institute of High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR),

via Pietro Castellino 111, 80131 Naples, Italy

Department of Technology, University of Naples Parthenope, 80143 Naples, Italy

Neatec S.p.a. R&D Support, Via Campi Flegrei 34, 80078 Pozzuoli, Italy

Keywords:

Physiological Monitoring, Wearable Device, m-Health System, Continua Alliance Guidelines, Chronic

Diseases.

Abstract:

Nowadays the upsurge in the prevalence of chronic diseases represents an increasing burden on individuals

and society. Chronic diseases can require repeated and frequent hospital treatment to control vital parameters

of interest. The use of automatic instruments for a real-time monitoring of biological parameters constitutes

a valid instrument to improve the patient’s quality of life. The integration of mobile communications with

wearable devices has facilitated the shift of healthcare assistance from clinic-centric to patient-centric mon-

itoring. In this paper, a real-time monitoring system is proposed. The system is conceptualized to provide

an instrument for patients, by means of which they can easily monitor, analyse and save their own vital signs

using wearable sensors and an Android device such as a smartphone or tablet, offering an efﬁcient solution in

terms of a decrease in time, human error and cost.

1 INTRODUCTION

During the recent decade the demographic changes in

developed countries resulting in a more elderly pop-

ulation and the increasing prevalence of chronic dis-

eases have contributed to the need for a constant mon-

itoring of the state of patients’ health.

According to the World Health Organization

(World Health Organization, 2016), chronic diseases

such as coronary heart disease, cancer, chronic ob-

structive pulmonary disease and diabetes mellitus

type 2 constitute the leading cause of mortality in the

world, representing about 60% of all deaths. Chronic

diseases are primarily attributable to heart failure, cur-

rently the main cause of death in most western coun-

tries. The 2016 Heart Disease and Stroke Statistics

update of the American Heart Association (AHA)

reported that 15.5 million people in the USA suf-

fer from cardiovascular disease, this prevalence in-

creasing with age for both women and men (Sanchis-

Gomar et al., 2016). In Europe, in the last 12 months

the prevalence of people reporting heart problems

was 9.2% for both sexes (Townsend et al., 2016).

In particular, in Italy cardiovascular diseases are the

main cause of death, responsible for 44% of all

deaths (Centro nazionale di epidemiologia, sorveg-

lianza e promozione della salute dell’Istituto superi-

ore di sanit

a, 2016).

Chronic diseases also have a negative impact on

the quality of people’s life. Patients suffering from

these pathologies must, often, carry out a monitor-

ing of physiological parameters such as heart rate and

blood pressure as well as take control of the main

risk factors that can aggravate their state of health. In

less dangerous cases, it is convenient to monitor pa-

tients outside the hospital. On the one hand,in fact,

such patients can face their illness in a family con-

text that helps to speed up their recovery time. On

the other, this strategy implies a considerable saving

of resources, allowing social health facilities and per-

sonnel to be assigned to patients with more severe dis-

eases.

Therefore, an efﬁcient solution for the monitor-

ing of a patients’ state of health is required, which

is able to collect, record and analyse vital signs, and

so support preventive care, diagnosis and rehabilita-

tion planning. Moreover, the automation of physio-

logical data capture and its visualisation on a device

Naddeo S., Verde L., Forastiere M., De Pietro G. and Sannino G.

A Real-time m-Health Monitoring System: An Integrated Solution Combining the Use of Several Wearable Sensors and Mobile Devices.

DOI: 10.5220/0006296105450552

reduces the risk of errors in manual harvesting. This

lower risk of error results in increased efﬁciency, re-

duced costs and improved vital qualities in an area

such as healthcare one where human error can really

make the difference between life and death. The most

innovative aspect of the adoption of a tele-monitoring

system is represented by the means by which patients

and healthcare professionals communicate and inter-

act. Patients are directly involved in managing their

health and wellness.

Mobile devices, such as smartphones or tablets,

constitute the perfect instrument to monitor the vital

parameters of a patient. Thanks to the use of appro-

priate wearable sensors, it is possible to collect and

analyse the data coming from these devices to moni-

tor the patient’s state of health.

In this work we present a real-time m-health mon-

itoring system for people suffering from chronic dis-

eases that enables the collection, sharing and ex-

change of physiological data, such as blood pressure,

heart and respiration rate and ECG signals. The sys-

tem is not limited only to the acquisition of such data

but also enables the analysis of vital signs. The phys-

iological data are captured using wearable systems

compliant and not to Continua Health Alliance guide-

lines (Carroll et al., 2007), one of the most respected

communication standardization protocol.

2 RELATED WORK

Mobile healthcare applications constitute an instru-

ment for individuals to keep track of their health con-

dition, to take more responsibility for their lifestyle

and to improve the efﬁciency of care by providing

high quality data to health professionals. Such med-

ical data are often acquired by means of the use of

wearable devices. Interest in the research and devel-

opment of smart wearable systems is increasing in

both the academic world and industry, to promote the

realization of devices that comply with the required

standards of effectiveness, interoperability, cost, pri-

vacy and ethics (Lmberis and Dittmar, 2007; Konstan-

tas, 2007; Chan et al., 2012). These systems are able

to measure vital signs, such as body and skin temper-

ature, heart rate, blood pressure, blood oxygen sat-

uration (SpO2), electrocardiograms (ECG), electron-

cephalograms (EEG) and respiration rate.

To implement an m-health solution a level of stan-

dardization is necessary to ensure an easy and quick

integration between the wearable device (e.g. a blood

pressure monitor or pulse-oximeter) and the gateway

device (e.g. a smartphone). The interoperability

among all devices that compose a telehealth system

is regulated by the guidelines of the Continua Health

Alliance.is necessary which describe a set of interna-

tionally established standards and frameworks to en-

sure interoperability of devices (Carroll et al., 2007).

Several examples of monitoring systems are de-

scribed in literature useful for the monitoring of phys-

iological data taken from wearable devices. Unfor-

tunately, not all the presented systems use devices

that follow the Continua Health Alliance guidelines.

Kakria et al. (Kakria et al., 2015), for example, pro-

pose a real-time monitoring system to collect data

about heart rate, blood pressure and body tempera-

ture using wearable devices non-compliant with the

Continua Health Alliance guidelines. The same lim-

itation applies to the device used by PhysioDroid

(Banos et al., 2014), a mobile system to monitor

heart rate, body temperature and respiration rate, and

to the system realized by Forastiere et al.(Forastiere

et al., 2016) for a monitoring of a patient’s own well-

ness. The proposed system, instead, supports devices

compatible with the Continua Health Alliance design

Guidelines. In addition, the open architecture of our

framework allows an integration with wearable de-

vices that use other communication and data access

protocols to provide a system that can monitor pa-

tients integrating data coming from several medical

devices, unlike the system proposed by (Park et al.,

2016). They develop an m-health application limit-

ing the integration to only certiﬁed devices. While,

Szydlo et al (Szydło and Konieczny, 2016) present a

data acquisition module, implemented as a mobile ap-

plication to integrate several medical devices, whether

or not compliant or not with the Continua Health Al-

liance guidelines, but without analysing the acquired

data.

Many mobile healthcare systems are aimed at

patients with cardiac diseases, diabetes, hyperten-

sion, or hyperthermia, limiting the acquisition and

monitoring to only one set of physiological data, and

sometimes providing an analysis such as (Gay and

Leijdekkers, 2007; Lv et al., 2010; Rodrigues et al.,

2014; Guo et al., 2013; Lee et al., 2016), Sense-

View (http://www.senseview.mobi/), and SimpleEye

(https://simpleeye.com/platforms/android/bluetooth-

pulse-oximeter/). Our proposed system, however,

is not limited to capturing and monitoring in real

time vital signs but also to allows data processing

and analysis locally, to better evaluate whether health

activities have been performed planned and to assess

whether the desired results are being achieved.

Moreover, in literature there are platforms that

collect the physiological data and send it to a care

center for processing and analysis. These solutions

do not process the medical data locally on mobile de-

Figure 1: System Structure.

vice, but the signal needs to be continuously trans-

ferred to a health center. For example, the Euro-

pean Union-funded research project AMON (Anliker

et al., 2004) has an objective realizing a wrist-worn

medical monitoring and alert system. The data are

transmitted via GSM to a medical center. Unfortu-

nately, for heart monitoring the system is technically

limited by the noisy ECG signal that is acquired on

the wrist through the realized device and is not ap-

propriate for the diagnosis of cardiac abnormalities.

The Epi-medics project (Rubel et al., 2005) is another

project for ECG monitoring that records and analyses

ECG signals and generates alarms. In this case, also,

the monitoring system uses its own sensor device, not

interfaced with other sensor devices. Bisio et al. (Bi-

sio et al., 2015), instead, proposed a platform for pa-

tients suffering from a heart failure, able to monitor

data coming from the pulse oximeter to measure the

saturation of peripheral oxygen and a weighting scale

to measure the body weight, using devices compliant

with the Continua Health Alliance standards. More-

over, Al-Taee et al.(Al-Taee et al., 2015) present a

platform to support the self-management of diabetes

using several medical sensors which are not compli-

ant the guidelines.

3 MONITORING SYSTEM

The proposed system is able to monitor several health

parameters using multiple sensors. The acquired

physiological data are processed and analysed locally

on a mobile device. A report of the analysis and the

original ﬁles are saved, can be visualized whenever

the user wants and are sent to the medical specialist.

The scheme in the ﬁgure 1 shows the structure of

the system, a set of interconnected blocks in which

the data ﬂows start from the patient and arrive at the

medical specialist. A patient can monitor his/her own

vital signs using an Android device, which is easy to

use and able to capture and store the data from the

wearable sensors. Once the acquisition has been com-

pleted, the Android device sends the data to a remote

Personal Health Record (PHR) server, which the doc-

tor can access to monitor the patient’s health state. In

the following section the system architecture is ex-

plained in detail.

3.1 System Architecture

The system has a modular structure, in which ev-

ery module performs a speciﬁc set of operations, as

shown in the ﬁgure 2.

The Health Device Proﬁle (HDP) module is re-

sponsible for the transmission of data from the wear-

able devices to the mobile devices using the Blue-

tooth protocol. Moreover, it is possible to connect

the proposed system with an external PHR, as shown

in the ﬁgure 1, and/or Decision Support Systems. In

the interaction with these systems, the adoption of

the Continua Alliance standard, using an appropri-

ate wrapper, allows a fast and accurate data trans-

lation in accordance with the two Health Level 7

standards (http://www.hl7.org/implement/standards):

Fast Healthcare Interoperability Resources (FHIR)

and Personal Healthcare Monitoring Report (PHMR).

The FHIR Standard (Bender and Sartipi, 2013) is use-

ful to represent and exchange information between

several types of monitoring devices, for a correct syn-

tactic and semantic interoperability and appropriate

efﬁciency between information systems and biomed-

ical devices. Moreover, in order to improve the

data dissertation in a tele-monitoring application, the

PHMR (Wolf et al., 2009) was introduced, which is

able to translate personal healthcare monitoring infor-

mation into electronic medical record systems includ-

ing a representation of the measurements captured.

The data, that are received by the HDP module,

will be forwarded to the other modules connected

with it, the Storage module and the Signal Analysis

module. The ﬁrst one is used to save physiological

data and a patient’s analysis report in the device in-

Figure 2: System Architecture.

ternal memory. SQLite database is used to store mea-

surement data acquired from the mobile health device

and/or data from manual measurement that the patient

performs with devices not equipped with a Bluetooth

connection. In the ﬁrst case, the Storage Module re-

ceives data from the HDP Module, while in the sec-

ond the data are directly inserted by the user. Col-

lected and analysed data, during the monitoring, con-

stitute the Personal Health Record (PHR) of the pa-

tient (Tang et al., 2006). To access the data contained

in the PHR the doctor, through appropriate devices,

must interact with a remote server to visualize any de-

sired data. This operation is fundamental when there

are anomalies that generate an alert. In this situa-

tion the specialist visualizes all the patient’s measure-

ments and can decide to change the therapy, perform

in depth examinations or modify the monitoring plan.

The monitored data, besides being saved, can be

analyzed, using the Signal Analysis Module. In de-

tail,such analysis can include:

• Filtering data to remove the additive noise that

can distort the signal and the resulting interpre-

tation. It is possible to choose between four types

of ﬁlter: high-pass and low-pass (Chen and Chen,

2003), recursive (Cuomo et al., 2016) and Han-

ning ﬁlters (Verde et al., 2015). The implemented

ﬁlters are characterized by appropriate parameters

such as the cut-off frequency that the user can set

indicating the desirable value;

• Data processing, whereby the signals can be anal-

ysed to extract characteristic parameters, useful to

evaluate the patient’s health state.

Data obtained from the HDP and Signal Analy-

sis modules are sent to the DSS Module. This mod-

ule constitutes the Decision Support System, apable

of supporting and improving the real-time monitoring

and analysis using ”the medical specialist’s knowl-

edge”. The data are, in fact, compared with selected

threshold values to check the patient’s state of health.

If the module discovers a warning situation, it asks

the Report Module to generate a message to be sent

to the doctor via the Notiﬁcation Module.

All acquired data and analyses are saved in the ap-

propriate reports, realized by the Report Module. The

system saves a report with all measurement data once

a day, which is then sent to the medical specialist at

the time scheduled by the user. Moreover, the sys-

tem can create a warning report message, attaching

the report ﬁles, if an analysis of the data using the

DSS Module reveals an abnormal situation in the pa-

tient’s state of health.

The Notiﬁcation Module is the part of the software

that is responsible for notifying messages generated

by the system to the doctor and the patient. The mes-

sages can be of two types:

• Alerts, that instructs the user to perform the de-

sired measurements at scheduled time;

• E-mail messages, sent to the medical specialist in-

dicated by the user. Such messages include any

report of the patient’s measurements.

Finally, the Visualization Module is the interface

between the system and the users. It shows the real

time measurements acquired from the wearable de-

vices, the analysis data and the report ﬁles, whenever

the user chooses.

4 USE-CASE

The realized monitoring system can be used with pa-

tients suffering from cardiovascular diseases. These

patients have to constantly monitor characteristic phi-

siological data, such as blood pressure and heart rate.

Therefore, they must report these data, very often col-

lected manually over several days, to their medical

specialists during rountine follow-up visits.

The manual collection of physiological data can

increase the risk of errors, and the recording of erro-

neous values can change the evaluation of patient’s

state of health. To avoid this problem it is possible

to automate the process for capturing and monitor-

ing vital signs, as performed by the proposed system.

Moreover, this system allows the patients to monitor

their own physiological data at home using appropri-

ate wearable devices without the necessity of going to

a hospital center, providing a complete clinical picture

to the medical specialist.

The fundamental vital signs, in order to perform

a monitoring of a patient suffering from cardiovascu-

lar disease, are the heart rate, respiration rate, ECG

signal, blood pressure and oxygen saturation. There-

fore, the patient, using an appropriate wereable de-

vice, such as Zephyr Bioharness BH3, A&D Medical

UA-767PBT-C and Nonin 9560 Onyx 2 (described

in the following subsection), can capture and mon-

itor the relevant parameters, by means of a mobile

device, such as a smartphone or tablet, on which the

tele-monitoring application has been installed.

4.1 Medical Devices

The measuring accuracy of the sensor has a direct im-

pact on the accuracy of the heart parameter measure-

ment in real-time monitoring systems. Therefore, the

selection of an accurate heart parameter monitoring

device plays an important role in the early detection

of any possible heart disease.

Zephyr Bioharness BH3 is a multisensory belt

used to monitor heart rate, breathing rate and posture

in real-time. The selection of the Zephyr Bioharness

BH3 device was made on the basis of the accuracy of

the acquired physiological data, low cost and comfort

for patient comfort. This instrument is able to cap-

ture the vital signs in three different ways: with an

elastic belt in which the sensors are integrated, with a

shirt with the sensors integrated inside or with a and

holder connected with common cardiac electrodes, as

shown in the ﬁgure 3.

The second measurement parameter in our mon-

itoring system is the evaluation of blood pressure.

Blood pressure is an index of the force of the blood

Figure 3: Zephyr Bioharness BH3.

pushing against the walls of the arteries as the heart

pumps blood. It is measured as both the pressure

when the heart beats pump blood (the systolic pres-

sure) and the pressure when the heart is at rest be-

tween beats (the diastolic pressure). To monitor the

patient’s blood pressure the realized system uses the

A&D Medical UA-767PBT-C blood pressure monitor.

An acute rate of these parameters is considered as

an early indicator which supports medical specialists

in the diagnosis of serious diseases. Additionally, the

blood oxygen saturation and pulse rate can be indices

of possible alterations, monitored in our system by us-

ing the Nonin 9560 Onyx 2. The blood pressure mon-

itor and the pulse oximeter are illustred in the ﬁgure

Figure 4: A&D Medical UA-767PBT-C and Nonin 9560

Onyx 2.

These three devices uses the Bluetooth protocol to

communicate with the monitoring software installed

on the Android device. The communication between

the realized system and the Nonin 9560 Onyx 2 and

the UA-767PBT-C follows the Continua Health Al-

liance guidelines. Although the Zephyr Bioharness

BH3 is not certiﬁed for this standard, a dedicated li-

brary is available enabling its connection with the mo-

bile devices.

4.2 Mobile Application

The system was implemented for mobile devices,

such as a smartphone or tablet, developed by using

the Java Programming Language through the use of

Eclipse IDE and the Android Software Developer Kit

(SDK). The mobile system offers several functionali-

ties. At the ﬁrst access the user must complete a Reg-

istration form, in which he/she inserts personal infor-

mation such as name, surname, date of birth, gender,

email address. In detail, the e-mail address is both

that of the user and that of the medical specialist who

will receive the user’s report containing the measure-

ment of the physiological data and the analysis of the

estimated values. Such an e-mail will be sent at the

time indicated in the registration phase.

After the registration, the user can choose any of

the operations in the main menu shown in the screen-

shot in the ﬁgure 5.

Figure 5: Screenshots of the Homepage and History of

Measurement.

In detail, the user can perform the following func-

tionalities:

• Search Bluethooth Devices: the system searches

for Bluethooth devices and shows them to the

user. The user can choose to collect his/her pre-

ferred wearable devices able to execute different

the measurements and analyses. If the user con-

nects to the Zephyr Bioharness BH3, for example,

he/she can monitor in real-time physiological data

such as the heart and breath rate or the ECG sig-

nal, as shown in the ﬁgure 6.

Figure 6: Screenshot of the ECG monitoring.

During the signal capture, noise can add to the

useful signal distorting its interpretation and the

resulting diagnosis. To avoid this problem the user

can ﬁlter the captured signal choosing a ﬁlter of-

fered by our system, described in section 3.1. In

addition, it is possible to extract the characteristic

parameters of the Heart Variability Index (HRV).

The HRV describes the variations of both the in-

stantaneous heart rate and RR intervals, useful to

evaluate the heart state and any possible anoma-

lies due to the presence of arrhythmias (Townsend

et al., 2016);

• Speciﬁcations: information about cardiac dis-

eases and healthy lifestyles is provided, useful for

the prevention of these disorders;

• Manual Data Entry: the user can directly insert

measurement data such as blood pressure, blood

oxygen saturation, pulse rate and weight, indicat-

ing the date and time when he/she made this mea-

surement;

• Loading Report and EDF ﬁle: any report with

the recorded measurements can be saved and ex-

ported in pdf and European Data Format (EDF)

format. EDF is the most used standard to ex-

change and store biological and physical signals

(Kemp and Olivan, 2003). The user can choose

this functionality to upload these reports and ﬁles,

thanks to which the specialist can monitor the

user’s progress;

• History of Measurements: a calendar with a his-

tory of all the measurements performed is dis-

played.

4.3 Evaluation of Performance

The performance of the proposed system has been

evaluated in terms of the allocation of memory and

CPU used by the mobile application during the real-

time monitoring. The ﬁrst test consisted of tracking

memory and CPU usage during a monitoring period

of 15 minutes so as to assess over time the trend of

these quantities. The real-time monitoring feature

was tested on two different devices with two differ-

ent Android versions: a Samsung GT-i9505 (S4) with

the Android 5.0.1 version and a HUAWEI GRA-L09

(P8) with the Android 6.1 version. The performance

of these analyses showed good results, as reported in

the ﬁgures 7 and 8.

The obtained results indicate that the application

is efﬁcient in the use of system resources, which is

very important considering that it is designed to run

on a mobile device. The memory used by the applica-

tion in the real-time monitoring feature is constantly

less than 32 MB, increasing slightly applying when

the ﬁlters are applied, and the use of the CPU is low.

Figure 7: Performance on Samsung GT-i9505

Figure 8: Performance on HUAWEI GRA-L09

To evaluate the CPU use, we compared the per-

formance of our system with that of the SenseView

App (http://www.senseview.mobi/), which is one of

the most used apps on the market to connect with the

Zephyr Bioharness BH3. The results show that our

proposed system occupies, during the instantaneous

monitoring, about 0,4 % of CPU, while the CPU used

by the SenseView App is about 0,2 %. The RAM

used, instead, is less for our proposed app (21 MB)

than that used by the SenseView App (52 MB). This

means that the realized system can be run on mid-

range mobile devices without degrading the device

performance.

5 CONCLUSIONS

In recent years the prevalence of chronic diseases

has increased due to the rise in life expectancy and

changes in lifestyle, and thus people suffering from

these pathologies often need a continuos monitoring

of their vital signs. The mobile health ﬁeld can pro-

vide new access opportunities for treatment and med-

ical services, constituting a valuable support instru-

ment for both patients and doctors.

In this paper we have presented a physiological

monitoring application designed for Android mobile

devices. The user can visualize his/her own vital

signs information, collected by means of an instru-

ment, easy and fast to use, such as a smartphone or

tablet and appropriate wearable devices. The system

does not only monitor these parameters but also ana-

lyzes them, allowing the medical specialist to make a

more accurate analysis. The system provides a good

performance in terms of memory and CPU used, in

comparison with other systems on the market.

ACKNOWLEDGEMENTS

This work has received funding from the European

Unions Horizon 2020 Framework Programme for

Research and Innovation under grant agreement no

727528 (KONFIDO) and the project ”eHealthNet”

PON03PE 00128 1. The authors would like to ac-

knowledge support from Neatec S.p.A.

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