Data Collection via Wearable Medical Devices for Mobile Health
Vincenza Carchiolo
1 a
, Alessandro Longheu
1 b
, Simone Tinella
1
, Salvo Ferrara
2
and Nicol
`
o Savalli
2
1
University of Catania, Italy
2
Wisnam S.R.L., Acireale (CT), Italy
Keywords:
Mobile Health, IoT, Werable Medical Device, Big Data.
Abstract:
The prevention and early detection of illness symptoms is becoming more and more essentials in a world where
the improvements in healthcare extends life expectancy. New technologies led to new paradigma as e-health,
m-health, smart-health and pervasive health. Wearable networked devices for real-time and self-health moni-
toring represent an effective approach that fulfil prevention goal at the same time keeping costs under control.
In this work, we present a Wearable Health Monitoring Systems (WHMS) capable of collecting, digitizing,
connecting to a wearable medical device via Bluetooth, and measuring various physiological parameters of
patients in particular suffering from heart disease. System’s architecture, requirements, adopted technologies
and implementation issues are presented and discussed, showing its effectiveness in healthcare support.
1 INTRODUCTION
Advances in medical researches and the related im-
provement of both life quality and expectancy (GHO,
2019) is shifting the main reason for humans death
from infectious diseases to chronic illnesses; in such
a scenario, the prevention and early detection of ill-
ness symptoms plays a key role to preserve people’s
life.
The strengthening of medical care, either at home
or in the hospital, required the adoption of new tech-
nologies and led to new paradigma as e-health (Ey-
senbach, 2001), m-health (Istepanian et al., 2006),
smart-health (Solanas et al., 2014) and pervasive-
health (Postolache et al., 2013). Among the set of new
frameworks and innovative architectures as IoT and
Smart cities, wearable devices (Haghi et al., 2017)
also significantly endorse the self-health monitoring
approach, that allows to detect and prevent illness also
reducing overall costs in healthcare management.
The concept of wearable equipment devoted to
wellness and healthcare actually goes back to the XIII
century, when corrective lenses were firstly used and
evolved across decades through ears trumpets and
contact lenses, arriving to the XX century with elec-
a
https://orcid.org/0000-0002-1671-840X
b
https://orcid.org/0000-0002-9898-8808
tric/electronic devices as pacemakers, insulin pumps
and hearing digital aids. Recent trends for this market
are highly promising (TMR, 2019), although signifi-
cant drawbacks still deserve a major attention, as reg-
ulatory hurdles that somehow prevents an intensive
adoption of such devices by healthcare professionals
and final users, and security issues that hold a critical
position due to the nature of personal information.
A further question to consider is the data man-
agement, from gathering to the storage and process-
ing steps, being health data sometimes collected in
a real-time fashion and/or requiring additional infor-
mation as the (possibly full) patient’s medical his-
tory extracted from central databases of healthcare
providers (Ferebee et al., 2016). Problems related
to data management also includes irregularity, high-
dimensionality and sparsity (Ismail et al., 2019), that
naturally leads to the world of Big data algorithms and
analytics (Chen et al., 2014).
The work presented in this paper falls into this
scenario, in particular here we present a case study
concerning an architecture where health data are col-
lected and stored, and can be later retrieved and visu-
alized through a Web application. We focus on col-
lection and manipulation of data gathered via wear-
able technology in cardiology. The use of wearable
devices in cardiology is well established but recently
wearable devices allowing passive hearth rate moni-
586
Carchiolo, V., Longheu, A., Tinella, S., Ferrara, S. and Savalli, N.
Data Collection via Wearable Medical Devices for Mobile Health.
DOI: 10.5220/0009100705860592
In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 586-592
ISBN: 978-989-758-398-8; ISSN: 2184-4305
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

toring become largely available. On the other side,
wide range for applications in the active monitoring
sector and in the emergency management is a cur-
rent area of concern. The new frontiers of application
of wearable devices in cardiology also highlight open
problems in the field of data security and their vali-
dation. Another aspect of great interest lies in the re-
quirements for real-time data collection management
and how they can be effectively used to train future
intelligent systems; the case study presented in this
paper concerns issues related to real time and data se-
curity.
The paper is organized as follows: in section 2
a brief overview of related works concerning health
systems is discussed, while in section 3 the case study
is presented and in section 4 some specific use cases
are illustrated. Finally, section 5 shows our conclud-
ing remarks and future works.
2 MOBILE HEALTH AND
WEARABLE DEVICES
First E-health systems were developed in the early
2000s with personal medical data recording also
known as Electronic Health Record or EHR (Baird
et al., 2011), thus providing easy access to pa-
tients information. More recently, the exploitation of
mobile devices endorsed the diffusion of M-Health
paradigma (Istepanian et al., 2006), where medical
data, related treatments and two-way communication
with physicians is granted via apps avoiding physical
meetings at the hospital whenever possible.
IoT universe (Miraz et al., 2015) improved M-
Health with new services thanks to the massive
use of medical sensors, seamlessly integrated into
mobile/wearable devices as smartphones and fitness
bands, therefore making it possible even real-time vi-
tal signs detection. One step further was achieved
with Smart Cities integration, determining the so-
called Smart-Health or S-Health (Solanas et al.,
2014); the harnessing of city related information re-
sults in the healthcare improvement, for instance pol-
lution data can be used to suggest allergic individuals
to avoid specific areas to prevent allergy attacks, or
an emergency can be better managed by optimizing
ambulance path exploting both patient’s position and
traffic information.
In general, smart-health allows to seize several op-
portunities as:
• Chronic illness prevention and management, in-
deed thanks to data gathering a situation requir-
ing immediate intervention can be detected and
proper action will be carried out timely
• Data analysis can allow to discover incorrect or
inefficient medical cases management, so these
can be dealt with better in the future. Data can
also be matched with state, position and current
patient activities to tailor actions and treatment to
personal needs, for instance identifying and ex-
cluding false positives for specific scenarios.
• Emergency detection and control by leveraging
citizen vital signs and activities, and major risk
areas; such information can be used to effec-
tively and safely address situation as for instance
outbreaks, unexpected pollution increase, chemi-
cal/nuclear accidents etc.
• Healthcare cost reduction, thanks to the increase
in overall efficiency and effectiveness, avoid-
ing unnecessary hospitalization, therapies and/or
treatments; this reduction is expected to increase
more and more also thanks to a better monitoring
of elder people
All these fascinating advances collide with pri-
vacy issues coming from the massive personal health
data gathering. Some projects have been developed
to define boundaries for health related data exploita-
tion and protect from data breaching, as the Trustwor-
thy Health and Wellness (THaW) (THaW, 2019), an
NSF-founded project aiming to provide trustworthy
information systems for health and wellness, or the
Strategic Healthcare IT Advanced Research Project
on Security (SHARPS) (SHARPS, 2019) whose goal
is to develop ”technologies and policy insights con-
cerning the requirements, foundations, design, devel-
opment, and deployment of security and privacy tools
and methods as they apply to health information tech-
nology.”
Systems for monitoring healthcare data can be
classified as follows:
• Remote Health Monitoring Systems or RHMS,
that include those systems capable to send and/or
receive remotely their data
• Mobile Health Monitoring Systems (MHMS), an
RHMS enhancement that leverages smartphones
or other mobile devices for local data processing
whenever needed
• Wearable Health Monitoring Systems (WHMS),
where mobility is further enriched with wearable
devices/sensors
• Smart Health Monitoring Systems (SHMS),
where ’smart’ characterizes the approach and re-
lated devices
According to this classification, in (Ren et al., 2010)
an MHMS is proposed, specifically focused on en-
ergy management, whereas in (Shih et al., 2010) au-
Data Collection via Wearable Medical Devices for Mobile Health
587

thors introduce a system for ECG monitoring via RF
id (WHMS) based on a client-server architecture that
collect and store patient’s data, sent to the server via
mobile network on a regular basis. Some MHMS
solutions can exploit local processing capabilities of
mobile devices to analyze gathered data and establish
whether critical conditions arise; in such cases, im-
mediate alert is generated and transmitted to medical
staff, whereas a not-real-time data upload is usually
adopted to mitigate power consuption. The work (S.
et al., 2017) provide a comparison of several health
and activity monitoring systems, including textile-
based sensors intended for wearable systems, whereas
in (Hong et al., 2010) daily activities are detected
using wireless accelerometers, permitting the detec-
tion of falls, incorrect postures and sleeping disor-
ders. Authors proposed accellerometers as wearable
devices communicating with mobile devices via low
power protocols as Bluetooth or ZigBee; the mobile
device collects and eventually processes and sends
data to physicians. An advanced proposal is described
in (Pandian et al., 2008), where authors present a
washable shirt embedding a set of sensors working
as an array for continuously monitoring physiological
signals.
3 WHMS FOR HEART DISEASE
PATIENTS: A CASE STUDY
In this section we introduce a case study of a WHMS
capable of collecting, digitizing, connecting to a
wearable medical device via Bluetooth, and measur-
ing various physiological parameters of patients in
particular suffering from heart disease. The devel-
oped system has an user friendly interface based on
a web portal that allows to monitor in real time both
health conditions as well as the status of devices, thus
providing an effective tool to reduce the time of inter-
vention when complications rise, as well as allowing
continuous long-term monitoring and data collection.
The implemented system provide several func-
tionalities to:
• Collect the health data from measurements on the
patient;
• Transmit them to Cloud services;
• Memorize measurements in a long-term storage;
• Assess statistics from such data;
• Retrieve data for authorized users, i.e. patients
and/or medicians.
Figure 1 depicts the overall system architecture.
Figure 1: System Architecture.
From a software point of view, main components
to implement the service are:
• A Backend Server for the communication and
data processing functions.
• A Database to store data collected by the monitor-
ing subsystem (and used by the Backend)
• Web Portal available for computers and mobile
devices, to provide access to the system.
3.1 Functional Requirements
An intelligent monitoring system that allows to man-
age the patients should provide different functionali-
ties for users as patients themselves, but also doctors,
control staff, each according to his/her own profile.
In particular, the system must be able to display
the various data collected over time in the right for-
mat for the different types of analysis that can be per-
formed by users; a simple interface is therefore essen-
tial to display the most relevant measurements in real
time. Furthermore, time charts are useful to show the
dynamic data behaviour; it should be also possible to
graph and export data within a given period of time to
allow historical medical analysis with other specific
tools. All this data can be used for training an intelli-
gent system to detect and prevent alarm situation.
Finally, the system comes with the following fea-
tures:
• Management of user accounts
• Management of devices
• Associate devices to users
• Access to the measurements collected by the de-
vices
• Provide data graphical representation.
• Notification and management of alarm situation
• Export the data within a given time interval
HEALTHINF 2020 - 13th International Conference on Health Informatics
588

Figure 2: System Components.
3.2 Performance and Security
Requirements
Dealing with sensitive data the system must be
equipped with appropriate security mechanisms. It
must be designed to protect information access from
unauthorized users, also providing appropriate clear-
ances for different types of users according to their
profiles. Moreover, the system must cope with vari-
ous performance issue, i.e. it must be able to manage
peaks of information transmission (up to thousand of
active users on the web portal and a lot of devices
that continuously produce data). To address this, it is
necessary to design a robust and scalable architecture
able to manage variable workloads while maintaining
constant performance and high fault tolerance.
The features described above can be summarized
in the following points:
• The access to the system must be allowed only to
registered users according to proper ACL (Access
Control List)
• Each user must have access only to his/her own
devices.
• The web interface must be compatible with com-
mon browsers.
• The system must be scalable to cope high-volume
of active users simultaneously.
• The system must have low fault recovery times
and high availability.
3.3 Technologies and Services
To implement all requirements described so far, we
chose the following technologies.
• ASP.NET Core as framework for Back-end devel-
opment, as it natively supports various security-
related features also in cloud services scenario
• Microsoft Azure as a cloud service provider, as it
provides integration with the projects developed
in ASP.NET Core thanks to the App Service host-
ing services. It also provides the Azure Active
Directory B2C service for managing user authen-
tication as an Identity Provider
• Angular as a complete framework for developing
client-side applications
• Microsoft SQL Server to support transactions
and backup features (high reliability); it also
provides adequate security management mecha-
nisms; Based on the type of data (mainly struc-
tured), we did not choose a NoSQL database.
4 IMPLEMENTATION ISSUES
The WHMS architecture proposed in the previous
section is actually implemented following the Model-
View-Controller (MVC) pattern arranged into two
components: Controller-Model and View. The back-
end server (i.e. the Controller-Model) is hosted on a
cloud service that implements different REST APIs
for data extraction and that are accessed through the
Data Collection via Wearable Medical Devices for Mobile Health
589

Figure 4: General Sequence diagram of an API.
frontend interface (the View). Between the backend
server and the database there is often an intermediate
level to decouple the two actors using a set of Stored
Procedures. Figure 2 shows the MVC architecture
with the main APIs offered by the backend server to
fulfil the requirements.
Access to each API is protected by the Azure B2C
authentication mechanism (namely Active directory
B2C or AD–B2C). In each request it is necessary to
send the JWT Token obtained through the log-in pro-
cedure. In addition to the mechanism offered by AD–
B2C, in each API there is an additional level of se-
curity that verifies whether the user who made the re-
quest has access to the resources he is also requesting
for; some specific APIs such as those related to device
or user management are protected by a user type filter
as only administrators are authorized to use it.
Using these two security levels, access to data is
guaranteed only to users authenticated via AD B2C
and authorized through a proper internal policy of ac-
cess management.
In Figure 3 is represented the two levels MVC ar-
chitecture, in particular the Controller is represented
by the API Controller that intercepts calls coming
from the frontend (View), retrieving the necessary in-
formation from the Model. The Model is represented
by the DataAccess Layer, but to make the Model flex-
ible a Business level has been added to the DataAc-
cessLayer to decouple the implementation of the busi-
ness rules from the implementation of each individual
variant of the Model itself.
Taking into account this software structure where
the Business Layer represents an extension and inter-
Figure 3: Back-end structure.
face to the DAL, we included a caching mechanism
for data with low rates of change and high request
rates. In particular the data concerning the users are
continuously requested for security purposes as it is
necessary to verify the access authorizations to the re-
sources. Since the UserBusiness module in the Busi-
ness Layer is the only that manipulate such data, it is
easy to keep this cache coherent, invalidating it when
a modification method is called.
Figure 4 shows a general sequence diagram rep-
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590

Figure 5: Use cases.
Figure 6: The application interface showing an alarm.
resenting the typical operations that are performed at
each invocation of an API. Note the lack of commu-
nication between UserBusiness and Repository as the
caching mechanism is often used.
On the front-end architecture we briefly discuss
the implementation of the communication logic with
the APIs. In particular with AD–B2C policies web
pages where users can register or log in are available.
Once these operations are completed, the web pages
automatically redirect the browser to the web appli-
cation page by sending the generated access token to
the URL. The application caches the token during the
session using the API as Bearer Token at every re-
quest. Most APIs implement a simple communication
flow that ends with a single call. For instance, the API
that return the points for the graphs implements a pag-
ing mechanism where the whole data flow is received
through several calls; this is performed since data to
download are huge and we want to avoid communica-
tion timeout problems.
Figure 5 illustrate a set of use cases the proposed
application actually address; here we focus on users
and administrators as two main actors that can access
the system.
Finally, figure 6 shows the GUI of the proposed
WHMS, in particular displaying an example of an
alarm detected (high left ventricle temperature warn-
ing).
5 CONCLUSIONS AND FUTURE
WORK
In this paper, an implementation of a Wearable Health
Monitoring Systems has been presented. The pro-
posed system gathers physiological parameters of
patients suffering from heart disease, sends data
to Cloud services, and allow processing data for
medical analysis, providing secure access to both
patients and medicians within a proper architec-
ture. The system has been implemented by Wisnam
Data Collection via Wearable Medical Devices for Mobile Health
591

(https://www.wisnam.com/), and it is currently under
intensive testing, also to exploit gathered data to train
intelligent monitoring algorithms. In particular, ma-
chine learning techniques are under investigation to
effectively support the prevention and early detection
of relevant illness symptoms to activate timely needed
medical treatments. Another line of research concerns
the improvement of back-end services in order to pro-
cess more and more data in a lesser time, for and also
to support as many simultaneous users as possible. Fi-
nally, a next step is the migration of the system onto a
serverless architecture, to not depend on a single point
of failure at the same time achieving a high scalability.
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