Context awareness in healthcare: a systematic literature review

Abstract

The incorporation of information and communication technologies has transformed the health field. With the constant miniaturization of embedded devices, the increase in human–computer interactions and their ubiquity has increased the possibilities of intervention in this field of study. One of the fundamental characteristics of ubiquitous computing applied to health is context awareness. The use of context awareness in healthcare faces many challenges, which has led to the search for several solutions in the integration of sensors from different origins, in data fusion and reasoning algorithms, among others. This paper aims to explore the recent literature related to the use of context awareness in health, defining the taxonomy and identifying challenges and open questions. The method for achieving these objectives is to use the systematic literature review approach, which is characterized by research questions that guide the definition of a taxonomy and the search for challenges in the area. As a result, we have reviewed around 4000 scientific studies published over the last ten years, selecting and researching the most meaningful, in-depth approaches in the field of context-aware health, resulting in a final corpus of 38 articles. We have developed an up-to-date taxonomy that classifies context awareness in the field of health, as well as identifying open questions and issues that can guide future work in the area. These results, unified in one paper, contribute to a significant degree of coverage of the use of context-aware data in health.

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https://doi.org/10.1007/s10209-019-00664-z
REVIEW PAPER
Context awareness inhealthcare: asystematic literature review
LuisClaudioGubert1,2· CristianoAndrédaCosta1 · Rodrigo daRosa Righi1
© Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract
The incorporation of information and communication technologies has transformed the health field. With the constant
miniaturization of embedded devices, the increase in human–computer interactions and their ubiquity has increased the
possibilities of intervention in this field of study. One of the fundamental characteristics of ubiquitous computing applied to
health is context awareness. The use of context awareness in healthcare faces many challenges, which has led to the search
for several solutions in the integration of sensors from different origins, in data fusion and reasoning algorithms, among oth-
ers. This paper aims to explore the recent literature related to the use of context awareness in health, defining the taxonomy
and identifying challenges and open questions. The method for achieving these objectives is to use the systematic literature
review approach, which is characterized by research questions that guide the definition of a taxonomy and the search for
challenges in the area. As a result, we have reviewed around 4000 scientific studies published over the last ten years, select-
ing and researching the most meaningful, in-depth approaches in the field of context-aware health, resulting in a final corpus
of 38 articles. We have developed an up-to-date taxonomy that classifies context awareness in the field of health, as well as
identifying open questions and issues that can guide future work in the area. These results, unified in one paper, contribute
to a significant degree of coverage of the use of context-aware data in health.
Keywords Survey· Health· Sensor· Wearable· Ubiquitous computing
1 Introduction
With the advancement and application of computational
technology, ubiquitous computing is now used everywhere
[15]. With the miniaturization of embedded devices as
well as with the increasingly intelligent nature of software,
human–computer interaction is continuously increasing. In
addition to this interaction, the hardware and software must
cooperate, thereby improving the human–machine interac-
tion. One of the critical features of ubiquitous computing
is context awareness [18, 60]. The idea of context aware-
ness is to provide the appropriate services to users, such as
smart homes, smart offices and health-related services [42,
43]. Also, currently the Internet of things (IoT) has been
emerging as a new paradigm in information technology. The
central idea is to build a dynamic network infrastructure,
connecting a variety of physical and virtual “things” with
the use of mobile devices and sensors. The practical use
of these sensors on an Internet-connected platform raises
many research possibilities, such as system architecture and
application data processing [14, 23]. Collaborating with
this, IoT technology has driven healthcare through a rapid
proliferation of handheld devices and smartphones, from a
conventional hub-based system to more personalized health-
care systems. However, enabling advanced IoT technology
in custom systems is still a significant challenge in this field,
suffering from problems such as the lack of accurate and
economical medical sensors, non-standardized IoT sys-
tem architectures, the heterogeneity of connected wearable
devices, the multidimensionality of the data, and the high
demand for interoperability [8, 48, 50].
Traditionally, the motivation to use information and com-
munication technologies (ICT) in a health system is to pro-
vide medical services to patients, called e-health, such as
electronic registration systems, telemedicine systems, and
* Cristiano André da Costa
cac@unisinos.br
1 Software Innovation Laboratory - SOFTWARELAB,
Applied Computing Graduate Program, Universidade
doVale doRio dos Sinos - Unisinos, Av Unisinos 950,
SãoLeopoldo93022-750, Brazil
2 Instituto Federal de Educação, Ciência e Tecnologia doRio
Grande doSul - Campus Ibirubá, Rua Nelsi Ribas Fritsch,
1111, Ibirubá, RS98200-000, Brazil

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devices for diagnosis [50]. But with increasing longevity and
the increase in the number of people with chronic diseases,
there are problems with the economic viability of traditional
health systems, thus generating the need to develop ubiq-
uitous health systems to provide quality, patient-centered
health services [10]. This includes, for example, the devel-
opment of e-health systems applied to smart homes [43],
systems for pervasive healthcare monitoring [1, 20, 53], and
systems for senior wellness services in smart homes [31].
For the full functionality of these systems, we must consider
the context, which is defined as any information that can
be used to characterize the situation of a person, place or
object deemed relevant to the interaction between a user and
an application, where the relevance depends on the user’s
task. As a consequence, some desirable characteristics for
context-aware systems in health are the presentation of infor-
mation and services to a patient, the automatic execution of
a function or the context tagging for later retrieval purposes
[18]. Thus, in the health field, wearable sensors, such as
accelerometers or ECG, are coupled to the patient to meas-
ure physiological and environmental data. These data are
then analyzed to provide feedback for a variety of purposes,
such as evaluating the effectiveness of a new treatment, bet-
ter studying a disease, overcoming a patient’s behavior, or
adjusting medication. Context-sensitive healthcare applica-
tions face some common challenges, regardless of the ulti-
mate goals [25, 52].
As examples of challenges related to the use of context
awareness in health, we can mention the integration of heter-
ogeneous sensor data, with different technologies, included
in these networks and protocols [16, 22]. The energy effi-
ciency of the sensors, as well as their responsiveness and
robustness, are some of the challenges that arise [17, 38, 51].
Another challenge is context switching and continuous data
delivery, without gaps [8, 10]. There are also issues related
to the safety and privacy of the health information, which
is important within the construction of the context where
the patient is under care [5, 34, 63]. Finally, the increasing
use of artificial intelligence/machine learning in healthcare
has appeared as a significant trend [28], though it runs into
the dilemma between vigilance and control on the one hand
and support and personal benefit on the other [54]. Thus,
considering the challenges cited and the fact that there are
no documents that present these issues in a unified form,
the objective of this article is to identify the technology in
the use of context-aware health and to discuss the issues
and challenges that surround the area, investigating the main
contributions made in the last decade. A review was con-
ducted of the literature on the use of context-aware data in
the health field, describing the technologies involved and
their uses. As a way of mapping the scenario, we used the
method of a systematic review of the literature to choose a
corpus of studies [11, 65]. As a result, we present an updated
taxonomy and indicate the paths and challenges to be over-
come for the application of context awareness in health.
This paper is organized as follows. In Section2, we
describe the study protocol used in the literature review. In
Section3, we describe the results of the research and evalu-
ate the quality of the articles in the corpus. In Section4, we
answer and discuss the formulated research questions. In
Section5, we present the challenges and future directions
for the use of context awareness in the health field. Finally,
in Section6, we present the conclusions and limitations of
this paper.
2 Method
This section focuses on the description of the study proto-
col, which introduces the procedures adopted and guides
subsequent decisions. Unlike a conventional and unstruc-
tured review process, a systematic review follows a rigorous
sequence of methodological steps [11, 65]. Based on a well-
defined and evaluated review protocol, this study presents a
systematic literature review designed to provide a broader
view of context awareness in healthcare research.
2.1 Study design
We chose the systematic literature review approach because
a large number of articles can be combined and at the same
time it can provide a summary, allowing gaps to be identified
for future research. The systematic review of the literature
is increasingly used in the health field, the central theme of
this study, a factor that contributed to its adoption. The steps
below define the scope of this systematic literature review
(SLR) [55]:
1. Research questions: present the research questions used;
2. Search strategy: present the strategy and libraries
exploited to collect the data;
3. Article selection: explain the criteria for selecting the
studies;
4. Distribution of articles: present how the studies are dis-
tributed chronologically;
5. Quality assessment: assess the quality of the selected
studies;
6. Data extraction: compare the selected studies with the
research questions.
2.2 Research questions
Research questions are based on the motivation to conduct
the SLR, that is, the answers to these questions should pro-
vide an evidence-based consolidation to define, apply and

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acquire knowledge about the use of context awareness in
healthcare.
According to Table1, question RQ1 concerns the defini-
tions, classifications, and characterizations existing in the
literature (if any). RQ2 relates to the challenges that arise
in the use of context-aware information in the health field.
Question RQ3 refers to scenarios in which context-aware
data are applied, or its use has relevance to the health field.
Question RQ4 seeks to identify and compare the evidence
regarding context-aware health modeling methods. Finally,
question RQ5 asks how contextual reasoning is presented,
also including data fusion methods.
2.3 Search strategy
After defining the research questions, the next step was to
select a set of studies related to the research questions. This
process involves the definition of search keywords and the
definition of the scope of the search [49]. The search key-
words were defined after reading articles in the area of inter-
est, and the separation of terms, synonyms, and acronyms
that best defined the search object (taking into account its
relation to previously defined research questions). Following
what was defined by Kitchenham and Charters [32], these
terms were combined using Boolean operators.
We define the scope of the work as the use of context-
aware data in the health field. In this way, the final research
chain, formed from the criteria described above, was defined
as follows.
Search String
(Context aware OR Context awareness OR Situ-
ational Aware OR SituationalAwareness) AND
(Health OR HealthCare)AND (Environment
data)
In the execution of the article search, the source studies
were obtained from electronic databases, selected through
searches using the keywords of the constructed string. The
electronic databases included in the survey were: ACM
Digital Library1; Google scholar2; IEEE Xplore Library3;
IET Digital Library4; JMIR Publications5; PubMed6; Sci-
enceDirect7; Springer Link8; Web of Science9; and Wiley
Online Library10.
These databases were chosen because they constitute a
significant sample of the databases and provide full-text
journals and conference proceedings of the most important
health conferences involving e-health, wearables, and their
relations.
2.4 Article selection
After collecting the set of related articles in the databases,
we proceeded to remove studies that were not relevant, so
as to maintain only the most representative ones. For this,
after the initial search, the impurities were removed, which
entailed the removal of duplicate articles (because some
studies were available in more than one database), and arti-
cles not related to the search string, but which were returned
due to characteristics of the different electronic databases.
After the first filter, the second and third filters con-
sisted of analyzing the title and the abstract, respectively,
and excluding those that did not mention health and con-
text (according to the search string). In the fourth step, the
remaining studies were pooled, and the book chapters, the-
ses, and short articles were removed. For withdrawing the
short articles, a criterion of at least five pages was used,
and some articles with fewer were kept since they presented
some desirable characteristics, such as related works and
final results. Finally, some studies not related to this research
Table 1 Research questions
Identifier Issue
RQ1 How is the use of context awareness in the health field defined and classified in the existing research?
RQ2 What are the challenges related to the use of context-aware information in healthcare?
RQ3 What are the use case scenarios related to context awareness in healthcare?
RQ4 What are the context modeling techniques employed in healthcare?
RQ5 What are the reasoning and data fusion methods used in context-aware information in healthcare?
1 https ://dl.acm.org/.
2 https ://schol ar.googl e.com.br/.
3 https ://ieeex plore .ieee.org/Xplor e/home.jsp.
4 http://digit al-libra ry.theie t.org/.
5 http://jmirp ublic ation s.com/.
6 https ://www.ncbi.nlm.nih.gov/pubme d/.
7 https ://www.scien cedir ect.com/.
8 https ://link.sprin ger.com/.
9 http://isikn owled ge.com.
10 https ://onlin elibr ary.wiley .com/.

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remained. We looked at the full text to remove those that
were not relevant.
2.5 Quality assessment
One of the concerns of this review is to assess the quality of
the selected corpus. According to [32], the quality criterion
verifies whether the article is a significant study. Therefore,
questions were elaborated, which were used as criteria to
evaluate the articles found. According to Table2, the pres-
ence of the following items in each article was verified: the
research proposal, literature review, related works, methods,
results, conclusions, and future work.
2.6 Data extraction
The data extraction and synthesis were performed by reading
each of the articles included in the review and by extracting
the relevant data. This was managed through the Mendeley
bibliographic management tool and spreadsheet software.
To keep the information consistent, the data extraction for
the studies included in the review was based on the research
questions and where they were answered within the body of
the article, as can be seen in Table3. To synthesize the data,
we inspected the extracted data for similarities. The results
of the synthesis will be described in the subsequent sections.
3 Results
In this section, we present the results of the studies evalu-
ated, related to the research theme. In the following sub-
sections, we attempt to answer each proposed research
question by synthesizing the information. As a result, in
addition to answering the research questions, contributions
are also suggested in the field of the use of context-aware
data in the health field, with a proposed taxonomy and an
updated view of the main challenges.
3.1 Strategy forconducting thesearch
For this research, we chose ten electronic databases (listed
in Sect.2.3) from which to select the articles. The criteria
for choosing these databases were their coverage of the
fields of computer science and healthcare. For the selec-
tion of articles in each database, we used a few procedures
to limit our search by selecting articles in English, in the
period from 2008 through 2018, and excluding results
from patents and citations.
3.2 Proceeding withthearticle selection
Our search process is presented in detail in Fig. 1, show-
ing the processes of exclusion and filtering.
Initially, our search string found 3826 articles in the
different databases. After the initial outcome, we used the
criterion of removal of impurities, which consists of the
removal of articles that were published in journals and
conferences unrelated to the topic of this research, and
the removal of duplicate articles, with 724 studies remain-
ing. After the first filter, we used the title and the article
abstract as an exclusion criterion. Studies in which the
titles or abstract did not contain words related to the sub-
ject of this study, such as health, context, aware (especially
words that are in the search chain), were excluded from
the corpus, leaving 154 articles that were grouped to apply
new criteria of exclusion.
After grouping the articles, the exclusion criteria
applied were the removal of chapters from books, theses,
and short articles, with 79 articles remaining. The crite-
rion for short articles was used to eliminate articles with
five or fewer pages, except articles that presented propos-
als of architectures and presented final results.
In the remaining 79 articles, an analysis was performed,
and the articles that did not contribute significantly to the
criteria used in this research were eliminated. In addi-
tion, some of these studies belonged to the same group of
researchers, presenting the same methods or techniques,
Table 2 Quality assessment items
Section Description
Criteria 1 The article has a research proposal
Criteria 2 The article presents a literature review
Criteria 3 The article discusses related work
Criteria 4 The article has a methodology
Criteria 5 The article presents results
Criteria 6 The article has a conclusion
Criteria 7 The article suggests future research
Table 3 Review articles related to the research questions
Section Description Research questions
Open content
Title RQ4, RQ5
Abstract RQ1, RQ3, RQ5
Keywords RQ1, RQ3, RQ4, RQ5
Article content
Introduction All questions
Method All questions
Results All questions
Discussion All questions
Conclusion All questions

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from which only the most representative articles were
selected. Thus, 38 articles were selected as the basis for
this study. An overview of the selected studies is presented
in Table4, showing the identifier, reference, year of pub-
lication, publisher, and type.
3.3 Assessing thequality
In this section, we present a qualitative analysis of the arti-
cles, based on the criteria defined in Sect. 2.5. We used a
scale from 0 to 7 as in Roehrs [55] to analyze and classify
these articles, described in Table2, with 31 articles (81%)
covering five or more of the cited criteria and 7 articles cov-
ering four of the quality criteria.
Regarding the year of publication, the distribution of arti-
cles is shown in Fig.2, of which 26 articles of the corpus
were published in the last five years.
Figure3 shows the distribution of the number of citations
per article. From the figure, it is possible to perceive that
articles that do not have citations are mostly from the year
2018. On the other hand, the articles most cited (more than
30) were published, for the most part, in recent years. The
remaining articles have, in their majority, more than eight
citations.
4 Answers totheQuestions andDiscussion
This section focuses on the research questions defined
in Sect. 2.2 to review the results of the literature review
and gain some insight. The literature researched discusses
various definitions and classifications, which are presented
and discussed below.
4.1 How istheuse ofcontext awareness
inthehealth eld dened andclassied
inexisting research?
The answer to question RQ1 leads to the definition of a tax-
onomy based on the analysis of the current studies. This
taxonomy is illustrated in Fig.4 and is based on observations
about the technologies, methods of use, and target problems.
Sometimes, the proposals combine various techniques. As a
result, the classes in this taxonomy are not mutually exclu-
sive, and each study can be inserted into one or more of
them. The process of defining the taxonomy began with
an analysis of all the articles of the corpus to identify pat-
terns, characteristics, and categories. Moreover, we defined
five central concepts to analyze (1) sensors, (2) scenarios,
(3) context modeling, (4) fusion methods, and (5) context
reasoning.
The first element refers to the sensors, which were clas-
sified into three types: physical, virtual, and logical. Physi-
cal sensors were separated in the taxonomy into embedded
sensors (subdivided into wearable and internal/smartphones)
and external sensors. Physical sensors appear to evaluate
activities, as Mshali, Lemlouma and Magoni [43], who use
location sensors (GPS) and environmental sensors (humid-
ity, temperature, camera) to monitor orientation and mem-
ory activities in the elderly living alone. Another way to
use physical sensors, this time using an accelerometer, is
to encourage a more active lifestyle by promoting physical
exercise [4]. Unlike physical sensors, virtual sensors do not
Fig. 1 Articles selected by database

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generate data by themselves, but retrieve data from differ-
ent sources and publish it as sensor data. Logical sensors
produce useful information through the combination of
physical and virtual sensors (e.g., a calendar). In Sannino
and De Pietro [57], accelerometer and ambient temperature
data (physical) are combined with location data and (virtual)
maps to indicate the location in risk situations.
The second element of the taxonomy concerns the sce-
narios in which the data collected and the context can aid
in healthcare. Some researchers have proposed middleware
for ambient assisted living [26], increasing the degree of
independence and mobility for the elderly or people with
chronic illnesses. Chiang and Liang [13] propose an intel-
ligent home care system, which is also applied to the elderly
and to patients with chronic illnesses. Mshali, Lemlouma
and Magoni [43] suggest an adaptive e-health system for
smart homes, able to detect behavioral changes, focusing on
the elderly and people living alone. Already, Nava-Muñhoz
and Morán [45] report the lack of awareness about the
situations that involve care for the elderly and the lack of
information about the availability of caregivers in a nursing
home, proposing a context-aware notification model.
Table 4 Articles related to the
research questions Identifier Year Author Publisher Type
[8] 2018 Baloch, Shaikh and Unar Springer Journal
[43] 2018 Haider, Tayeb and Damien Elsevier Journal
[44] 2018 Mshali etal. Elsevier Journal
[46] 2018 Newcombe etal. IEEE Conference
[3] 2017 Alirezaie etal. MDPI Journal
[6] 2017 Azimi etal. Springer Journal
[26] 2017 Gomes etal. Wiley Journal
[31] 2017 Jung MDPI Journal
[38] 2017 Michalakis and Caridakis Springer Journal
[36] 2016 Matthew-Maich etal. JMIR Journal
[58] 2016 Santos etal. Springer Journal
[13] 2015 Chiang and Wen-Hua Springer Journal
[17] 2015 Deen Springer Journal
[19] 2015 Dobrescu Inase Journal
[21] 2015 Elmalaki, etal. ACM Journal
[24] 2015 Forkan etal. IEEE Journal
[27] 2015 Hameurlaine etal. Springer Journal
[34] 2015 Kuijs, Rosencrantz, and Reich Iaria Conference
[4] 2014 Alshurafa etal. IEEE Journal
[9] 2014 Beattie etal. ACM Journal
[12] 2014 Cherkaoui and Agoulmine IEEE Journal
[37] 2014 Mcheick Elsevier Journal
[57] 2014 Sannino and De Pietro Scopus Journal
[61] 2014 Thomas etal. Inderscience Journal
[64] 2014 Zerkouk etal. Springer Journal
[35] 2013 Machado etal. Springer Conference
[41] 2012 Mnatsakanyan etal. Elsevier Journal
[45] 2012 Nava-Muñhoz and Morán MDPI Journal
[47] 2012 O’Donoghue and Herbert ACM Journal
[56] 2012 Sanchez-Pi and Carb Springer Journal
[59] 2012 Song etal. Taylor & Francis Conference
[7] 2012 Wan D. Bae etal. IEEE Conference
[40] 2011 Mitchell etal. IEEE Journal
[62] 2010 Trebatoski etal. Journals@UIC Journal
[2] 2009 Al-Neyadi and Abawajy Springer Conference
[20] 2009 ElHelw etal. IEEE Conference
[39] 2009 Mileo etal. Oxford Journal
[33] 2008 Komnakos etal. ACM Journal

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The third element of taxonomy refers to context mod-
eling. Data must be modeled and represented according to
its meaning. For this, different techniques are used. Bae,
Alkobaisi, and Narayanappa [7] propose a framework of
data analysis to support environmental health decisions,
with context modeling based on ontology. Alirezaie etal. [3]
present a system for smart homes, also with a context mod-
eling technique based on ontology. Mcheick [37] proposes
a hybrid form for context modeling, using an object-based
approach and XML (markup scheme).
The fourth and fifth elements of the taxonomy refer to con-
text reasoning. This element includes data fusion and reason-
ing techniques. Hameurlaine etal. [27] propose a rule-based
model for reasoning about contextual information to provide
appropriate services in U-Healthcare systems. Similarly, Mileo
etal. [39] use a reasoning component that applies logical rules
intended for the correct interpretation of incomplete or incon-
sistent contextual information applied to home healthcare.
Alshurafa etal. [4] use an unsupervised technique for cluster-
ing in the proposal of a health-oriented physical activity recog-
nition framework using wearables. ElHelw etal. [20] propose
a statistical method for merging information from environmen-
tal sensors and wearables applied in a framework for pervasive
healthcare monitoring and use a probabilistic technique for
context reasoning. Mnatsakanyan etal. [41] propose a model
of distributed information fusion based on Bayesian networks,
applied to regional public health surveillance.
4.2 What are thechallenges related totheuse
ofcontext‑aware information inhealthcare?
To answer question RQ2, we list and identify common
challenges in using context-aware data in healthcare. The
Fig. 2 Number of papers by year of publication
Fig. 3 Number of citations per article

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problems are grouped in Table5. As can be seen, the content
brings together some common characteristics of challenges
related to the use of heterogeneous technologies, challenges
associated with the aspects of communication, energy man-
agement, scalability, and security.
The first and second lines of Table5 deal with hetero-
geneous technologies and trusted communication. Gomes
etal. [26] point to the need for a software infrastructure
flexible enough to interact with different types of sensors and
actuators, and with hardware and communication technolo-
gies of different formats. We should take into consideration
that the data packets of different sensors can have different
formats and encodings, increasing the need for transcoding
modules that encapsulate the logic needed to interpret the
data exchanged [20].
The third line of Table5, power management, refers to
the power consumption problem in the sensors. Assum-
ing that most sensors do not have a permanent source of
energy, depending on batteries for their power source,
power consumption becomes crucial for sensors used in
environmental and health contexts [26]. In addition, the
use of different transmission technologies (Zigbee, Blue-
tooth, WiFi) with different transmission consumption
needs makes power management a significant challenge
in this scenario. For this, Alirezaie etal. [3] make exten-
sive use of state-of-the-art communication protocols in
a context-aware system based on ontology for intelligent
houses, seeking a robust communication between IoT
devices, but efficient in terms of consumption.
The fourth and fifth lines of Table5, scalability and
precision, are related, in the case of scalability, to the
capacity to attend to a varied and increasing number of
sensors and, in this case, to attend to the increased pro-
cessing related to the generated data [26, 44]. In turn,
accuracy and reliability are perceived as critical issues in
context-aware applications. Sometimes, wearable sensors
do not provide enough information to build the situation,
so additional sensors are needed. Baloch, Shaikh, and Unar
[8] propose a three-tiered health-IoT data fusion approach,
which includes, in one of them, context-aware data fusion,
Fig. 4 Taxonomy of the use of context awareness in healthcare
Table 5 Challenges and concerns
Challenge Reference article
Heterogeneous technologies: data integration/data fusion [3, 6–8, 13, 17, 19, 20, 26, 34, 39, 41, 43, 44, 47]
Reliable communication: different wired and wireless technologies [3, 7, 12, 17, 19, 20, 26, 33, 36, 38, 39, 44, 45, 47, 58, 59, 64]
Power management: power consumption [3, 8, 12, 17, 20, 21, 24, 26, 33, 40, 44, 47, 61]
Scalability: reasoning and inference functionalities [3, 7, 8, 20, 26, 34, 44]
Accuracy: data, monitoring [7, 8, 20, 24, 33, 34, 38–40, 43–47, 56, 57, 61, 64]
Security: availability, privacy, confidentiality, etc. [2, 3, 8, 24, 31, 34, 36, 38, 44, 58, 64]

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combining vital sign data analysis and environment to pre-
vent false alarms.
Finally, the sixth row of Table5 refers to security. In
health monitoring systems, security is a critical issue
because it involves many processes and components: sen-
sors and actuators, data collection, and communication.
Michalakis and Caridakis [38] point out that people show
greater sensitivity when sharing their personal health infor-
mation, that is, privacy and security are of greater impor-
tance for health services compared to other similar services.
Al-Neyadi and Abawajy [2] propose a mechanism to con-
trol access to e-health systems based on context. To ensure
that services and information are accessed only by people
who have the privileges to access them, it takes into account
the person trying to access the data, the type of data being
accessed, and the context of the transaction in which the
access attempt is made.
4.3 What are theuse case scenarios related
tocontext awareness inhealthcare?
The answer to question RQ3 defines the scenarios in which
healthcare technologies are applied. These scenarios are
shown in Table6 and are based on the analysis of the stud-
ies that are part of the corpus treated in the present paper.
Often, the same study presents more than one scenario of
application, as can be seen in Table6.
The first line of Table6, home care, refers to the sce-
nario defined by home healthcare. In this regard, several
studies in the corpus of this article present different solu-
tions. Mileo etal. [39] propose the use of an intelligent
home health system characterized by a wireless sensor net-
work (WSN) and a reasoning component based on the set
of responses (ASP). Alirezaie etal. [3] use an ontology to
integrate the measurements collected from heterogeneous
sources to enable the semantic interpretation of events and
context awareness, while Hameurlaine etal. [27] use an
ontology to represent information about the context and a
rule-based model to reason about this contextual informa-
tion. Jung [31] presents a smart home technology using
wearable sensors and environmental sensors, with the aim
of creating an awareness of the situation and supporting
decision-making and recommending appropriate treatment
based on one health risk ratio.
The second line of Table6, mobile health, refers to the
ability of systems to adapt health monitoring in the face
of patient mobility. In this context, Komnakos etal. [33]
present a study on the potential and performance evalua-
tion of 3.5G technology to provide comprehensive elec-
tronic health applications, considering the performance
of the sensor network together with the 3.5G network, a
critical factor for this application. On the other hand, Bae
etal. [7], taking into consideration the negative health
effects of environmental factors, such as air pollution and
humidity, propose the monitoring of individual movement
trajectories and environmental conditions, to identify sig-
nificant relations between these data as a way of support-
ing public health systems. Both Sannino and Pietro [57]
and Komnakos etal. [33] point out that a knowledge of
the patient’s context is essential for application services in
mobile health environments and uses. The former propose,
to this end, a rule-based decision support system; the latter
propose a scalable ontology for modeling and reasoning.
For the third row of Table6, according to Nava-Muñhoz
and Morán [45], there are two significant problems in the
care of the elderly in a nursing home, taking into account
the criticality of this type of environment. First, there is
a lack of awareness about situations involving care of the
elderly, and second, the lack of information about the
availability and activities of other caregivers to support
the process of coordination. To deal with this kind of
situation, they propose a model for the design of context-
sensitive notifications in critical environments, having
as the main characteristic that it considers three context
sources (the environment, the sender, and the recipient of
the notification).
Regarding assisted self-care and telehealthcare, the
fourth and fifth lines of Table6, respectively, refer to self-
care assisted through the application of digital technolo-
gies. In Beatti etal. [9], regarding persons suffering from
chronic obstructive pulmonary disease, it is pointed out that
the continuous monitoring of a patient’s health, behavior,
and contextual information provides the ability to detect a
decline in their health before a problem occurs. They pro-
pose a context-aware self-management tool with a predic-
tion module to generate appropriate intervention warnings,
informing the patient that their health status is declining.
Chiang and Liang [13] and Mitchell etal. [40] propose a
home care system that stores the required contexts of knowl-
edge in ontologies, including the physiological information
and the patient’s environmental information, providing a
unified query interface with the contextual data on mobile
devices and providing interactive user feedback.
Table 6 Scenarios
Scenario Reference article
Home care [3, 6, 17, 27, 31,
33, 35, 36, 39,
45, 47, 61]
Mobile health [7, 9, 27, 33, 36,
38, 41, 47, 57,
59, 62]
Nursing home [45]
Assisted auto care [9, 40]
Tele-healthcare [9, 13, 61]

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4.4 What are thecontext modeling techniques
employed inhealthcare?
To answer question RQ4, we discuss the most popu-
lar context modeling techniques. Each of the following
methods has its strengths and weaknesses, and the actual
implementations of these techniques may vary, depending
on the application domain.
According to Mshali etal. [44], the key-value mod-
eling technique models information as key-value pairs in
different formats, but is not scalable and is unsuitable for
storing complex data structures. Another technique, using
a markup scheme, defines hierarchical data structures
using a markup language, such as XML. Marking tags
are used to represent the data format. Graphical modeling
consists of a diagrammatic representation of contextual
data at the design level, using appropriate models, such
as the UML and others. Object-oriented modeling uses
the concept of objects, with class and relationship hierar-
chies, employs encapsulation, reusability, and inheritance
to represent context data. In logic-based modeling, con-
text is represented as a set of facts, expressions, and logi-
cal rules. Ontology-based modeling represents knowledge
and contextual information using semantic technologies.
Different reasoning and standardization (RDF, OWL)
capabilities are available.
Mileo etal. [39] present an intelligent home system
with a reasoning component based on the set of responses
(ASP), responsible for the continuous contextualization
of the patient’s physical, mental, and social state. The
reasoning component applies expressive logical rules
that aim at the correct interpretation of incomplete or
inconsistent contextual information. Chiang and Liang
[13] and Kuijs, Rosencrantz and Reich [34] use ontolo-
gies for storing contexts, environmental information,
and information about the patient. Alirezaie etal. [3] and
Hameurlaine etal. [27] determine the potential of intel-
ligent home environments and propose the use of scalable
ontologies for modeling and reasoning, not only about
patients’ health measurements but also about all the con-
textual information, so as to provide appropriate health
services in smart homes.
Mcheick [37] states that to model the context of an
application, one must first look for the different elements
that affect the application, in order to infer contextual ele-
ments. Thus, the context construction is divided into two
types of features: dynamic ones (medical and environ-
mental data) that change frequently, and static elements
(name, age, medical condition). A hybrid form for con-
text modeling is proposed, which is object-based for the
dynamic components but uses XML (a markup scheme
for the static elements).
4.5 What are themethods ofreasoning andfusion
ofdata used incontext‑sensitive information
inhealth?
In question RQ5, we present and discuss the fusion tech-
niques used to extract significant data from different IoT sen-
sors, as well as present the methods of contextual reasoning
that appear in the articles in the corpus. The characteristics
of contextual information make it difficult to give it a pre-
cise meaning: Substantial amounts of data are required to
understand the user’s intentions and situations. Also, medi-
cal services require precision. To this end, data from various
sources and sensors are combined in order to obtain a more
reliable, accurate, and complete result. These data are used
to support reasoning components. Using context informa-
tion, the behavior of the application can be customized to
a specific situation [8, 27]. The methods are presented in
Table7 and are based on the analysis of the studies that are
part of the corpus. Often, the same study employs more than
one method, as can be seen in the table.
According to Alshurafa etal. [4], the detection of human
activity independently of its intensity is essential in many
applications, especially in the calculation of equivalent met-
abolic rates and the extraction of consciousness from the
human context. For this purpose, they use k-means exclu-
sive clustering and a probabilistic clustering algorithm based
on a Gaussian mixture model. Alirezaie etal. [3] focus on
using ontologies to integrate the measurements collected
from heterogeneous sources, in order to enable a semantic
interpretation of events and context awareness. The reason-
ing component uses a solution based on answer set program-
ming. Likewise, Hameurlaine etal. [27] propose a scalable
ontology for the integration of data from different origins,
as well as a rule-based reasoning component. Sannino and
De Pietro [57] also propose an intelligent mobile system that
automatically recognizes the context, analyzing data from
Table 7 Fusion and reasoning methods
Methods Reference articles
Data
Statistical [20, 31, 43]
Fusion
Probabilistic [7, 9, 17, 20, 41, 45, 59]
Supervised learning [24, 40]
Unsupervised learning [4]
Context
Rules based [2, 9, 19, 24, 27, 39, 57, 58]
Reasoning
Probabilistic logic [9, 20]
Ontology based [3, 34, 64]
Fuzzy logic [13]

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various sensors and automatically choosing, through a rule-
based decision support system, the action to be performed.
Chiang and Liang [13] develop an ontology-based health-
care system for storing contexts, with a fuzzy inference
mechanism for decision-making. Song etal. [59] propose a
context-aware middleware for mobile health services, spe-
cifically for preventive medicine. The middleware collects
the information through the recognition of diverse environ-
mental data that affect the patients’ health.
Bayesian probability has also been applied to the calcula-
tion of repetitive frequency values in the environmental and
personal information of the user to determine the relations
between the context information and preventive medicine
practices. For example, Forkan etal. [24] present a two-stage
learning methodology, that correlates contextual attributes
with limit values of vital signs, generating at the end a set
of association rules specific to a given patient: A supervised
learning system is applied to the dataset generated in the first
step, thus improving the accuracy of predicting the patient’s
situation. Mitchell etal. [40] present a framework for queries
on the mobile device with a supervised learning component.
5 Challenges andFuture Directions
After answering all the research questions, we can iden-
tify the challenges inherent in the use of context data in
healthcare. Starting from the taxonomy, we highlight the
challenges in Figure5. The first challenges are (a) context
acquisition (including sensors and application scenarios).
[26] addresses sensors concerning their quality and improve-
ment in the provided context data. [33] provides a compari-
son between various low- and high-rate sensor technologies
(e.g., Zigbee vs. Bluetooth) in different scenarios and with
different quantities including patient movement, as a way
to make a home care platform more effective. Other chal-
lenges to data acquisition include energy efficiency, respon-
siveness, and robustness. Since sensors often report their
data to a central node, there is a need for secure protocols.
The standardization and integration of sensors is desirable
and presents itself as another challenge [17, 38], in addition
to extending the existing models to different scenarios, as a
form of validation.
With respect to context (b) modeling, the reliability and
integrity of the provided data are essential, with no room
for data provision gaps. Otherwise, there will be poor rep-
resentation of context awareness [26]. For [13], domain
experts could provide more accurate knowledge, to enrich
the knowledge ontologies of the user context. The exchange
of scenarios performed by the patient can cause multiple
difficulties in the construction of the new context and its
representation according to [24]. Just as the validation of
contextual representation models should be extended with
added data, to better represent reality [4, 64], the expansion
of prototypes for real physical environments is pointed to by
[35] as something to be achieved.
From the perspective of (c) reasoning, which involves
context reasoning and data fusion methods, some chal-
lenges arise. These include the development of algorithms
for the extraction and combination of visual information and
improved data fusion techniques for more accurate detec-
tion of activities, avoiding false alarms [3, 20]. The graphi-
cal representation of dependencies and results of reasoning
tasks can be improved through automatic methods applied
to the analysis of the history of the inferences, according to
[39]. More robust methods of profiling autonomic behavior,
Fig. 5 Summary of challenges

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which will improve the detection of activities, would mini-
mize the workload of caregivers, making possible the long-
term monitoring of the elderly [20, 45].
To conclude, we can cite the challenges and issues of
security and privacy, regarding the use of context infor-
mation in health. These issues directly affect the trust in
systems that use information generated by sensors to cre-
ate a contextual perception [64] [34]. At this point, we can
highlight acceptance as an important challenge in the use of
sensors, especially in what refers to the reliability in the use
of intelligent medical systems at home [66], or the ethical
and morally justified use of the technology, as discussed in
[29]. In [30], there is a discussion of the acceptance of new
technologies in light of usability and accessibility engineer-
ing, concerning their development and use. Likewise, the
use of cloud computing for intense computing procedures
and the insecurity of people with regards to the sharing of
their personal medical data poses challenges to be overcome
concerning data security and privacy [38]. Finally, access
to patient information by third parties should be carefully
controlled, incorporating trusted domains so that unknown
users who are not part of the domain do not have access [2].
6 Conclusions
This study aimed to raise and discuss the main issues related
to context awareness in healthcare, and to identify the con-
cepts and technologies in this field. To answer the research
questions, we first sought to systematize and qualify the
information that served as the source for the research. To
conclude the paper, we have identified and proposed a tax-
onomy for the scope of the research, which was created fol-
lowing an analysis of the relevant articles published over
the last decade. In the taxonomy, we were able to identify
and group various scenarios and classifications for context-
aware information in healthcare, from “Data Fusion Meth-
ods” and “Scenarios” to “Context Reasoning” and “Data
Fusion” approaches. By establishing the taxonomy, it was
possible to observe other relations essential to understand-
ing the context-aware information, perceiving aspects con-
cerning concerns and challenges. We have identified sev-
eral approaches for applications in different scenarios and
using different types of sensors, including their limitations.
The difficulty in mapping the patient’s context from differ-
ent scenarios and sensors, using different communication
protocols, leads to a difficulty of precision in the definition
of the patient’s context awareness. Thus, different methods
were also analyzed for data fusion and different reasoning
techniques.
This research has been limited to aspects of context-aware
health use, not including the use of context information in
other domains, for example. In this sense, the review focused
exclusively on articles that addressed the basic concepts
of context awareness of health data. This research sought
to answer the research questions that formed proposals to
obtain a sketch of the current literature related to this sub-
ject, without specifically evaluating any framework or com-
puterized system that refers to the use of context awareness
using health data. The research was limited to obtaining
articles published in several scientific portals related to ICT
and health, limited to studies found in these sites when we
implemented the steps of the methodology of a systematic
review of the literature. We focused our work on scientific
articles and have not considered commercial solutions or
more technological approaches.
In future studies, we envision a focus on security, privacy,
and trust issues and challenges that directly affect the trust
in the use of context-aware health data. Although challenges
related to these issues have been around for a quite some
time, they still do not have definitive answers. Other aspects
that can be studied that are important for context awareness
in health include the standardization and integration of sen-
sors from different manufacturers, using different commu-
nication protocols and transmission technologies, improv-
ing the representation of the consciousness of the contexts
through a more accurate knowledge of the environments and
contexts of application, and the development of new algo-
rithms for context reasoning and data fusion, aiming at a
more precise detection of activities.
Acknowledgements This work was supported in part by the Coordi-
nation for the Improvement in Higher Education Personnel—CAPES
(Finance Code 001), the National Council for Scientific and Techno-
logical Development—CNPq (Grant Numbers 303640 / 2017-0 and
405354 / 2016-9), and the Federal Institute of Education, Science and
Technology—IFRS.
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