Guidelines’ Parametrization to Assess AAL Ecosystems' Usability
Carlos Romeiro
and Pedro Araújo
Instituto de Telecomunicações (IT-UBI), Universidade da Beira Interior, Covilhã, Portugal
Department of Computing, Beira Interior University, Covilhã, Portugal
Keywords: Usability, Ambient Assisted Living, User Interaction, Automation, Heuristics Analysis.
Abstract: With the aging of the population, healthcare services worldwide are faced with new economic, technical, and
demographic challenges. Indeed, an effort has been made to develop viable alternatives capable of mitigating
current services’ bottlenecks and of assisting/improving end-user’s life quality. Through a combination of
information and communication technologies, specialized ecosystems have been developed; however,
multiple challenges (ecosystems autonomy, robustness, security, integration, human-computer interactions
and usability) have arisen, compromising their adoption and acceptance among the main stakeholders.
Dealing with the technical related flaws has led to a shift in the focus of the development process from the
end-user towards the ecosystem’s technological impairments. Although many issues, namely usability, have
been reported, solutions are still lacking. This article proposes a set of metrics based on the parametrization
of literature guidelines, with the aim of providing a consistent and accurate way of using the heuristic
methodology not only to evaluate the ecosystem’s usability compliance level, but also to create the building
blocks required to include automation mechanisms.
The age pyramid has been shifting in both western
and eastern civilizations. The decrease in birth-rates
and the overall improvement of health care services,
combined with higher life expectancy, have led to the
current population distribution tendency on a
worldwide scale (Eurostat, 2019).
This phenomenon poses new challenges and
opportunities in several sectors, specially in the health
sector, where there have been growing demands to
ensure the elderly’s wellbeing. These demands,
combined with resources shortage and lack of a
patient-oriented approach, compromise both the
efficiency and availability of these core services. As
a consequence, the scientific and industrial
communities have attempted to develop an ICT based
solution able to improve the user’s quality of life,
ensure his/her autonomy, optimize the economic
sustainability of medical assistance services and
address the healthcare services specific needs the
Ambient Assisted Living Ecosystems (Curran, 2014).
Despite their improvement, multiple challenges
should be tackled in order to make their widespread
adoption feasible and secure. Challenges related with
multiple topics, such as security, usability, aytonomy,
data management among others (Curran, 2014; A. V.
Gundlapalli, M.-C. Jaulent, 2018; Van Den Broek,
G., Cavallo, F., Wehrmann, 2010; Peek et al., 2014;
Greenhalgh et al., 2013; Duarte et al., 2018; Mkpa et
al., 2019; Ismail & Shehab, 2019; Vimarlund &
Wass, 2014).
Concerning the system’s usability, multiple
studies have attempted to identify the key factors
compromising it, and indeed several approaches have
been proposed to aid in its multiple context analysis
(Macis et al., 2019; Martins & Cerqueira, 2018;
Hallewell Haslwanter, Neureiter, et al., 2018;
Hallewell Haslwanter, Fitzpatrick, et al., 2018;
Holthe et al., 2018). To tackle this issue the author’s
proposition was to empower the product
manufacturers and provide them a simple and feasible
way of evaluating and monitoring the product’s
usability. From all the available methodologies, the
one eligible to be executed in an enterprise setup, due
to its ihnerent cost and speed of execution, was the
heuristic-based. Alas, it also presents limitations that
compromise its adoption, namely its resultsaccuracy
and its restricted applicability.
Considering the challenges presented, this article
provides a parametrization of the usability guidelines
depicted in literature. Our aim is to: 1) optimize the
Romeiro, C. and Araújo, P.
Guidelines’ Parametrization to Assess AAL Ecosystems’ Usability.
DOI: 10.5220/0011043800003179
In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 309-316
ISBN: 978-989-758-569-2; ISSN: 2184-4992
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
subjectivity level typically found in heuristic-based
methodologies, 2) optimize their overall accuracy and
results consistency, 3) extend its accessibility/
aplicability to non-usability experts and 4) minimize
the effort typically related to their automation.
A thorough search in literature was conducted in
order to identify what can be learned from the best
practices depicted, how they can be applied in a
practical scenario and how the inclusion of
automation can be a feasible option in an enterprise
Usability is a multidimensional property that reflects
the scope in which a product/service is expected to be
used (Application, 2016; ISO 9241-11, 1998; Cruz et
al., 2015). Typically, both User Experience and
Usability are mixed during the product analysis
phase, given their broad scope and definition.
However, these properties have different purposes:
while User Experience focuses on the analysis of how
behavioural, social or environmental factors
influence the user’s product perception (Saeed et al.,
2015; Martins et al., 2015a; Quiñones et al., 2018),
Usability focuses on the efficiency, effectiveness and
satisfaction in which the user is able to accomplish a
certain goal during the product’s interaction process
(Quiñones et al., 2018). To ensure the product
usability from an early stage, it is mandatory to define
a set of guidelines to be adopted during the interface’s
implementation phase and methodologies to identify
usability bottlenecks.
2.1 Guidelines
The search for a set of golden rules that could assist
the team during the development cycle was explored
from an early stage.
In 1990 the authors Jakob Nielsen and Rolf
Molich proposed 10 heuristic principles (Molich &
Nielsen, 1990). In 1996 the author Jill Gerhardt-
Powals proposed 10 cognitive principles (Powalsa,
1996) focused on a holistic analysis of the usability
evaluation process. In 1998 the author Ben
Shneiderman proposed a set of 8 golden rules
(Shneiderman, 2010). In 2000 the authors Susan
Weinschenk and Dean Barker combined Jakob
Nielsen’s principles with vendor specific guidelines
to achieve a set of 20 principles (Science, 2016) that
intended to bridge the gap between the defined
principles and the typical environments in which they
were to be applied.
2.2 Methodologies
Regarded as an intrinsic part of the design and
development lifecycle, the usability methodologies’
main role is the identification and mitigation of
usability bottlenecks (Martins et al., 2015b).
From the multiple methodologies available
enquiries, inspection and test-based - this article
focuses on an inspection-based methodology
heuristic methodology. However, before applying
any guideline breakdown, it is important to identify
the main benefits and drawbacks, in order to address
what motivated the selection of such methodology in
the first place.
In terms of benefits, the heuristic methodology is
a quick and low cost approach that provides feedback
to the designers in an early development stage,
without the direct intervention of end-users. This
approach uses literature guidelines to evaluate the
interface and this assists the designers in identifying
correction measures to solve usability bottlenecks
detected. Regarding drawbacks, the ones most
frequently highlighted are: 1) the efficiency and
viability of any approach depend on expert’s know-
how regarding usability guidelines and best practises;
2) inability to evaluate usability in its full extent
indeed, the approaches evaluation scope does not
include user related metrics, such as user’s
satisfaction; 3) the uncertainty regarding the end-
results reliability, which can be tackled by including
a significant number of specialists with the proper
know-how in the development cycle, and 4)
costs/expenses (Molich & Nielsen, n.d.)(Federal
Aviation Administration, n.d.).
The dependency on expert’s know-how and the
execution restrictions identified in the heuristic
approach are challenges that the proposed
parametrization intends to mitigate, so as to ensure
that its execution is accessible to any non-usability
expert. However, it should be noted that applying a
set of well defined metrics to manually evaluate an
interface in terms of guidelines compliance level is a
time consuming task. Since parametrization is a first
step to define business rules to be consumed by a yet
to define tool, it is reasonable to explore the use of
automation mechanisms to handle such procedure.
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
The use of automation in the heuristic methodology
is an explored topic in the literature, that brings
several benefits such as: evaluation cost reductions,
maximization of the interface’s test coverage,
provisioning of mechanism to accurately assess the
gap between the actual and the expected results and
to predict design changes side effects and
independence from usability expert’s know-how
(Bakaev, Mamysheva, et al., 2017; Ivory & Hearst,
2001; Quade, 2015).
Nonetheless, the inclusion of automation neither
discards the need for manual testing, nor provides
mechanisms capable of evaluating usability to its full
extent (Ivory & Hearst, 2001). There are user related
metrics which are out of the scope of the heuristic
methodology approach, namely the user’s satisfaction
level - unmeasurable by currently available automatic
Note that the advantages that automation ihnerent
brings to the heuristic methodology applicability
motivated the scientific community to explore and
create tools to assist developers and end-users in the
usability evaluation process. The direct result of such
analysis was the definition of four tool categories,
each one with their unique characteristics:
interaction-based focused on the use of users’
interactions to evaluate the interface’s usability
(Bakaev, Mamysheva, et al., 2017; Bakaev,
Khvorostov, et al., 2017; Type & Chapter, 2021;
Limaylla Lunarejo et al., 2020; Paternò et al., 2017),
metric-based – focused on the definition metrics used
to quantify the interface’s compliance level with
usability guidelines defined in literature (Bakaev,
Mamysheva, et al., 2017; Bakaev, Khvorostov, et al.,
2017; Type & Chapter, 2021), model-based focused
on the definition of interaction models through the
use of Artificial Intelligence mechanisms to evaluate
the interface (Bakaev, Mamysheva, et al., 2017;
Bakaev, Khvorostov, et al., 2017; Type & Chapter,
2021; Todi et al., 2021), and the hybrid-based
(Bakaev, Mamysheva, et al., 2017; Bakaev,
Khvorostov, et al., 2017).
According to the environment in which these
solutions are integrated, a tendency towards the type
of categories adopted can be noticed.
3.1 Enterprise Context
In an enterprise context, the available options are
metric-based standalone tools that corroborate the
interface’s compliance level with the accessibility
guidelines. The elapsed time required to manually
check each individual guideline, as well as the
government accessibility guidelines compliance
policy (European Commission, 2010) (Pădure &
Independentei, 2019), were the two reasons that
further fostered the development of several tools for
web and mobile applications between 2010 and
2020 (Dynomapper
, AChecker
, UI Automator
, WCAG Accessibility Checklist
3.2 Academic Context
In an academic context, the solutions developed
focused on the evaluation of the multiple features
which define usability. An analysis of 96 scientific
articles ranging from 1997 to 2021 provides an
overview of the trends in the usability automation
domain (41% interaction-based, 26% model-based,
25% metric-based and 2% hybrid-base proposals).
The approach that is explored in the article intends
to follow the hybrid-based approach. We will
combine a metric-based approach with a model-based
approach; within the former, we have used the
definition of metrics to assert the interfaces’
compliance level with defined guidelines; within the
latter, we have checked the compliance level of the
actions executed within the interface of the
interaction models created and trained. By combining
the characteristics of both approaches, we have thus
created a heuristic methodology capable of checking
interface’s components and actions with neither the
external expert’s direct intervention based know how,
nor end-users’.
4.1 Principles’ Breakdown
Considering the highlighted principles and with the
objective of identifying aspects in common, a
parallelism between each author’s specific set and the
principles/definitions unique of each subset has been
Guidelines’ Parametrization to Assess AAL Ecosystems’ Usability
established. The generated output provided the
insights required to minimize the subjectivity in the
principles analysis and consequently define the
parametrization building blocks.
The considered guidelines within the
parametrization scope were the following: 1) Jakob
Nielsen’s principles, 2) the Shneiderman’s golden
rules and 3) the Weinschenk and Barker’s cognitive
principles. Each principle was grouped according to
its scope within the interface. Segmentation that took
into account the interface main building blocks: the
components and the actions (Galitz, 2002). As a
direct result the analysis was divided into three
scopes: component oriented (CO), action oriented
(AO); and section oriented (SeO).
For each guideline, the respective parametrization
is presented in Table 1, Table 2 and Table 3.
Table 1: Jakob Nielsen and Rolf Molich’s principles
parametrization (Galitz, 2002; Harley, 2018; Kaley, 2018;
Nielsen, 1999; Laubheimer, 2015; Budiu, 2014; Moran,
2015; Nielsen, 2001).
Type Parameters
Visibility of system status - Providing feedback
for each unique interactive state.
Visibility of system status - Providing a
progress bar indicator and a closure dialogue
when applicable.
Match between system and real world -
Including an icon within the component that
provides a real-world visual representation of
the component’s purpose; Excluding system
terminology from component’s text content.
User control and freedom - Ensuring action
Consistency and standards - Ensuring the
compliance of the components structure, look
and feel properties with pre-set values.
Error prevention - Restricting the user’s input;
Providing defaults; Disabling a control when
mandatory data is missing; Presenting warning
messages reporting any unconformities
regarding the input provided before action
Error prevention - Providing a confirmation
dialog; Including a resolution within the error
messages presented to the user; Providing an
option to cancel the action execution in any
given time.
Error prevention - Including a mechanism that
automatically saves the user’s work when an
abnormal event that prevents the interface
stability occurs.
Recognition rather than recall - Including hints
that identify the data type required, tooltips
with the description of the component’s action,
labels/icons that clarif
the com
onent’s action
purpose; Ensuring components’ consistency to
help the user recollect the component’s purpose
through its aesthetic.
Flexibility and efficiency of use - Providing
short keys to navigate across the interface
components and interact with them
Aesthetic and minimalist design - Avoiding the
use of highlights, shadows, glossy effects, and
3D effects; Including colour contrasts that
consider the accessibility guidelines defined for
the interface t
e created.
Help users recognize, diagnose, and recover
from errors - Providing a non-technical error
message that includes the reason which led to
the abnormal event and some advice on how to
recover from it; Ensuring that error messages
rovided do not exceed the 20 words limit.
Help and documentation - Providing, in the
global navigation menu, a dedicated option
where the user can access the interface’s
official documentation.
Table 2: Shneiderman’s golden rules parametrization
(Mazumder & Das, 2014; Rozanski & Haake, 2017;
Shneiderman et al., 2017).
Type Parameters
Offer informative feedback - Providing a task’s
completion rate and a progress bar for time
consuming operations; Including a clear
indication of the current section.
Design dialog to yield closure - Providing a
closure dialo
Support internal locus of control - Providing a
confirmation dialogue.
Support internal locus of control - Including a
clear indication of the user’s position and the
interface navigation hierarchy through
breadcrumbs; Providing a global navigation
Table 3: Weinschenk and Barker’s cognitive principles
parametrization (Nayebi et al., 2013; Nielsen, 2010;
Rempel, 2015; Kvasnicová et al., 2015).
Type Parameters
User Control - Ensuring action reversibility;
Providing a task’s completion rate and a
confirmation dialogue during the actions
User Control - Including a clear indication of
the current section; Providing a global
navigation menu.
Human Limitations - Ensuring interface
response time lower than 10s; Providing
stateful component’s capable of giving
feedback to the user.
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
Table 3: Weinschenk and Barker’s cognitive principles
parametrization (Nayebi et al., 2013; Nielsen, 2010;
Rempel, 2015; Kvasnicová et al., 2015) (cont.).
Type Parameters
Human Limitations - Providing text content in
a simple and direct manner; Avoiding
flourished font families and redundant
hyperlinks; Avoiding the use of unrelated
images within the section’s context.
Linguistic Clarity - Including hints that identify
the data type required and tooltips/labels with
the description of the component’s action
purpose; Avoiding the use of foreign words or
acronyms in the text content provided;
Aesthetic Integrity - Ensuring aesthetic
similarity, proximity, and continuity across
components from the same family or used to
erform a similar action.
Simplicity - Providing default in the multiple-
choice fields.
Simplicity - Providing mechanisms to display in
a gradual fashion the interface functionalities,
from a basic to an advanced settin
Predictability - Ensuring the component’s
Predictability - Including a clear indication of
the user’s position and the interface navigation
hierarchy through breadcrumbs; Providing a
lobal navi
ation menu.
Interpretation - Including mechanisms to
redict the user’s intents.
Technical Clarity - Presenting trustworthy
information according to the domain being
modelled b
the interface.
Flexibility - Providing mechanisms which allow
the user to chan
e the interface look and feel.
Precision - Ensuring that results/feedback
rovided matches user’s expectations.
Forgiveness - Providing mechanisms that allow
for reversion/recovery from any action
executed within the interface.
Note that there were principles in the Shneiderman
and Weinschenk and Barker’s principle subset that
have not been described, since they are conceptually
similar to principles whose evaluation process had
already been discussed. Principles such as 1) “Strive
for consistency”, 2) “Seek universal usability”, 3)
“Prevent errors”, 4) “Permit easy reversal of actions”
and 5)Reduce short-term memory load share the
same evaluation process described for their
counterparts in the Jakob Nielsen and Rolf Molich’s
set (respectively “Consistency and standards”,
“Flexibility and efficiency of use”, “Error
prevention”, “User control and freedom” and
“Recognition rather than recall”).
4.2 Real Environment Applicability
The parametrization defined provided a rule set to
assert the interface’s compliance level with the major
guidelines depicted in literature. However, its
applicability in a real environment is mandatory to
identify challenges behind its quantification in a real
use case. For this purpose, two e-health applications
were used: an academic prototype and an enterprise
solution available in the market.
The analysis evaluated the unique principles
within the guideline subsets selected for each
4.2.1 Academic Prototype
The evaluation of the academic prototype considered
the 106 actions and 356 components available in the
15 screens of the entire interface, and covered the
Jakob Nielsen and Rolf Molich, Shneiderman and
Weinschenk and Barker guidelines. The end-results
are presented in Table 4.
Table 4: Academic prototype evaluation results.
Subset Evaluation
Visibility of system status 69%;
Match between system and the real
world – 89%; User control and freedom
73%; Consistency and standards
90%; Error prevention 35%;
Recognition rather than recall 76%;
Flexibility and efficiency of use - 85%;
Aesthetic and minimalist design – 84%;
Help users recognize, diagnose, and
recover from errors 79%; Help and
Offer informative feedback 83%;
Design dialog to yield closure 74%;
ort internal locus of control
and Barker
User Control 83%; Human
Limitations – 63%; Linguistic Clarity
100%; Aesthetic Integrity 100%;
Simplicity – 78%; Predictability – 92%;
Interpretation – 0%; Technical Clarity –
83%; Flexibility – 100%; and Precision
According to the results obtained it was detected
a total amount of 1781 usability smells. From the
principles evaluated the lowest scores (<70%) were
related to “Error Prevention” in the Jakob Nielsen
subset, “Human Limitations” and “Interpretation” for
the Weinchenk and Barker subset.
For the Jakob Nielsen subset in terms of “Error
Prevention” the main bottlenecks identified are
related with the lack of an autocomplete mechanism
Guidelines’ Parametrization to Assess AAL Ecosystems’ Usability
in any of the components that receive an input from
the user, the lack of a mechanism that automatically
save the user’s work, the lack of error messages
providing clear indications of the type of
inconformities detected in the user’s input and the
lack of mechanisms capable of disabling the action
related controls when the view requirements are not
For the Weinchenk and Barker subset in terms of
“Human Limitation” it should be emphasized the
components’ lack of capability to store their previous
state, in order to make the user aware of his/her
previous interactions without forcing the user of
his/her memory. Regarding “Interpretation” principle
context the main bottleneck detect was related with
the lack of a mechanism in the system capable to
predict the user’s intentions or user’s input when the
interaction process is taking place.
4.2.2 Enterprise Application
is the name of the enterprise application
selected to be part of the parameter evaluation effort.
The evaluation took into account 523 actions and
1918 components from a total of 103 screens of the
entire interface. Regarding the principles covered due
to its technical depth it was opted to cover the Jakob
Nielsen and Rolf Molich subset, which already
provided a proper overview of the interface state. The
end-results are presented in Table 5.
Table 5: SmartAL evaluation results.
Subset Evaluation
Visibility of system status 52%; Match
between system and the real world 74%;
User control and freedom 59%; Error
prevention 23%; Recognition rather than
recall – 73%; Flexibility and efficiency of use
- 0%; Help users recognize, diagnose, and
recover from errors 30%; Help and
The analysis performed allowed the identification
of a total amount of 2844 usability smells. From the
principles evaluated the scores below the minimum
quality threshold defined (70%) were related with the
“Visibility of system status”, “User control and
freedom”, “Error prevention”, “Aesthetic and
minimalist design”, “Help users recognize, diagnose,
and recover from errors” and “Help and
documentation” ” in the Jakob Nielsen subset.
The parametrization proposed compiled the
knowledge depicted in the literature to provide an
objective approach to interpret the usability
guidelines to be applied in a heuristic evaluation
process. Its main differentiating factor is related to the
metrics definition process. Each guideline was
explored thoroughly to identify ideal practices that
enforce such principles. Practices that are typically
applied to address usability bottlenecks in each
guideline scope. By isolating the typical approaches
used it was possible to define binary metrics that
allow to check the compliance level of the interface
with the guidelines parametrized in this study. Thus
maximizing the evaluation’s results accuracy and
consistency, and the overall accessibility of the
heuristic methodology to users without expert
usability know-how.
The next iterations will be focused over checking how
the results obtained compare with the end-users
feedback. Information required to assert the metrics
validity and accuracy in the detection of critical
usability issues. Additionally the automation of the
current manual evaluation process is another topic to
be tackled to ensure the process applicability and
feasibility in an enterprise environment. Therefore an
effort will be performed to identify which metrics
defined are eligible to be automated to create a tool
capable of taking an interface, run a static analysis,
identify possible botlenecks and suggest
optimizations based on the metrics defined.
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