Past, Present and Research Challenge in Adaptive User Interfaces

Past, Present and Research
Challenges in Adaptive User
Interfaces
Eduardo Castillejo, PhD. student
DeustoTech - Deusto Institute of Technology, University of Deusto
http://www.morelab.deusto.es

December 9, 2013

Outline
Introduction
User Interfaces

Outline
Introduction
User Interfaces
Personal research

Outline
Introduction
User Interfaces
Personal research
Contributions

Outline
Introduction
User Interfaces
Personal research
Contributions
Evaluation

Outline
Introduction
User Interfaces
Personal research
Contributions
Evaluation
Conclusions

Outline
Introduction
User Interfaces
Personal research
Contributions
Evaluation
Conclusions
Questions And Feedback

Outline
Introduction
University of Deusto
User Interfaces
Personal research
Contributions
Evaluation
Conclusions

University of Deusto - Bilbao Campus

Past, Present and Research Challenges in Adaptive User Interfaces
Introduction

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University of Deusto

997 staff
> 12 K students (15% international)
125 anniversary in 2012
2 campus: Bilbao & San Sebastian

Introduction

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DeustoTech - Deusto Institute of Technology

Associated to Faculty of
Engineering, it belongs to
´
Fundacion Deusto
150 people divided in 7 research
units
Representing
DeustoTech-INTERNET, a.k.a.
MORElab – envisioning future
Internet research group

Introduction

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DeustoTech-INTERNET
Motto: “User-centred Intelligent Services for Anything,
Anywhere at Anytime”
Areas of research:
Context-aware Mobile Computing for Enhanced
User-Environment Interaction
Semantic Middleware for Embedded Wirelessly-connected
Devices
Smart Environments of Augmented Internet-connected
Objects
Ambient Assisted Living (AAL): adaptive accessible interfaces
and social robotics
Future Internet: Internet of Services, Internet/Web of Things
and Web of Data

Introduction

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DeustoTech-INTERNET Unit
Principal researcher:
´
˜
Diego Lopez-de-Ipina,
http://paginaspersonales.deusto.es/dipina/

It comprises:
4 lecturers
4 PostDoc
6 full-time researchers
7 PhD grant holders
3 MSc grant holders

24 people

Introduction

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DeustoTech-INTERNET Performance

Scientiﬁc:
2012: 66 publications
8 JCRs, 12 book chapters, 33 indexed conferences, 13 other
publications

2011: 48 publications
8 JCRs, 13 book chapters, 3 indexed conferences, 24 other
publications

Introduction

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Active projects

European projects:
1. Go-Lab: Global Online Science Labs for Inquiry Learning at
School (FP7-ICT-2011-8, Nr. 317601, IP project)
2. IES CITIES: Internet-Enabled Services for the Cities across
´
Europe, FP7, Comision Europea, CIP-ICT-PSP-2012-6, Pilot
Type B - CIP-ICT-PSP-P
3. SONOPA: SOcial Networks for Older adults to Promote an
Active life (AAL-2012-5-187 and AAL-010000-2013-13), AAL
call 5.

Introduction

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Active projects

Spanish projects (mostly applied research):
1. THOFU: Future Hotel Technologies, CENIT 2010, Spanish
IP-like project
2. ADAPTA. Adapting, validating and integrating open data for
governments and companies, IPT-2011-0949-430000
3. Social Awareness Based Emergency Situation Solver.
SABESS, IPT-2011-1052-390000
4. Migration towards the Cloud - mCLOUD,
IPT-2011-1558-430000
5. TALIS+ENGINE: Hybrid Cooperative and Semantic Reasoning
for Service Orchestration in Assistive Environments
(TIN2010-20510-C04-03), Basic Research project

Introduction

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Active projects

Basque projects (mostly basic research):
1. DYNUI: Capability and Context-aware Dynamic Adaptation of
User Interfaces for Ambient Assisted Living (PC2012-73A)
2. UCADAMI: User and Context-aware Dynamically Adaptable
Mobile Interfaces (S-PE12FD006)
3. SmarTUR: Tourism in Smart Intelligent Environments
4. DEUSTEK3: Research group recognized by the Basque
University system (IT745-13)
5. . . . upto 8

Introduction

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What we actually do?
Remote Labs & Internet-connected Objects:
GO-LAB – federation of remote labs to enable
cross-organization remote experiments
WebLab-Deusto – open platform to ease the deployment of
remote labs

Enabling Smart Assistive Environments:
THOFU – creating the ICT infrastructure for next generation
hotels and tourism including smart objects and sentiment
analysis
SONOPA – activity-aware social networks to promote social
interaction among elderlies
TALIS+ENGINE – fostering personal autonomy by ambiguous
context modeling, reasoning and services coordination
through triple spaces
DYNUI – user interfaces adaptable to user context, capabilities
and devices
Introduction

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Social Data Mining & Opinion Mining:
SABESS – extracting structured knowledge about
emergencies from social networks
THOFU – analysing information about hotel reviews to perform
sentiment analysis

Linked Data & Linked Data Apps:
IES CITIES – urban app ecosystems based on council and
government open data where users prosume data
ADAPTA – enabling a holistic LinkedData platform to adapt,
validate and exploit open data (dataset recommendation)
SmarTUR – tourism related LinkedData Apps (LinkedQR)

Introduction

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Semantic Embedded Middleware:
TALIS+ENGINE – coordination of distributed embedded
objects through Triple Spaces
Sustainable IoT – persuasive interfaces and cooperation
among smart connected objects to foster sustainability

Cloud Computing:
mCLOUD – migration of enterprise applications to the Cloud

Mobile Computing for Enhanced User-Environment
Interaction:
Q-Apps - Quality-in-use assessment framework for mobile
apps
KONTATU - Context-Aware Communication Means
Recommendation
LaguNFC - Enabling access to the digital world to the elderly

Introduction

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More info about our projects:
Projects page:
Semantic searcher and RDF descriptions

Introduction

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Sentiment analysis: Hotel review analysis
THOFU project
Localization data fusion
Reality Mining from user data

Introduction

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Social trends analysis
Imhotep project
What do we consider a “big” screen for a mobile phone?
Would a Japan individual consider the same screen “big”?

Introduction

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Web of Data: Waste-related LinkedStats
http://helheim.deusto.es/linkedstats/

Introduction

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SABESS: Social Data Mining for Emergency
Detection
Data gathered from social networks
NLP
Alarms triggering

Introduction

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Federating Labs for Remote Experimentation
using the Web
http://www.weblab.deusto.es

Scalable, web-based and experiment-agnostic remote
laboratory management system:

Introduction

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Social Coffee

https://twitter.com/Social_Coffee
Introduction

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Collaborative Eco-aware Everyday Things

Introduction

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Persuasive Eco-aware Everyday Things

Introduction

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PhDs defended in the group
“CONCERT: A new framework for contextual computing in
tourism to support human mobility”, by Carlos Lamsfus,
´
˜
supervised by Diego Lopez-de-Ipina y Aurkene Alzua,
29/10/2010
“Middleware Framework for the Conﬁguration and
Personalisation of Ubiquitous Environments by the Final
´
˜
User” by Aitor Uribarren, supervised by Diego Lopez-de-Ipina
and Rosa Iglesias, 01/07/2011
“The web as a suitable execution platform to precisely
represent audio-visual contents and registering user
interaction” by Pablo Garaizar, supervised by Dr. Diego
´
´
˜
Lopez-de-Ipina y Dr. Miguel Angel Vadillo, 29/04/2013
“New protocols for the discovery and automatic composition
of services in ad hoc mobile networks”, by Unai Aguilera,
´
˜
supervised by Dr. Diego Lopez-de-Ipina, 3/05/2013
Introduction

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PhDs defended in the group
“Transitive and Scalable Federation Model for Remote
˜
´
Laboratories” by Pablo Orduna Fernandez, supervised by Dr.
Javier Garcá Zubia, 31/05/2013
ı
“Plataforma web y metodologá para el desarrollo de
ı
´
sistemas sensibles al contexto basada en la colaboracion
entre programadores y expertos en el dominio” by David
´
˜
Martń del Canto, supervised by Dr. Diego Lopez-de-Ipina y
ı
Dra. Aurkene Alzua, 7/6/2013
“Towards more Reliable and Efficient Intelligent
Environments: Uncertainty, Vagueness and Reasoning
Distribution” by Aitor Almeida Escondrillas, supervised by Dr.
´
˜
Diego Lopez-de-Ipina, 10/06/2013

Introduction

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Some selected publications
User-Aware Location Management of Prosumed
´
˜
Micro-services. Bernhard Klein, Diego Lopez-de-Ipina,
´
Christian Guggenmos and Jorge Perez. Interacting with
Computers, ACCEPTED, in press, ISSN:0953-5438, JCR
Impact Factor (2012): 1.158, Q2(COMPUTER SCIENCE,
CYBERNETICS), ranked 10/21, OXFORD UNIV PRESS
Towards federated interoperable bridges for sharing
˜
educational remote laboratories. Pablo Orduna, Philip H
´
˜
Bailey, Kimberly DeLong, Diego Lopez-de-Ipina, Javier
Garcia-Zubia. Computers in Human Behavior (Journal),
http://dx.doi.org/10.1016/j.chb.2013.04.029, ISSN 0747-5632,
JCR Impact Factor (2011): 2.293, Q1(PSYCHOLOGY,
MULTIDISCIPLINARY), ranked 22/125,
PERGAMON-ELSEVIER SCIENCE LTD, March 2013.
Software Engineering Aspects of Ubiquitous Computing and
´
˜
Ambient Intelligence. Diego Lopez-de-Ipina, Sergio F. Ochoa
´
and Jose Bravo. Science of Computer Programming,

Introduction

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Assessing Ambiguity of Context Data in Intelligent
Environments: Towards a More Reliable Context Managing
´
˜
System, Aitor Almeida and Diego Lopez-de-Ipina, Sensors
(Journal). Volume 12, Issue 4, pp 4934-4951. MDPI. JCR
Impact Factor (2011): 1.739,
Q1(INSTRUMENTS&INSTRUMENTATION), ranked 14/58.
http://dx.doi.org/10.3390/s120404934. April 2012
Imhotep: an approach to user and device conscious mobile
˜
applications, Aitor Almeida, Pablo Orduna, Eduardo
˜
´
Castillejo, Diego Lopez-de-Ipina, Marcos Sacristan, Personal
and Ubiquitous Computing (Journal). Springer. Vol. 15, no.4.
pp.419-429. JCR Impact Factor (2011): 0.938,
Q2(COMPUTER SCIENCE, INFORMATION SYSTEMS),
ranked 66/133. ISSN: 1617-4909.
http://dx.doi.org/10.1007/s00779-010-0359-8. January 2011
Introduction

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For more details look at:
http://paginaspersonales.deusto.es/dipina/
publications.html
http://www.morelab.deusto.es/labman/publications/

Introduction

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Other achievements
1 spin-off came up from the research group,
http://www.symplio.com/
Open source contributions:
Imhotep framework (Apache license):
http://www.morelab.deusto.es/imhotep/
WebLabDeusto – https://www.weblab.deusto.es/web/
Otsopack – http://code.google.com/p/otsopack/
Zxing – databar – http://code.google.com/p/zxing/
(Barcode Scanner - Android)

Open dataset released in CKAN about MORElab’s people,
projects and publications:
http://ckan.linkeddata.es/dataset/morelab
Our datasets are scheduled to appear in next
http://lod-cloud.net/
˜
MORElab researcher Pablo Orduna was awarded MIT TR35
SPAIN in 2012
Introduction

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Activities Organized @ MORElab

Open Hack Day 2013:
http://dev.morelab.deusto.es/hackathon/index.php/P%
C3%A1gina_principal#Resultados

Random Hacks for Kindness @Bilbao
http:
//www.morelab.deusto.es/index.php/news-287822021/
past-news/405-random-hacks-for-kindness-bilbao

AppCircus in Bilbao
http://appcircus.com/event/appcircus-en-bilbao

Apps4BetterWorld
http://www.morelab.deusto.es/concurso/index.html

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MORElab’s website

Introduction

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Outline
Introduction
User Interfaces
UIs, GUIs, AUIs and more
Personal research
Contributions
Evaluation
Conclusions

User Interfaces is the space where interaction between
humans and machines occurs.
The goal of this interaction is effective operation and control
of the machine on the user’s end, and feedback from the
machine.

User Interfaces

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Evolution:

User Interfaces

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Graphical User Interfaces are a type of user interface that
allows users to interact with electronic devices through
graphical icons and visual indicators
The actions in GUI are usually performed through direct
manipulation of the graphical elements.

User Interfaces

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Adaptive User Interfaces are a type of user interface that
change their layout and elements to the needs of the user
or context and is similarly alterable by each user.
Examples.
PC web browsing Vs. mobile (content adaptation)
Accessibility tools for mobile devices
SW tools menus personalization
...

Adaptive Vs. Adaptable.
Adaptable: Android brightness control, Ofﬁce menus, etc.
No context-aware...

User Interfaces

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It is becoming more and more difﬁcult to distinguish between
user interfaces, graphical and adaptive user interfaces
Technology and interaction boundaries allow researches to
seek for alternatives
For example:

User Interfaces

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Beyond – Collapsible Input Device for Direct 3D Manipulation
beyond the Screen

http://vimeo.com/11015834
User Interfaces

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Samsung Galaxy Note 3 display adaptability

User Interfaces

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Mobile physical adaptive display (fake)

User Interfaces

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inFORM - Interacting With a Dynamic Shape Display

http://vimeo.com/79179138

User Interfaces

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Outline
Introduction
User Interfaces
Personal research
Adaptive User Interfaces
From the Imhotep framework. . .
. . . To New Research Challenges
Addressed Research Areas
Contributions
Evaluation
Conclusions

Adaptive User Interfaces (I)

Each user has his own preferences.
Moreover, there are some groups of users who have special
needs and capabilities: people with disabilities and the
elderly.
Furthermore, people with the same disability do not react in
the same way.

Past, Present and Research Challenges in Adaptive User Interfaces research
Personal

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Adaptive User Interfaces (II)

One of the main objectives of Adaptive User Interfaces is to
reduce the interaction problems that these groups suffer.
In Europe the share of people aged 65 represent a 17% of
the current population. By the year 2060 this ﬁgure is
projected to rise to 30% 1 .
In fact, the European Commission states, “the EU would
move from having four people of working-age to each person
aged over 65 years to about two people of working-age”

1

http://ec.europa.eu/economy ﬁnance/articles/structural reforms/2012-0515 ageing report en.htm

Personal

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Adaptive User Interfaces (III)

Limitations:
Adaptive systems solutions are very domain dependent.
Airport scenarios for recommending services (users and
devices are modeled), smart homes for controlling and share
information between devices (again, users and devices),
desktop to mobile web content adaptation (user’s
preferences). . .
But it is true that sometimes it is inevitable. . . (e.g., medical
environments).
This means: similar entities considered but using different
models, techniques, approaches. . .

Personal

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Adaptive User Interfaces (IV)

Limitations:
Unrelated models (users, context, devices. . . ).
They usually are considered like independent entities. But. . .
Context might affect user’s capabilities.
Devices can bother the user.

There is no standardization for designing these systems.
Not for the models.
Not for the methodology.
Only for several quality and usability goals (ISO 9126-I, ISO
9241-II. . . ).

Personal

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Adaptive User Interfaces (V)

Limitations:
External server dependency.
Static information about the user or the device.
...
In fact, there are several accessibility tools in mobile devices
(Android, iPhone. . . )
The problem is that they are not adaptative. They are
adaptable.
It requires the user intervention for conﬁguring the model2 .

They do not evolve through time. They are static.
No learning.

2

Heckmann, D., 2005. Ubiquitous user modeling. IOS Press.

Personal

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The Imhotep Framework (I)
Imhotep3 is a framework that tries to ease the development of
accessible and adaptable user interfaces taking into
account both the user capabilities and the device
characteristics.
Preprocessor directives within the application’s source code
evaluated in the server.
User’s disability (e.g., blindness) is configured in a
configuration mobile application.
The configured profile is sent to the server, which compiles the
application’s source code taking into account the received user
profile and the defined preprocessor directives.
Once the binaries have been generated by the server, the
adapted application is sent back to the user’s device.
3
˜
´
˜
´
Almeida, A., Orduna, P., Castillejo, E., Lopez-de-Ipina, D., Sacristan, M.,
2011. Imhotep: an approach to user and device conscious mobile applications.
Personal and Ubiquitous Computing 15, 419–429.

Personal

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The Imhotep Framework (II)
Imhotep:

Preprocessor directives:

Personal

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Example: AssistedCity

a) Adapted user interface for a tour-guide application
for a blind user conﬁguration proﬁle. The interaction
channel is mainly conducted by voice commands
and text-to-speech.
b) Default tour-guide application, where the interaction
channel is mostly visual.

Personal

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The Imhotep Framework (III)

Imhotep main limitations:
Static.
External server dependency.
Pre-known and static user and device capabilities.
Unreal user capabilities:
What (sight) graduation is the maximum for justifying a user
interface change? 30%? 40%?
We are no doctors. . .

No context-awareness.

New challenges
http://www.morelab.deusto.es/imhotep/

Personal

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User’s Context Disabilities
There are several groups of people who suffer from several
disabilities: people with disabilities and elder people.
These people have conditions which make difﬁcult to carry
out diary tasks.
But the truth is that all of us suffer from certain disabilities
during the day. . .
When sunlight reﬂects on a glossy screen (e.g., our
smartphones’ screen) our sight capability is reduced.
When we try to call a friend in a concert, in the subway, in a
crowded street. . . our hearing capability is “harmed”.

User’s context disability depends on how context might
affect user’s capabilities when several tasks are being carried
out.

Personal

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User’s Context Disabilities

Example:

Personal

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Research Objectives (I)

Now, regarding all these problems, what is my research
about?
To reduce the users’ context disabilities through a
dynamic methodology which employs several dynamic
models and considers users’ conﬁgured capabilities,
the set of characteristics which deﬁnes the current
environment where users actually are and the actual
devices they use by adapting mobile applications’ user
interfaces to the current situation.

Personal

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Research Objectives (II)
Main goals:
1. To design a methodology where users, context and devices
evolve because of the context variability.
2. To model this evolution through a process which will be able
to dynamically adapt the best and most suitable user interface
for each precise context situation.
3. To validate the results by capturing the interaction between
the user and the adapted user interface.
4. To demonstrate that it is possible to develop dynamic
adaptive applications which are able to reduce users’
disabilities taking into account their own characteristics, the
devices’ ones and those which belong to the current context.

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Areas of research

This research tackles the following areas:
Context-awareness
Human-Computer Interaction
Adaptive user interfaces
Inclusive design
Considerate computing

Personal

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Areas of research

This research tackles the following areas:
Context-awareness
Human-Computer Interaction
Adaptive user interfaces
Inclusive design
Considerate computing

Personal

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Outline
Introduction
User Interfaces
Personal research
Contributions
Interdependences
Models
Adaptation Process
Evaluation
Conclusions

Interdependences (I)
An analysis of the
context/user and
device/user inﬂuence.
For example, which
context parameters
affect users’
capabilities.

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Interdependences (II)
An inﬂuence association between entities (user, context,
device) and the ﬁnal adaptation of each UI component.
DEVICE

DV_CONTRAST

US_BRIGHTNESS

DV_BRIGHTNESS

US_OUTPUT

BRIGHTNESS

US_CONTRAST

CTX_TEMPERATURE

CTX_LUMINOSITY

CONTRAST

DV_OUTPUT

VIEW_SIZE

CONTEXT

US_VIEW_SIZE

DV_ACCELERATION

US_TEXT_COLOR

DV_TEXT_COLOR

VIEW_COLOR

USER

US_TEXT_SIZE

US_VIEW_COLOR

DV_BATTERY

TEXT_SIZE

DV_ORIENTATION

TEXT_COLOR

DV_VIEW_SIZE

DV_TEXT_SIZE

US_INPUT

DV_VIEW_COLOR

CTX_NOISE

DV_VOLUME

DV_INPUT

VOLUME
INPUT

OUTPUT

INPUT

OUTPUT

Contributions

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US_VOLUME

Models
A user, context and device model which is:
Domain independent. It gathers most of the most signiﬁcant
models in the literature.
HCI, Smart Environments, Ubiquitous Computing. . .

Physical/medical user’s disabilities independent.
The user “conﬁgures” his/her capabilities.

A usability model (based on usability metrics) which allows
to analyze the interaction between the user and the adapted
user interface (this model will be necessary for the
evaluation).
Collect user data

Extra: A Java (Android) library for developers to ease the
design of dynamic adaptive user interfaces.

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Literature Context Models (I)

Contributions

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Context Model (II)
Proposed model:

Contributions

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Context Model (III)

Divided into two main groups:
Primary:
Physical context : Environment information from sensors
(e.g., temperature, absolute location, time. . . ).
High-level context : Physical richen information (e.g., “it’s
cold”, “ofﬁce”, “morning”. . . ).

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Context Model (IV-a)

Secondary:
Environment metadata : Environment knowledge is
associated to sensors. A sensor can provide
information about the temperature (23o C). But
this information by itself is poor in a
context-aware system. Environment metadata
can describe and enrich this knowledge,
providing time and location data. For example,
“the current temperature at 12:00 AM in Bilbao is
13o C”.. . .

Contributions

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Context Model (IV-b)

Secondary:
Virtual environment : Combining the knowledge of the
categories above it is possible to extract
high-level information. For example, if a sensor
shows that there is a light turned on at ofﬁce, we
can deduce that there are people working. This
way, we avoid the usage of other sensors to
indicate this activity.. . .
“Stressful conditions” : ???

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Context Model (V)

Stressful conditions :
We need something more to characterize the
current situation that involves user, its current
context and the device.
Incongruent adaptations: deﬁned by several
environment parameters that induce the platform
to perform a certain adaptation for the current
conditions. However, the result of this
adaptation, although it can be linearly aligned
with the context characteristics, can be
incongruent.

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Context Model (VI)

Stressful conditions :
Activities help to understand the current user,
context and device situation.
Activities enrich the environment information.
Manipulating with hands or being at a certain
location (like a library, where people are in
silence) are aspects that we should consider
when we model context. For example, driving or
cooking restrict user capabilities momentarily.
This way, we can state that these activities
impede the user.

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Context Model (VII)

Several groups of activities that should be considered:
Activities that limit the use of the hands.
Activities that limit the use of the voice.
Activities that limit the user’s sight capability.
Activities that limit the user’s hearing capability.
Activities that limit the user’s mobility? (not sure)
Activities that limit the user attention.
Combinations of these activities.

Contributions

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Literature user models

Contributions

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User model (I)

Proposed model:
Modeling users’ concrete disabilities is troublesome (we are
no doctors) 4 .
Instead of this, it is possible to let the users to “conﬁgure” their
disabilities.

4
Casas, R., Blasco Mar´n, R., Robinet, A., Delgado, A., Yarza, A., Mcginn, J.,
ı
Picking, R., Grout, V., 2008. User modelling in ambient intelligence for elderly
and disabled people. Computers Helping People with Special Needs 114–122.

Contributions

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User model (II)

Contributions

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User model (III)
The model is categorized as follows:
Interface : which models the I/O “preferences” (e.g., if the
user is blind or has any sight problem, he/she
would like to interact with gestures, voice control,
etc.).
Display : for taking care about the orientation,
brightness, colors (color blindness). . .
Audio : volume, language. . .
View : it conﬁgures each View or Control displayed in
the device’s screen (i.e., a Label, EditText,
ComboBox. . . ).
Others : which gathers several extra interaction
features.
Minimum UI
Contributions

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Device Model (I)
Proposed model:

Contributions

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Device Model (II)

It models several dynamic capabilities (“Status”).
These capabilities should be taken into account when facing
any adaptation process (e.g., current brightness could be
enough to avoid an adaptation, current battery levels might
advise against new processing activities. . . )

Contributions

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Models’ granularity (I)

“The performance of a model depends critically on the
granularity, for example the choice of precision of the
parameters. Too high precision generally involves modeling of
accidental noise and too low precision may lead to confusion
of models that should be distinguished” 5

5

´
Gao, Q., Li, M., Vitanyi, P., 2000. Applying MDL to learn best model
granularity. Artiﬁcial Intelligence 121, 1–29.

Contributions

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Models’ granularity (II)
How do I tackle this?
Each modeled entity is considered separately:
Context:
Different levels (low-level parameters from sensors Vs.
high-level information)
“Activities” (stressful conditions) are not considered as a list of
speciﬁc activities (instead of “cooking” we consider an activity
that impedes the use of the hands and distracts user attention)

User:
Capabilities are not “medical-based”, they are conﬁgured

Device:
As Context, several “physical” data is required
(battery levels, screen orientation, available memory. . . )

Contributions

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User’s model granularity

Contributions

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Context’s model granularity

Contributions

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Device’s model granularity

Contributions

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Modeling technique

The power of the mark-up language
The power of semantics
So. . . Ontologies Vs. XML
The problem with ontologies is to ﬁnd those that already model
those parameters that I’m interested in (e.g., the temperature,
luminosity, etc.)

Contributions

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Usability (I)
A usability model is proposed to collect the user interaction
data with the adapted user interface.
This model is based on several “quality of use” metrics
focused on usability:
Task effectiveness.
Task completion.
Error frequency.
Task time:
Time to start the task.
Time to ﬁnish the task.

Satisfactory clicks.
Error clicks.
...

ISO/IEC 9126-4.

Contributions

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Usability (II)

The idea is to get a % of the compatibility between the user
and the adapted user interface.
The user is his own “expert”.
This way the platform learns from the interaction reﬁning the
UI results.

Contributions

84 / 99

Usability (III)

The % of the compatibility is calculated comparing a best
situation scenario with the adapted user interface.
The best scenario is the one on which the user can perfectly
interact and perform the desired task.
To “capture” this scenario a mobile user interface conﬁgurator
has been developed.
The idea is to compare the usability metrics between the
perfect and the adapted user interfaces.

Contributions

85 / 99

Usability (IV)
The user-interaction “conﬁgurator”:

https://github.com/edlectrico/dynamic-capability-tester
Contributions

86 / 99

Adaptation Process (I)
Dynamic process:

Contributions

87 / 99

Adaptation Process (II)
Context Manager : It gathers data from the environment.
User Capabilities Reasoner : It “mixes” context and user’s
capabilities (user’s context capabilities) to generate
an updated user capabilities profile using the
context-user influence taxonomy.
UI Reasoner : With the updated user capabilities and the device’s
ones it search for the best user interface for the
current situation. It searches in a repository for
previous similar configurations to avoid unnecessary
adaptations. It uses several rules to recommend the
best configuration.
UI Adaptation Engine : It is a dynamic Android module which
performs on-the-fly user interface adaptations.

Contributions

88 / 99

Adaptation Process (III)
Historical adaptation.
Standard UI.

Contributions

89 / 99

Evaluation (I)

Of the model:
Scenarios deﬁnition.
Not adapted Vs. adapted user interfaces.
Android accessibility user interface Vs. adapted user interfaces.
...

State of the art models comparison. the resulting UIs.

Of the generated/adapted UI: Through the interaction model
(Best case Vs. Adapted case)
Usability models.

Evaluation

91 / 99

Evaluation (II)
Spanish Blind Organization:

Basque Deaf Organization:

Evaluation

92 / 99

Contributions status (I)
Objective: To reduce user’s context disabilities by using an
adaptive user interface dynamic process.
Contributions:
1. An analysis of the context/user and device/user inﬂuence.
Status : First version.
2. An interdependency table that shows how each entity (user,
context, device) affects the ﬁnal adaptation.
Status : First version.
3. A user, context and device dynamic model for adaptive user
interface domains.
Status : First XML versions. Integrated with the Android
process.

Conclusions

94 / 99

Contributions status (II)
A usability matrix (based on usability metrics) which allows
to analyze the interaction between the user and the adapted
user interface.
Status : Not started. First: analyze which
usability/productivity metrics are needed (ISO
9126-4).
A dynamic process which using these models and
interdependencies allows developers to design adaptive
applications.
Status : A ﬁrst operative Android version (next slide).
The evaluation scenarios deﬁnition.
Status : Not started yet. . .

Conclusions

95 / 99

Contributions status (III)
Updated methodology:

Conclusions

96 / 99


...?


98 / 99

Past, Present and Research Challenge in Adaptive User Interfaces

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Past, Present and Research Challenge in Adaptive User Interfaces