=Paper= {{Paper |id=Vol-2164/paper2 |storemode=property |title=An Ontology-Driven Elderly People Home Mobilization Approach |pdfUrl=https://ceur-ws.org/Vol-2164/paper2.pdf |volume=Vol-2164 |authors=Sophia Karagiorgou,Dimitrios Ntalaperas,Georgios Vafeiadis,Dimitrios Alexandrou,Konstantinos Perakis,Dimitrios Baltas,Claudiu Amza,Anna Wanka,Hermine Freitag,Marije Blok,Martin Kampel,Veerle de Rond,Thomas Munzer,Rainer Planinc |dblpUrl=https://dblp.org/rec/conf/semweb/KaragiorgouNVAP18 }} ==An Ontology-Driven Elderly People Home Mobilization Approach== https://ceur-ws.org/Vol-2164/paper2.pdf

An Ontology-Driven Elderly People Home
Mobilization Approach?
Karagiorgou Sophia1 , Ntalaperas Dimitrios2 , Vafeiadis Georgios2 , Alexandrou
Dimitrios1 , Perakis Konstantinos1 , Baltas Dimitrios2 , Amza Claudiu2 , Wanka
Anna3 , Freitag Hermine4 , Blok Marije5 , Kampel Martin6 , Veerle de Rond7 ,
Münzer Thomas8 , and Planinc Rainer9
1
UBITECH LTD
2
Bluepoint Consulting Srl
3
Department of Sociology, University of Vienna
4
Samariterbund Wien
5
National Foundation for the Elderly
6
Computer Vision Lab, Vienna University of Technology
7
SilverFit
8
Geriatrische Klinik St. Gallen
9
CogVis Software and Consulting GmbH

Abstract. The use of virtual reality games, known as “exergaming”, is
gaining more and more interest as a mobilization tool and as a key piece
in the delivery of quality health, especially in elderly people. Mobility
tracking of elderly people facilitates the extraction of useful spatiotem-
poral characteristics regarding their activities and behavior at home.
Currently, the analysis of human mobility is based on expensive technolo-
gies. In this paper, we propose a semantic interoperability agent which
exploits mobility tracking and spatiotemporal characteristics to extract
human profiling and give incentives for mobilization at home. The agent
exploits an extended ontology which facilitates the collation of evidence
for the effects of exergaming on the movement control of older adults.
In order to provide personalized monitoring services, a number of rules
are individually defined to generate incentives. To evaluate the proposed
semantic interoperability agent, human mobility data are collected and
analyzed based on daily activities, their duration and mobility patterns.
We show that the proposed agent is robust enough for activity classifi-
cation, and that the recommendations for mobilization are accurate. We
further demonstrate the agent’s potential in useful knowledge inference
regarding personalized elderly people home care.

Keywords: sensor and health data integration · ontologies and data
models · health semantics · recommendations · knowledge management.

1 Introduction
The increasing trend in the number of elderly people is a major public health
challenge. Home support is an important preoccupation for the elderly and their
families. It is known that physical activity is important for older adults at any
age and health status, i.e. from a 50-year-old up to a 80-year-old.
?
This work will be published as part of the book ”Emerging Topics in Semantic
Technologies. ISWC 2018 Satellite Events. E. Demidova, A.J. Zaveri, E. Simperl
(Eds.), ISBN: 978-3-89838-736-1, 2018, AKA Verlag Berlin”.
2 Karagiorgou et al.

However the home is not a place without risk for the older adults. The means
to enhance the health and quality of life by motivating them for physical training
in an entertaining way with real-time interventions are very limited. A nowa-
days popular solution for enhancing the physical activity of older adults is to
provide them with computer games which are played via body movement and
thus have the inherent effect of unobtrusive physical exercise, i.e. the so-called
“exergaming” [21]. These games are based on common sensors that track the
user’s movement such as the Microsoft Kinect [23] and can therefore easily be
played at home. The core idea of exergaming is that they persuade older adults
to exercise more simply because they enjoy to play, but there are known barriers
to be overcome.
The game must be designed for non-skilled users in order to be accepted by
older adults. This includes clear user interfaces, suitable game topics, avoidance
of small objects and the encouragement by visual and auditive feedback. Also
the social factor and personal preferences, e.g. by providing multi-user games,
variety, etc., have to be considered. A very crucial requirement is to mind the
mobility of the individual user, as many age-related processes have an impact
on the ability to move which may differ from the skills needed to play a game.
Common age-related changes are decrements in balance, gait, strength, impacts
on visual and hearing senses as well as impairment of memory, attention and
vigilance. Additional aspects to be addressed are the longer reaction, the overall
movement times and the increased risk of falling. The high importance of this
personalization aspect is given by its high correlation with the older adult’s
motivation to play and hence with the acceptability of exergaming, i.e. if a frail
80-year-old is confronted with games that require unachievable movements, she
will feel over strained and soon lose interest. On the other hand, if a healthy
65-year-old is confronted with unchallenging game tasks, she will feel bored and
will lose interest as well.
The required personalization can be achieved by manually configuring the
exergaming platform based on the supposed mobility of the older adult, but this
has many drawbacks, e.g it is hard to assess the mobility beforehand, mobility
can change over time, and the individual preferences are not considered. Hence,
what is needed is a platform that automatically and continuously adapts to the
user’s preferences, skills and mobility. Despite the ongoing research and devel-
opment in the area of exergaming, performed both by industry and the research
community, the problem of personalized recommendations are not properly ad-
dressed.
The goal of this work is to contribute towards the personalization of elderly
people home care [22] by developing an ontology-driven semantic interoperabil-
ity [26] agent that facilitates diverse human mobility activities to be captured
and monitored for motivating further incentives and recommendations. More
particularly, the main contributions of our work are as follows:
1. We introduce a semantic interoperability agent that incrementally builds a
knowledge base and autonomously learns from the individual playing habits,
what kind of games are preferred by the user, as well as her playing skills from
the game performance, and utilizes this information to provide personalized
and inspiring incentives for future mobilization;
An Ontology-Driven Elderly People Home Mobilization Approach 3

2. We extend a data model described by a standardized ontology which is
familiar to domain experts and expose data in a standardized format by
supporting interoperability with existing systems and other services;
3. We evaluate the semantic interoperability agent using real-world datasets
demonstrating its effectiveness and efficiency. The outcome is a personalized
mobility model that is used to provide recommendations and incentives to
the end-user.
Figure 1 shows a high-level architecture of the semantic interoperability
agent. The remainder of this paper is organized as follows. Section 2 reviews
related work on ontology-driven techniques for home health care and positions
our approach accordingly. Section 3 presents our motivation and our approach
for ontology-driven semantic-aware health care insights and inference. Section 4
validates our proposal using various quantitative and qualitative metrics. Section
5 concludes the paper, also pointing out interesting future research directions.

Fig. 1. High-Level Architecture.
2 Related Work
Various approaches have been proposed for using mobility tracking data to facil-
itate semantic-aware health care services. In the following, we present a review
of the literature by using a categorization of the methods according to their
applied use and the type of the devised techniques.
Several works present literature reviews of data-driven health care systems.
Zenunia et al. [8] explore several repositories for ontology and semantic data
management for health services, conduct a survey on most representative appli-
cations in semantic health care and analyze the data mining and data analytics
approaches currently used to find useful patterns and extract knowledge in these
repositories. Chao et al. [7] present a literature review which summarizes and
synthesizes the impact of using the Nintendo Wii exergaming [24] in older adults
by concluding that it is not a very promising intervention means to improve phys-
ical function, cognition and psychosocial outcomes but it is instead a safe and
feasible tool to engage them in exercise. Sarafianos et al. [13] review the recent
advances in 3D human pose estimation from image sequences. A taxonomy of the
current approaches is proposed based on the input and their key characteristics.
4 Karagiorgou et al.

Other approaches consider spatiotemporal data analysis, information and im-
ages collected from wearables and other monitoring devices. Temporal aspects
to model behavior are considered by Floeck et al. [1], where activity data are ob-
tained from different sensors within a flat by learning an inactivity profile from
sensorial data in order to model the temporal behavior, but does not consider
spatial aspects. Another approach introduced by Felzenszwalb et al. [2] focuses
either on temporal or spatial aspects of the scene. Hence, the combination of both
spatial and temporal knowledge was recently introduced by Planinc et al. [9] and
resulted in a solid foundation for a behavior model. Their spatiotemporal be-
havior model analyses the scene in order to provide spatial knowledge about
regions of interest as well as functional areas within a room (i.e. walking and
sitting areas). The behavior over time is modeled within each area separately by
Planinc et al. [6], by using activity histograms or inactivity profiles and modeling
activity throughout the day.
Another category, to which the present work most closely relates, involves
ontology-driven approaches for health care services. A HealthIoT ontology is pro-
posed by Rhayem et al. [14] to overcome the problem of both medical connected
objects and their data to achieve efficient semantic representation to facilitate
patient monitoring, diagnosis and decision making. A hybrid framework which
supports knowledge-driven and probabilistic-driven methods for event recogni-
tion is presented by Crispim-Junior et al. [10]. The framework separates seman-
tic modeling from raw sensor data by using an intermediate level of semantic
representation, introduces an algorithm for sensor alignment that uses concept
similarity to address the inaccurate temporal information and proposes a com-
bined use of an ontology language, to overcome the issues arising at the model
definition. Lasierra et al. [5] developed an ontology-driven solution that enables
a wide range of remote chronic patients to be monitored at home. Riaño et
al. [4] introduce an ontology for the care of chronically ill patients and imple-
ment personalization processes which facilitate the support of a decision making
tool targeted at health care professionals.
Several methods address health care services from an applied perspective
based on gamification and applications. An application for mobile devices, de-
veloped for the Android platform in the JAVA programming language and XML
markup to identify the frailty phenotype among the elderly was proposed by
Silva dos Santos et al. [15] which allows the monitoring of the clinical status
and prognosis of the patient. Harris et al. [11] studied the effects of exergaming
on the balance and postural control of older adults and people with idiopathic
Parkinson’s disease. Their findings suggest that exergaming can be an appro-
priate therapeutic tool for improving balance and postural control. Dubois and
Charpillet [16] proposed a low-cost ambient system for helping elderly to stay at
home. The system recognizes the activity of the person based on Hidden Markov
Models and measures gait parameters from the analysis of simple features ex-
tracted from depth images. Vernon et al. [12] examined the reliability of using
the Microsoft Kinect Timed Up and Go component and whether it helps to
improve patient’s performance and physical conditions following a stroke.
From the point of sociology view, Wanka and Gallistl [17] suggest that the
study of aging and technologies can profit from a comprehensive integration
An Ontology-Driven Elderly People Home Mobilization Approach 5

of theories from the sociology of aging, critical gerontology and science-and-
technology offerings to facilitate active living.
Although the current approaches of data-driven health care systems use mon-
itoring devices, ambiguous semantics and data curation from diverse sources,
each individual work merely focuses on a single one of them. Compared to
the aforementioned approaches, the proposed method differs in that it exploits
user-driven behavioral characteristics using spatiotemporal information to pro-
vide concrete and targeted recommendations. It also infers useful personalized
knowledge which is built incrementally from individual users movements and
behavior at home and is delivered back to the end-user in the form of recom-
mendations and incentives for exercise. This work introduces a semantic inter-
operability agent which promotes easy data exchange with existing and other
systems and efficiently blends data-driven and semantic-aware health services to
fuel personalized interventions which improve the self-esteem and the quality of
elderly people life. A fringe benefit of the agent is that in this way, it supports
several frameworks as it is based on standardized data format and provides the
flexibility to be built on top existing and future systems.

3 Semantic Interoperability Agent
The semantic interoperability agent proposed in this paper consists of 4 com-
ponents: the Ontology, the Data Alignment, the Personalized Recommendations
and the Interfaces. The framework is realized by means of a knowledge base
which is built incrementally from user-driven behavioral characteristics and is
used to store information that corresponds to the movement sequences, along
with interfaces that provide mechanisms for accessing and updating this infor-
mation.
The Ontology contains the entities and relations describing the movement
sequences and the type of movements. The Data Alignment consists of a set
of classes which model the Ontology and specify the conditions which trigger
recommendations. The Personalized Recommendations consist of a set of meth-
ods which provide a rule based mechanism that generates interventions and
incentives for elderly mobilization according to their mobility conditions and a
knowledge base which is built incrementally from their behavioral characteris-
tics. The Interfaces consist of a set of methods and REST APIs which provide
the means to retrieve and update entities of the Ontology and expose the move-
ment sequences as they have been processed by the other components of the
agent.
This semantic interoperability agent automatically serves as a middleware
which provides knowledge gained during the execution history through the REST
APIs. If, for example, a set of movement patterns have been observed, these
sequences are stored to a centralized database and shared through all the in-
stantiations of the entire framework by the respective API. The semantic in-
teroperability is realized through the Ontology, Data Alignment and Interfaces
that are combined in order to turn domain specific data into domain agnos-
tic across different services. Figure 2 shows the functionality of the semantic
interoperability agent, how it interacts with the mobility tracking module and
how it produces recommendations for personalized exercises and incentives for
6 Karagiorgou et al.

mobilization through the Interfaces. Therefore, knowledge incrementally gained
information is derived by semantic-aware movement sequences, stored in the
database and exposed by the Interfaces.

Fig. 2. The Semantic Interoperability Agent.
3.1 Preliminaries
The input to the semantic interoperability agent comprises sparse mobility track-
ing data in the form of spatiotemporal sequences. Using linear interpolation
between consecutive samples, we derive the mobility tracks of each person. A
mobility track is modeled as a list of spatiotemporal points M = {p0 , . . . , pn }
with pi = hxi , yi , ti i and xi , yi ∈ R, ti ∈ R+ for i = 0, 1, . . . , n and t0 < t1 < t2 <
. . . < tn . These tracks are susceptible to noise, as they are affected by a measure-
ment error and a sampling error due to the variable sampling rate. The output
of the semantic interoperability agent is a set of recommendations R, modeled
as a set R = {id, ActivityT ype}, where id corresponds to a user identifier and
ActivityT ype corresponds to the category of the personalized activity proposed
each time to her. This activity may result in a walking game or a mind game
having as a sequence either a light mobilization or a mental exercise in the form
of recommendations to elderly people.
3.2 Mobility Tracking Model
The tracking of the user is realized by 3D sensors, happens throughout the day
and covers individual tracks. The individual tracks are stored and periodically
sent to the semantic interoperability agent for further analysis to gain informa-
tion of the user’s mobility by extracting whenever the person is moving within
the room, the duration, distance, type and velocity of her movement. End-users
data have been anonymized by removing their personal and identifiable infor-
mation. The following information is extracted from the stored tracks and is
taken into account in the rules definition and enforcement for the provision of
recommendations in each individual person:
– The Active Time in Room (ATR). The duration of every track is summed
up and divided through the estimated time that the user has actually been
in the room during the day. This leads to a relative amount of active time
within the observed room by also capturing the distance and duration.
– The Active Time of Day (ATD). The observed tracks are divided into times-
lots of one hour to give a statistical overview of the user’s most active/inactive
times during the day in the form of a histogram.
An Ontology-Driven Elderly People Home Mobilization Approach 7

– The Average Gait Velocity (AGV). The observed tracks are segmented and
filtered into straight parts to calculate the average gait velocity on straight
paths.
– The Average Stand up Time (AST). When the person stands up (i.e. a track
is recognized from within the scene) the time from sitting to standing is
measured.
– The Average Walking Time (AWT). When the person walks (i.e. a track is
recognized from within the scene) the duration of her walking is measured.
This mobility tracking information is analyzed to create a spatiotemporal
behavior model which shows where in the room the user stays most of the time.
This is analyzed over a long time period (i.e. 3 months) and is compared to
previous behavior recordings to determine changes in the user’s behavior.
3.3 Ontology
UniversAAL [18] is an open source platform that enables seamless interoperabil-
ity of devices, services and applications on a large scale. It provides an efficient
framework for communication in an ensemble of networking-enabled nodes by
hiding distribution and heterogeneity, acting as a broker between the communi-
cating services. It supports the integration of software components distributed
on different nodes and the collaborative communication among them. In this
work, we extend the ont.handgestures ontology which describes concepts related
with person’s gestures.
As an outcome of this stage, a formal conceptual model to define individual
elderly profiles is achieved in which data provided by the different sources partic-
ipating in the mobility tracking process can be mapped. This is achieved through
a flexible and extendable model for both data-in-motion and data-at-rest, which
can be further exploited across multiple processing components.
The main purpose of the Ontology is to model all possible movement se-
quences that are of interest to the semantic interoperability agent and thus
the entire framework. The ontology is developed in order to cover sequences of
movements and has been designed in such a way to allow easy extension, thus fa-
cilitating easy future modeling of different movement sequences, that are needed
due to the discovery of new efficient movement patterns.
All the implemented classes follow the hierarchical structure proposed by
UniversAAL [18]. Each movement sequence is modeled after the class Move-
mentSequence. An instance of MovementSequence consists of set of Movements
(e.g. walking, stand up, etc.) which are considered important to be captured by
the agent based on some criteria (e.g. the person is alone in the room). Each one
of these movements is an instance of the class Movement. A Movement can be a
Walking, a Stand up or an Active movement. In addition to MovementSequence
and Movement, the entities of Game and GameCategory are included in the on-
tology; these entities are used to encode information that is based on data stored
in the movement entities (e.g. proposed games based on the movement history
of the user). PeoplePresence class is defined for facilitating the proposition of
interesting incentives for mobilization. More specifically, it represents how many
persons are actually in the room (e.g. physiotherapists, care-givers, etc.), so that
the agent can use this information to determine when the user’s movements
8 Karagiorgou et al.

should be tracked. Figure 3 illustrates the main classes and properties of the
Ontology and the relations among the entities.

Fig. 3. Extension of universAAL Handgestures Ontology.
The OWL-DL language (an OWL – Ontology Web Language – sublanguage)
was chosen to describe the ontology model [19]. OWL is a vocabulary extension
of RDF (Resource Description Framework) [20]. It describes the structure of
a domain in terms of classes and properties and provides a set of axioms to
express assumptions or equivalences with respect to classes and properties. In
our case, OWL-DL provides the maximum expressivity that can be offered while
guaranteeing total computational capacity. The ontology was implemented by
using the Protégé-OWL v.5.2.0 ontology editor and its consistency was checked
using the Pellet reasoner [25].
3.4 Data Alignment
The main purpose of the Data Alignment is to map the mobility tracking data
provided as input to each class of the Ontology and finally to JAVA objects of
the semantic interoperability agent. The JAVA objects contain the relevant fields
and methods needed for representing the corresponding classes and some helper
methods. They are all serializable, so that they can be promptly converted to
JSON format and communicated via the endpoints of the Interfaces. Except
from the mapping of Ontology classes to JAVA objects the mapping of JAVA
objects to database tables is needed as well, in order to store all the required
information into the centralized database. A JAVA API serves as consumer which
acquires data that are generated by a scheduler and stores them in the centralized
database for further process and usage by the facets of the Interfaces. Mapping
JAVA objects to database tables is implemented via the JAVA Persistence API
(JPA). The JPA API allows to map, store, update and retrieve data from the
centralized database to JAVA objects and vice versa.
An Ontology-Driven Elderly People Home Mobilization Approach 9

3.5 Personalized Recommendations
In order to provide personalized recommendations, apart from an instance of the
elderly movement ontology, a number of rules are individually defined for each
person. These rules take into account the duration, the distance, the velocity
and the kind of elderlies activity extracted from the stored tracks (i.e. active
time, gait velocity, stand-up or walking time), as presented in Section 3.2. By
using these rules, the behavior of individuals are expressed inside the domain
and thus can be used to express individual recommendations according to their
movement conditions. In fact, rule-based systems have been extensively used
in applications that require personalized services [3]. Specifically, the SPARQL
(SPARQL Protocol and RDF Query Language) language was selected to ex-
press rules to be applied over the elderly profile instances. Although SPARQL
is a query language, it offers substantial power to filter individuals with specific
characteristics. Then, SPARQL rules are used to define personalized care tasks
according to elderly movement conditions.
The steps of the algorithm are listed in Algorithm 1. Specifically, the Semantic
Interoperability Agent takes as input Mobility Tracks M and gives as output a
set of Personalized Recommendations R. For each mobility track (Lines 3 - 18),
the algorithm looks for people’s presence in a room and a set of movements by
using specific temporal criteria (e.g. dates) (Lines 4 - 7). Then, the algorithm
records in the M ovingSequence the set of walking movement, dates and types
(Line 8). If the set of M ovingSequence is empty, the algorithm returns a set of
personalized recommendations regarding games which include walking exercises
in order to mobilize the elderly people (Line 13). On the contrary, if the set
of M ovingSequence is not empty, the algorithm returns a set of personalized
recommendations regarding mind games (Line 16).

Algorithm 1: Semantic Interoperability Agent
Input: A set of Mobility Tracks M
Output: A set of Personalized Recommendations R
1 begin
2 /*The Semantic Interoperability Agents receives Mobility Tracks as input*/
3 foreach (Mi ∈ M ) do
4 P ← LookupPeoplePresence(Mi )
5 W ← LookupWalkingMovement(Mi )
6 foreach (Pi ∈ P ) do
7 if OnAlone(Date(Pi )) ∈ Date(W ) then
8 M ovingSequence ← Concat(W, Date(Ri ), type)
9 end
10 end
11 /*Personalized Recommendations are sent as output*/
12 if IsEmpty(Lookup(M ovingSequence, P )) then
13 return(R ← WalkingGame(ActivityT ype))
14 end
15 else
16 return(R ← MindGame(ActivityT ype))
17 end
18 end
19 end

4 Experimental Evaluation
As interoperability and personalized services are one of the primary design and
evaluation goals, we need to ensure that the Semantic Interoperability Agent
10 Karagiorgou et al.

achieves abundant communication and interfaces among sensors, software and
tools. This is related with all the data needed to support the respective decision
support systems. Also, it achieves interoperability of the solution with external
services as the data are made available in a standardized format that can be
read and used by other systems.
The efficiency of the Semantic Interoperability Agent has been evaluated in
terms of technology acceptance and incentives for exergaming. We conducted a
survey interviewing end-users by both using quantitative and qualitative ques-
tions. In this section, we focus on the respective questions which concern the
Semantic Interoperability Agent and especially the technology use and accep-
tance, their attitude towards exergaming, the accuracy of personalized recom-
mendations and the efficiency of motives for physical activities. We also denote
some evaluation criteria regarding mental, functional and general health of end-
users. Mental health refers to psychological and social well-being condition of
end-users, functional health refers to the ability of end-users to do the activi-
ties they need to do and general health refers to end-users who are generally
healthy. We interviewed 201 end-users who are coming from Austria (i.e 100)
and Netherlands (i.e. 101). Their average age is approximately around 77 years
old where 33% of them are men and 67% of them are women. Also, 55% of
end-users live in single households. All the end-users gave their consent for the
participation in the survey and no personal data were collected. We used a 5-
points scale questionnaire in which we either measured and evaluated end-users
agreement (from 1 to 5, e.g. ranging from strongly disagree to strongly agree),
or satisfaction (from 1 to 5, e.g. ranging from very dissatisfied to very satisfied).

Fig. 4. Quantitative and Qualitative Results.
Figure 4 shows that the end-users exhibit wide acceptance (more than 68.5%)
of the system and believe that its use has significantly contributed in their men-
tal, functional and general health (more than 72%). However, the greater incen-
tives have been received by persons who do not exercise in their real life, so the
agent demonstrates a better potential in the elderly people of 75 years old or
more (30%). At the same time, the end-users who would not adopt exergaming
are either too fit and already prefer a more active way of life or too sick and thus
An Ontology-Driven Elderly People Home Mobilization Approach 11

an alternative option should be taken into account. Besides, the end-users who
proved not be such motivated (i.e. not interested, too boring) by exergaming
concern a small part of the interviewed of about 3%.
5 Conclusions and Future Directions
The purpose of this work is to offer an interoperability solution which is eas-
ily accessible and stores meaningful information driven by ontologies to provide
personalized recommendations to elderly people in exergaming. We extended a
data model described by a standardized ontology which is familiar to domain
experts. Having a clear model contributed to identify rules and provide person-
alized interventions.
In a nutshell, the semantic interoperability agent exposes data in a stan-
dardized format and supports interoperability with existing AAL systems and
external services. The agent serves as middleware by taking into consideration
mobility behaviors. It drives personalized recommendations to the end-users in
their private homes by increasing their self-esteem and thus their quality of life.
In the near future, we plan to experiment with the proposed agent in the
context of online methods. As it is becoming increasingly easier to gain access
to mobility data sources, such an agent could improve the process of enhancing,
combining and enriching disparate data sources and optimize the every day
life of elderly people through interactive interventions. To this effect, we are
investigating automatic methods to infer useful semantic knowledge from diverse
data sources with variable characteristics.
Acknowledgements
This work was supported by EnterTrain project which received fund under the
AAL Programme (Project No. AAL-2015-056).
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