=Paper=
{{Paper
|id=Vol-258/paper-42
|storemode=property
|title=Ontology-Based Management of the Telehealth Smart Home, Dedicated to Elderly in Loss of Cognitive Autonomy
|pdfUrl=https://ceur-ws.org/Vol-258/paper42.pdf
|volume=Vol-258
|dblpUrl=https://dblp.org/rec/conf/owled/LatfiLD07
}}
==Ontology-Based Management of the Telehealth Smart Home, Dedicated to Elderly in Loss of Cognitive Autonomy==
https://ceur-ws.org/Vol-258/paper42.pdf
Ontology-Based Management of the Telehealth
Smart Home, Dedicated to Elderly in Loss of
Cognitive Autonomy
Fatiha Latfi1 , Bernard Lefebvre1 and Céline Descheneaux2
1
Laboratoire GDAC, Département d’informatique
Université du Québec À Montréal (UQÀM)
Case postale 8888, succursale Centre-ville
2
Laboratoire DOMUS, Université de Sherbrooke
Québec, Canada
Abstract. Taking care of an elderly in loss of cognitive autonomy is a
challenging task. Artificial agents, such as the Telehealth Smart Home
(TSH) system can facilitate that task. However, the design of such a
system is, in itself, a challenging task as well. In this paper, we present
an ontology-based model of the TSH, which is designed in a modularized
approach and implemented in OWL using Protégé2000. Our main goal
is to take advantage of the full potential of ontologies to describe the
domain, in order to provide an effective base for the development, the
configuration and the execution of software applications. As an example,
instantiations of the ontologies of the TSH are needed to initialize the
Bayesian networks used to recognize the activity the patient is proba-
bly performing. They are also involved in the learning process of the life
habits of the TSH’s occupant, and in the system maintenance.
Key words: Ontologies, Telehealth Smart Home, OWL, Protégé2000
1 Introduction
1.1 Ageing and Alzheimer
Rapid ageing of the World population has been one of the major causes of concern
over the last decade. Fortunately, recent progresses made in the field of cognitive
ageing now allow earlier detection of various pathologies related to ageing. Early
detection, in turn, makes it possible to better prepare for the following stages of
the diseases and will, one day, perhaps help in finding a cure for some and/or all
of them. However, even if these researches offer partial solutions and can prove
to be helpful in alleviating some of the pain and distress caused by such diseases,
pathologies related to the ageing process still remain a major preoccupation in
today’s society and Alzheimer’s disease is, by far, one of the most worrisome age-
related cognitive disease of all. If ageing is a phenomenon that can be classified as
”deeply dynamic” by [14], requiring continual adaptations between the elderly
person and his relatives, in the case of Alzheimer’s, the dynamic aspect and
continual adaptation are even more crucial. The life of a person suffering from
Alzheimer’s is literally governed by the disease which keeps on progressing as
time goes on. As the disease progresses, the subject becomes more vulnerable
and finds it harder and harder to adapt to new situations, even very simple ones.
1.2 Telehealth Smart Home
The Intelligent Habitat in Telehealth system [12], also known as Telehealth
Smart Home (TSH) can provide solutions for this adaptability. The main goal
of the TSH is to provide assistance to the patient in order to prevent any poten-
tially dangerous situation that could endanger his life.To achieve this objective,
the TSH system relies on several physical and software components, supervised
by various individuals such as a nurse, a relative, the family doctor, the system
manager, etc. In order to be effective, such a system should have an adequate
representation of basic knowledge, a good inference module and finally, a good
HMI (Human Machine Interface) for an effective communication between the
system and its users.
1.3 TSH Challenges
The challenges related to the TSH are twofold. First, an adequate model of
what a smart home designed to care for someone with loss of cognitive auton-
omy has to be described with comprehensive ontologies. Second, those descrip-
tions/definitions have to be done by using an expressive language such as OWL
[9].
Adequate Representation. One of the key roles of a representation is to be
a substitute [3] for the real object we want to handle. A good representation
makes it also possible to perform various reasoning algorithms in order to infer
new knowledge in order to reflect as accurately as possible the future evolutions
of the world under study. However, to ensure the correctness of such knowledge,
we need a basic model that is as much representative of reality as possible.
Producing such an adequate model has always proven to be a challenge for
anyone interested in modeling part of the real world. The fact that we have
to find ways to represent elements and concepts related to individuals suffering
from a degenerative disease, such as Alzheimer’s, that keeps on evolving for the
worst makes that task even more daunting.
Adequate Communication. In addition to the need for adequacy related to
the representation of the field, the intelligence of the system lies also in the in-
teraction between the various users and this representation. This interaction is
a major feature of a TSH system. It needs to provide an effective way of display-
ing the information, which must allow the user to correctly perceive the message
conveyed by the interface. This kind of visualization can be found in domains
such as Human-Machine interfaces, Data-Mining, image processing, graphics. It
is thus an inter and multi disciplinary field of research [2], which is of the most
interest.
Modeling the system with the use of ontologies makes it possible to help provid-
ing an adequate representation and an adequate communication.
Ontologies. Ontologies are not only used to describe the real world. They demon-
strate their full potential when being used [9] as an effective support for the
development as well as the configuration of software applications, since they
allow an optimization of the quality cost ratio [13]. Since the occupant of the
habitat is an individual, usually elderly, in loss of cognitive autonomy, ontologies
must also adequately support the human machine communication[9] whose role
is to enable the interfaces to adapt and adjust themselves in accordance with
the characteristics of the patient. Our first goal is to build an ontological model
that is as comprehensive as possible. The ontologies taking part in the model
are presented in the next section.
2 Ontological Architecture of TSH
The approach we use to design our architecture consists in partitioning the do-
main into sub-domains in order to take advantage of the Divide and Conquer
principle. The different ontologies are defined individually in a first step with the
Protégé-OWL editor 3 and organized in a global structure [7] using the OWL
imports statement. A set of properties (called meta-relations and summarized
in figure 1) is defined to describe the relationships between the ontologies.
Seven ontologies are part of the model. The Habitat ontology describes the struc-
ture of the accommodation (the housing facility in which the patient lives: rooms,
doors, windows, etc). The PersonAndMedicalHistory ontology gives a descrip-
tion of the person who needs care and his medical history. It is also dedicated
to the person whose role is to ensure this care and what are its various duties.
The Equipment ontology defines the equipment that can equip the Smart House
and be involved in the system. The Software applications ontology describes the
modules of the system in such a way that they can be reused or understood.
The Task ontology details the tasks the patient, the actor and the system are
supposed to achieve. The Behavior Ontology addresses 2 important aspects in
the context of remote monitoring, the life habits and critical physiological pa-
rameters. Finally, the Decision ontology is closely related to the behavior when
a critical situation or a change of habits is detected. A mapping system like the
medical model Symptoms-Diagnosis, can be adapted to the problem of decision
making, when a particular number of facts (especially a critical behavior), corre-
sponds to a decision to be taken. This decision can quite simply consist in doing
nothing or be concretized in an action, whose type depends on the gravity of the
situation [8].
The architecture is shown in figure 1. In the next sections we will present the
PersonAndMedicalHistory and the Equipment ontologies.
3
http://protege.stanford.edu
Fig. 1. TSH Ontological Architecture and Meta-relations
2.1 The PersonAndMedicalHistory Ontology
In the framework of the TSH, the ontology of the person is composed of two main
parts: the first one relates to the person and the second one relates to the medical
history of the patient. The part in charge of describing the person is divided into
two sub-parts. The first sub-part describes the profile of the person living in
the TSH (the Patient), whereas the second part describes the various persons
(called Actors) in charge of ensuring the correct operation of this habitat. An
actor can be an individual (human being) or a legal entity. Three types of actor
are defined in this ontology.
1. A medical actor (Medical-staff) who describes all the people who are able
and likely to intervene in one way or another in order to insure the physical
and/or mental health of the patient;
2. The actor described by Habitat-staff is involved in the management of the
habitat;
3. The third type of actor describes everyone who can interact in a social man-
ner with the patient living in the TSH. They can be close relatives or simple
volunteers.
The second class entitled Medical-history describes the medical history of the
patient as well as the various risk factors which are either related to Alzheimer’s
disease or to other age-related diseases. These diseases can be temporary or
chronicle, mental or physical. Four great classes are located in the left part of
the figure. The Deficiency class describes all deficiencies (Intellectual-deficiency,
Physical-deficiency) that can afflict the patient. The Disease class lists the var-
ious pathologies (past and present) exhibited by the patient. This will make it
possible to make a follow-up of its health and to better know and understand
some of its actions or abnormal behaviors. The class Prescript-medication allows
the system to instantiate the hygiene and medication rules to be applied to the
patient. Finally, the Risk-factors class gives a description of diseases considered
as possible triggering factors for Alzheimer’s disease. In the case of the TSH, the
patient is already a person suffering from Alzheimer’s and thus these diseases,
such as Diabetes or Arterial-hypertension can be considered as potentially ex-
isting and their detection is thus very important.
2.2 The Equipment Ontology
This section gives a short description of the Equipment Ontology 4 which is an
important part of the ontological architecture of the TSH. It contains the de-
scription of all pieces of equipment that can be found in the habitat in order to
ensure the patient safety. This ontology, diagrammed in figure 3, is defined in
three main parts, which are the furniture equipment, the household equip-
ment and the technical equipment.
4
This ontology is detailed in [7].
Fig. 2. PersonAndMedicalHistory Ontology: Screenshot of Main Classes and Some
Object Properties
3 How do Ontologies Make the System be Intelligent?
Taking care of someone in loss of cognitive autonomy is a daunting task, which
requires huge efforts and a lot of flexibility from the caregiver. The design of the
knowledge part is in consequence a challenge. To be efficient, the system must
be able to answer questions such as: is the patient safe or is he at risk? The
knowledge base and how it is represented play an essential role in the answer.
Ontologies, as rigorous descriptive models [13], are appropriate for this task.
3.1 The Use of the Ontologies
Ontologies are used in order to initialize the system. At first, the ontologies have
to be instantiated and then, these instances are used to initialize the various
Bayesian Networks that are part of the activity recognition and learning module
in charge of recognizing activities being performed by the TSH occupant and in
the learning his life habits[4].
Instantiation of the Ontologies. This instantiation concerns first the Habitat
ontology. For a given habitat, a configuration of equipment is defined which cor-
responds to an instantiation of the three main classes of the corresponding ontol-
ogy. It is then possible to know what pieces of furniture are present in the habitat,
which pieces of equipment are located in each part of the house/apartment and
especially which equipment are used to forward data to the system. The basic
acquisition of data is realized by sensors (elements of the Technical-equipment
class). Some of these sensors are dedicated to the detection of activities but oth-
ers concern the monitoring of biological parameters related to the patient. At
a later stage and after being processed by learning components of the system,
these data will permit the instantiation of health and behavior parameters by
the means of the Behavior Ontology as well as the two classes Patient and Med-
icalHistory of the PersonAndMedicalHistory Ontology. The instantiation of the
Actor class also makes it possible to specify which people will be in charge of
the Habitat and the caring of the patient. Finally, the instantiation stage also
relates to the Task ontology as well as the Decision ontology.
This stage is followed by the initialization of Bayesian Networks.
Initialization of the Bayesian Networks and the Learning Steps. In
this second stage, Bayesian Networks are used for recognizing which activity
is most likely to be performed by the patient at a given time and in a given
place [4]. This recognition is the initial step in the process that will enable us to
learn the patient’s life habits, follow the evolution of his cognitive disorder, and
detect/prevent possible emergency situations. Several factors (such as the time
of day, the location of the patient, the patient’s level of activity, the previous
activity that was performed, etc.) have to be taken into account in order to de-
duce as accurately as possible what is the most likely activity being performed.
The structure of the various Bayesian networks also takes into account those
factors. There is a hierarchy of Bayesian networks which is closely related to the
hierarchy of activities. At the lower level are specialized networks devoted to
the recognition of simple activities such as the ones which can take place in the
bathroom in front of the wash basin. The structure of these networks is defined
using the instances of the corresponding ontologies.
These two stages are essential if we want the system to be able to monitor the
patient and learn what his life habits are. This learning process is also achieved
by upper level Bayesian networks in a generalization process which feeds and
updates the Behavior Ontology and consequently the decision Ontology [8].
The two stages are illustrated by figure 3.
3.2 How can Ontologies help the User Interface to be Intelligent?
The concept of intelligence always causes much controversy. According to [6], the
intelligent species is that which survives by constantly adapting itself, contribut-
ing thus to its own perpetuity. Don Norman said in [10]: ”Do we need intelligent
interfaces? I don’t think so: The intelligence should be inside, internal to the
system.”
The problem of developing ”intelligent” user interface has become (been) a wor-
rying topic for the research community in recent times. Furtado [5] and Puerta
[11] have already presented some ontology-based methodologies to produce or
develop good user interfaces.
Let’s explain how ontologies can help the user interface to be intelligent by a sim-
ple example. In the PersonAndMedicalHistory Ontology, we have a class called
Deficiency. When instantiated, this class gives the list of all deficiencies that
affect the patient. If the Visual-deficiency instance has a value, it means that
the patient’s sight can be partially or totally affected. Even if the patient sight’s
loss is very minimal, the user interface must adapt itself in order to compensate.
Conversely, if the patient suffers from an Auditive-deficiency , the user inter-
face must be centered on visual effects. This can be ensured by a visual metaphor
[1]. The user interface is defined and configured using the SoftwareApplications
Ontology and the Patient profile described in the PersonAndMedicalHistory
which is in turn updated by the others ontologies (especially the Behavior On-
tology) and the learning module.
4 Conclusion and Future Work
In this paper, we presented an overview of an ontology-based model of the Tele-
health Smart Home (TSH), dedicated to elderly in loss of cognitive autonomy.
We use Ontologies for their full potential to support effectively the development
as well as the configuration of software applications. We also use Ontologies for
their potential to support adequately human-human and human-machine com-
munication.
The development of such a system needs a strong process of validation, which
Fig. 3. Instantiation Ontologies and B.N. Initialization Example
will be achieved in collaboration with the DOMUS laboratory. 5 team and the
AFIRM 6 team.
Acknowledgment. This work was conducted using the Protégé resource, which
is supported by grant LM007885 from the United States National Library of
Medicine.
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5
http://www.domus.usherbrooke.ca/, Sherbrooke, Quebce, Canada
6
http://www-timc.imag.fr, Grenoble, France