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 Prot´eg´e2000. 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 probably performing. They are also involved in the learning process of the life habits of the TSH's occupant, and in the system maintenance.
1.1
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
agerelated cognitive disease of all. If ageing is a phenomenon that can be classified as
”deeply dynamic” by [
The Intelligent Habitat in Telehealth system [
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
autonomy has to be described with comprehensive ontologies. Second, those
descriptions/definitions have to be done by using an expressive language such as OWL
[
Adequate Representation. One of the key roles of a representation is to be
a substitute [
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
interaction 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
displaying 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 [
Modeling the system with the use of ontologies makes it possible to help
providing an adequate representation and an adequate communication.
Ontologies. Ontologies are not only used to describe the real world. They
demonstrate their full potential when being used [
The approach we use to design our architecture consists in partitioning the
domain 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
Prot´eg´e-OWL editor 3 and organized in a global structure [
The architecture is shown in figure 1. In the next sections we will present the PersonAndMedicalHistory and the Equipment ontologies.
Fig. 1. TSH Ontological Architecture and Meta-relations 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 manner 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 various 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 existing and their detection is thus very important. 2.2
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 description 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 equipment and the technical equipment.
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 [
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[
Instantiation of the Ontologies. This instantiation concerns first the Habitat ontology. For a given habitat, a configuration of equipment is defined which corresponds to an instantiation of the three main classes of the corresponding ontology. 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 others 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 MedicalHistory 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 [
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 [
The concept of intelligence always causes much controversy. According to [
Let’s explain how ontologies can help the user interface to be intelligent by a
simple 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
interface must be centered on visual effects. This can be ensured by a visual metaphor
[
In this paper, we presented an overview of an ontology-based model of the Telehealth 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 communication.
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 Prot´eg´e resource, which is supported by grant LM007885 from the United States National Library of Medicine.