# An Adaptive System for Home Monitoring Using a Multiagent Classification of Patterns

Authors: Ali Rammal; Sylvie Trouilhet; Nicolas Singer; Jean-Marie Pécatte
Journal: International Journal of Telemedicine and Applications
 (2008)

---

## Abstract

This research takes place in theS(MA)2D project which proposes software architecture to monitor elderly people in their own homes. We want to build patterns dynamically from data about activity, movements, and physiological information of the monitored people. To achieve that, we propose a multiagent method of classification: every agent has a simple know-how of classification. Data generated at this local level are communicated and adjusted between agents to obtain a set of patterns. The patterns are used at a personal level, for example to raise an alert, but also to evaluate global risks (epidemic, heat wave). These data are dynamic; the system has to maintain the built patterns and has to create new patterns. So, the system is adaptive and can be spread on a large scale.


---

## Body

## 1. Introduction

In Europe,
many countries will be confronted with aging populations in the coming decades.
For example, it is estimated that in 2020, 28% of the French population will be
over 60 [1]. A great way to resolve partially this difficulty is to encourage old
people to be cared for in their own homes. This strategy presents two main advantages:(i)
the elderly want to
stay at home as long as possible; they keep the privacy they do not want to
lose,(ii)
it is less expensive
than a place in a collective accommodation.
Our project takes place in this context. It aims to help professional home-care
teams in their job by thinking up innovative software technologies, more
precisely(i)
by increasing the
number of old people looked after in their homes with an adaptive and
nonintrusive remote assistance,(ii)
by reassuring family
circle. The system ensures that the monitored person is secure; so, people
around him feel at ease, and(iii)
by contributing
towards its democratization. The use of simple elements (e.g., basic sensors)
minimizes the initial cost of a monitoring system.
We made a study of systems having
the same aim—the following
section describes three well-known and relevant European systems in the home-care
domain. These systems focus on individuals (they are user-centred): a system
surveys only one person; thus, there is a duplication for each individual looked
after. None of these systems collects individual monitoring for merging global
behaviour patterns. Nevertheless, patterns of monitored people could be used to
estimate the status of someone in relation to their community or to integrate new
comings.We propose a multiagent system that is able to generalize, which builds a classification of monitored
people. An agent watches over one or more indicators of a group of people. An
indicator is data about daily activities, positions, and physiological
information. In a first step, the agent applies a local-classification method
and obtains an incomplete patterns’ partition. Next, the partial partitions are
compared with each other in order to build a complete classification.We conceived an open system: new people
or/and new indicators bring in new agents or/and new patterns.In Section3, we present the architecture
of the system and how it runs.The system manages a set of patterns
of monitored people. This dynamically updated classification has the following three
main uses:(i)
to find certain
similarities with the existing tools for evaluating the dependence—dependence grid
of the social services, for example,(ii)
to get global
statistical data about old people looked after in their own homes, and(iii)
to generate specialized alarms depending on the detected event. Once
the classification is set up and people status is known, decisions can be taken
to personalize the process
of monitoring someone—activated
sensors, generated alarms, and danger zone.
These aspects are discussed in the
last section.


## 2. A Survey of Three Home-Monitoring Systems

The use of computers to help people stay
at home has been the subject of many research projects. Some of them are quite
ambitious and regroup many partners. In this section, we describe a selection
of three projects designed to assist people in their living environment. We
expect to give the reader an overview of the advancement in this area and also the
bases our project is laying on.The selection shows different
hardware and software problematics (communication networks, system
interoperability, data analysis, emergency handling, and alerts filtering).
These problems must be solved to achieve efficient monitoring. We begin by
explaining the main objectives of each project. Then, we propose a table that
summarizes their most relevant features.


### 2.1. The PROSAFE Project

The PROSAFE project [2, 3] attempts to automatically identify
the daily activities of the monitored person. The processing of collected data
is carried out on doctor’s request with an adapted interface.The final operational objective is to detect any abnormal behaviour such
as a fall, a runaway, or an accident. The research objective is to gather
characteristic data about the nightly or daily activities of the patient. More
precisely, the system can(i)
describe events that
took place during monitoring time—time spent in bed
or in the toilets, entering or leaving the bedroom, moving inside the home,(ii)
build a database with
all abnormal situations detected, and(iii)
build statistics about
past activities.
At the hardware level, the system configuration
uses a ground network (a mobile version is also usable). Currently acquisition
and data processing are local, and monitoring is both local and distant.The PROSAFE system is primary used
by the medical staff in hospitals. The interface for nurses allows them visualizing
the patient state and abnormal situations (alerts and alarms) in the bedroom.
As soon as an alarm is raised, a beeper calls a nurse. In the same time,
doctors can access a database updated in real time with statistical data about
the patient behaviour.Experiments have been made to gather
data about the daily activities of patients in hospitals, especially during the
night. Experimental sites have been set up in two hospitals and three more are
being installed in elderly people residences.To conclude, let us say that one of
the main features of this project is to be based on real-time analysis of data.


### 2.2. The AILISA Project

The AILISA project [4, 5] (Intelligent Apartments for effective longevity) is an experimental platform to evaluate remote care and assistive
technologies in gerontology. This ambitious project regroups specialists of
smart homes, networks and computing, electronics, and signal processing. More precisely, the project sets up
a monitoring platform composed of(i)
a home equipped with a set of
sensors and health devices (presence detectors, wrist arterial pressure sensors,
and pulse oximeter),(ii)
a smart shirt developed by
the French company TAM with several sensors and electronics embedded in the
textile to detect falls,(iii)
a smart assistant robot for ambulation
to secure the displacements and assist the person during transfers, and(iv)
a software system to gather
and analyze the sensors output.
The project aims to set up an interdisciplinary platform for the
evaluation of the technologies at the three following levels: technical,
medical, and ethical.


### 2.3. The e-Vital Project

The e-Vital project [6] (cost-effective health services for interactive
continuous monitoring of vital signs parameters) is a modular and ambulatory telemedicine
platform. Its objective is to increase patient’s feeling of safety concerning their
health. Patients and caregivers feed a central database with some measuring
equipment. The developed device allows staff to take measurements and data
collected to be sent to the resident doctor. This doctor can remotely diagnose
whether there is a problem that needs them to visit or that requires the
resident to receive hospital treatment.By way of a personal digital assistant (PDA), the e-Vital server connects
monitoring devices produced by several manufacturers. The server is a
multiagent system where each agent focuses on a specific task related to the
medical stored data. For example, an alert manager is specialized for the
raising of alert messages, a profile manager for access management and a
schedule manager for healthcare scheduling.The e-Vital project is mainly hardware and tries to solve the
interoperability problems between non compatible devices. It focuses on communication protocols and on
the central database format.These objectives (care protocol, devices interoperability) are different
from ours but the approach is similar: e-Vital is an open system with several
interconnected modules, one of which being a multiagent system. The difference
resides on the application level: when our system is a group-centred system,
e-Vital is a patient-centred system (it does not use the patient’s record to
develop generic profiles).


### 2.4. Results of the Survey

We presented three systems which are
able to monitor the “elderly” in their own homes. Table1 summarizes some features
that we found relevant to compare.Table 1
Overview of the three projects.
ProjectCriteriaPROSAFEAILISAe-VitalSmart home equipmentYesYesNoEquipment is installed in hospitals and residences of elderly
peopleHealth smart homesBody wear equipmentYesYesNoAccelerometer (or GPS)Smart shirts with fall sensorsMedical equipmentYesYesYesDigital entries acquisition moduleWrist arterial pressure
Pulse oximeterAppropriate monitoring devicesDetected emergencies and supervised risksAccidents Falls
EscapesSome medical risks
FallsScheduled care
Vital signs defectionTargetElderly or handicapped people Patient with Alzheimer diseaseElderly people Handicapped peoplePeople with chronic diseasesProject range (home, living environment)At home and in hospitalAt homeLiving environment (that is at home but also in mobile situations)Risk-detection methodSensors and statistical methodsMainly hardwareData management and interpretation made by a multiagent systemScale (how many people are concerned)The system focuses
on one patient but the profiling can be used in a more large scale systemThe System is providing an individual helpLinks to personal medical dataYesNoPatient’s electronic health record are stored in a hospital database
(but this database is only used by the project)Medical validationTested in three hospitals (three other sites are planned)Planned in three hospitalsTests take place in four European pilot sitesEthical and psychological aspectsTechnical mediation between health caregivers and patientsTechnical mediation between
caregivers and patients
Psychiatric aspectsNot mentionedOperational or experimentalExperimentalExperimentalExperimentalAll three projects seek to gather
information about people by the way of hardware and software solutions. They
differ in the type of collected data, in the way they use it, and in their objectives.
From the simple gathering of health information for caregivers, to the complete
profiling of people, resources are quite different.In all cases, the patient is an
isolated person, installed in the centre of these systems; systems which have mainly
a local vision of situations.These works have inspired more
recent projects; these projects are in progress so their results can not be
analysed yet. This is the case of the GERHOME [7] project, led by two French
research centers. This project intends to create a smart home for weak people.
This objective can also be found in the European project SOPRANO [8]. Let us also talk of the European OLDES [9] project, which tackles the problem of the elderly
people access to the new technologies. It tries to create low-cost hardware
with very easy-to-use interfaces.Our research is based on the
progress and technologies developed in all these projects, especially those
that gather information about the monitored person, whatever granularity this
information can have. For example, the information can be the cardiac output,
or something of higher level like behaviour information. This data is the raw
material of our system and is used to generate several categories of people. Then
these categories are used to make global assumptions about people belonging to
the same class.So our problematic is to collect the
results of a large number of individual monitoring and to draw several
categories. This classification provides several reusable classes of
people.For that, we deploy a classification framework, usable in a large-scale
configuration, and based on multiagent technology. The next section describes
this architecture.


## 2.1. The PROSAFE Project

The PROSAFE project [2, 3] attempts to automatically identify
the daily activities of the monitored person. The processing of collected data
is carried out on doctor’s request with an adapted interface.The final operational objective is to detect any abnormal behaviour such
as a fall, a runaway, or an accident. The research objective is to gather
characteristic data about the nightly or daily activities of the patient. More
precisely, the system can(i)
describe events that
took place during monitoring time—time spent in bed
or in the toilets, entering or leaving the bedroom, moving inside the home,(ii)
build a database with
all abnormal situations detected, and(iii)
build statistics about
past activities.
At the hardware level, the system configuration
uses a ground network (a mobile version is also usable). Currently acquisition
and data processing are local, and monitoring is both local and distant.The PROSAFE system is primary used
by the medical staff in hospitals. The interface for nurses allows them visualizing
the patient state and abnormal situations (alerts and alarms) in the bedroom.
As soon as an alarm is raised, a beeper calls a nurse. In the same time,
doctors can access a database updated in real time with statistical data about
the patient behaviour.Experiments have been made to gather
data about the daily activities of patients in hospitals, especially during the
night. Experimental sites have been set up in two hospitals and three more are
being installed in elderly people residences.To conclude, let us say that one of
the main features of this project is to be based on real-time analysis of data.


## 2.2. The AILISA Project

The AILISA project [4, 5] (Intelligent Apartments for effective longevity) is an experimental platform to evaluate remote care and assistive
technologies in gerontology. This ambitious project regroups specialists of
smart homes, networks and computing, electronics, and signal processing. More precisely, the project sets up
a monitoring platform composed of(i)
a home equipped with a set of
sensors and health devices (presence detectors, wrist arterial pressure sensors,
and pulse oximeter),(ii)
a smart shirt developed by
the French company TAM with several sensors and electronics embedded in the
textile to detect falls,(iii)
a smart assistant robot for ambulation
to secure the displacements and assist the person during transfers, and(iv)
a software system to gather
and analyze the sensors output.
The project aims to set up an interdisciplinary platform for the
evaluation of the technologies at the three following levels: technical,
medical, and ethical.


## 2.3. The e-Vital Project

The e-Vital project [6] (cost-effective health services for interactive
continuous monitoring of vital signs parameters) is a modular and ambulatory telemedicine
platform. Its objective is to increase patient’s feeling of safety concerning their
health. Patients and caregivers feed a central database with some measuring
equipment. The developed device allows staff to take measurements and data
collected to be sent to the resident doctor. This doctor can remotely diagnose
whether there is a problem that needs them to visit or that requires the
resident to receive hospital treatment.By way of a personal digital assistant (PDA), the e-Vital server connects
monitoring devices produced by several manufacturers. The server is a
multiagent system where each agent focuses on a specific task related to the
medical stored data. For example, an alert manager is specialized for the
raising of alert messages, a profile manager for access management and a
schedule manager for healthcare scheduling.The e-Vital project is mainly hardware and tries to solve the
interoperability problems between non compatible devices. It focuses on communication protocols and on
the central database format.These objectives (care protocol, devices interoperability) are different
from ours but the approach is similar: e-Vital is an open system with several
interconnected modules, one of which being a multiagent system. The difference
resides on the application level: when our system is a group-centred system,
e-Vital is a patient-centred system (it does not use the patient’s record to
develop generic profiles).


## 2.4. Results of the Survey

We presented three systems which are
able to monitor the “elderly” in their own homes. Table1 summarizes some features
that we found relevant to compare.Table 1
Overview of the three projects.
ProjectCriteriaPROSAFEAILISAe-VitalSmart home equipmentYesYesNoEquipment is installed in hospitals and residences of elderly
peopleHealth smart homesBody wear equipmentYesYesNoAccelerometer (or GPS)Smart shirts with fall sensorsMedical equipmentYesYesYesDigital entries acquisition moduleWrist arterial pressure
Pulse oximeterAppropriate monitoring devicesDetected emergencies and supervised risksAccidents Falls
EscapesSome medical risks
FallsScheduled care
Vital signs defectionTargetElderly or handicapped people Patient with Alzheimer diseaseElderly people Handicapped peoplePeople with chronic diseasesProject range (home, living environment)At home and in hospitalAt homeLiving environment (that is at home but also in mobile situations)Risk-detection methodSensors and statistical methodsMainly hardwareData management and interpretation made by a multiagent systemScale (how many people are concerned)The system focuses
on one patient but the profiling can be used in a more large scale systemThe System is providing an individual helpLinks to personal medical dataYesNoPatient’s electronic health record are stored in a hospital database
(but this database is only used by the project)Medical validationTested in three hospitals (three other sites are planned)Planned in three hospitalsTests take place in four European pilot sitesEthical and psychological aspectsTechnical mediation between health caregivers and patientsTechnical mediation between
caregivers and patients
Psychiatric aspectsNot mentionedOperational or experimentalExperimentalExperimentalExperimentalAll three projects seek to gather
information about people by the way of hardware and software solutions. They
differ in the type of collected data, in the way they use it, and in their objectives.
From the simple gathering of health information for caregivers, to the complete
profiling of people, resources are quite different.In all cases, the patient is an
isolated person, installed in the centre of these systems; systems which have mainly
a local vision of situations.These works have inspired more
recent projects; these projects are in progress so their results can not be
analysed yet. This is the case of the GERHOME [7] project, led by two French
research centers. This project intends to create a smart home for weak people.
This objective can also be found in the European project SOPRANO [8]. Let us also talk of the European OLDES [9] project, which tackles the problem of the elderly
people access to the new technologies. It tries to create low-cost hardware
with very easy-to-use interfaces.Our research is based on the
progress and technologies developed in all these projects, especially those
that gather information about the monitored person, whatever granularity this
information can have. For example, the information can be the cardiac output,
or something of higher level like behaviour information. This data is the raw
material of our system and is used to generate several categories of people. Then
these categories are used to make global assumptions about people belonging to
the same class.So our problematic is to collect the
results of a large number of individual monitoring and to draw several
categories. This classification provides several reusable classes of
people.For that, we deploy a classification framework, usable in a large-scale
configuration, and based on multiagent technology. The next section describes
this architecture.


## 3.S(MA)2D System

We propose a system able to carry out a generalization of profiles’ patterns
and to propose a classification of monitored people.S(MA)2D (multiagent
system for keeping elderly people in their own homes) is a multiagent framework
in which agents use a restricted cooperation protocol to collectively perform
classifications.


### 3.1. Multiagent and Health

We chose a multiagent approach because these systems proved their
adequacy in many health problems [10]. In this field, medical knowledge to solve
a problem can be distributed in various places. For example, to establish the
medical file of a patient, it is necessary to have analyzes and tests coming
from several hospitals. Agents work in various places, each agent managing a part
of the knowledge.Multiagent architecture is particularly adequate if the problem-solving implies
the coordination of various specialized people (e.g., units of a hospital must collaborate
to establish patient scheduling). Then, the agents have cooperative skills to communicate
and to build together a solution progressively.Moreover, many medical problems are complex and often standard solutions
are not easy to find. A multiagent problem-solving is based on decomposition in
subproblems. Let us take for example organ transplants [11]: when a new organ is
available, the more appropriate recipient must be found very quickly. It can be
located in a very far medical center. Moreover, each hospital keeps the data of
its patients; they are in the waiting list depending on the type of organ. It
would be difficult to conceive and apply a complex centralized system to solve
this coordination problem (e.g., a standard decision aid expert system).Multiagent technology also proved its reliability in medical information
retrieval. A great quantity of medical knowledge is available on the Internet,
and it is necessary to access to the most suitable information. The agents can
be employed to play the mediators between doctors and patients, or between medical
resources. These agents seek information issued from various sources, analyze
selected data, and choose useful information according to the profiles of the
consultants.To conclude, the agents’ autonomy is an adequate paradigm to deploy systems,
in which each component models the behaviour of an independent entity; this
entity has its own knowledge, skills, and individual goals.We recalled the general interest of multiagent systems in the health
field. Now we are going to present the expected functionalities of our system.


### 3.2. Architecture and Functioning

The system is based on a bunch of sensors carried by monitored people or
installed in their homes. Those sensors are, for example, presence and movement
sensors or medical measuring apparatus. The data coming from sensors are
transformed into indicators. Some indicators can also come from human
information: notes of a nurse or patient’s answers to a questionnaire.These indicators will be used by the system to generate its
classification. Their abstraction from data requires a software layer. The set
of sensors and this software layer are out of the scope of our work. It is the result
of projects described in Section2.It is important to note that the functioning of the system is
independent of the type and the number of indicators.Indicators are collected by classification agents constituting the
system. Because the system is strongly distributed, indicators of two people
will not be inevitably collected by the same agent. There can also be some overlaps,
if the same information is collected by several agents.Thus, classification agentsAj have indicators ik concerning several individuals Pi (Figure 1).Figure 1
S(MA)2D architecture.With its indicators, each agent calculates a local, partial
classification. This classification does not take into account all the
indicators and is related to a reduced sample of the population.Since the data inputs are numerical values, any statistical
classification method is applicable.To refine this classification, the agents communicate each other. They
congregate in acquaintances network according to the similarity of the produced
partitions. More precisely, each agent seeks the other agents which made a
classification close to its own. To compute the classes of the collaboratively
determined partition, we designed a restricted cooperation protocol in three
steps: call for participation/acquaintance’s group constitution/multiagent
classification.Section3.3 gives a detailed example of this protocol.There may be several groups of agents. They constitute parallel classifications:
they are views of the same monitored people but according to various criteria
(Figure2).Figure 2
Multiagent classification.


### 3.3. Example

It is assumed that the behaviour indicators have numerical values. These
values can be normalized by several methods as(i) normalization between[0⋯1](1)I˜j=Ij−IjminIjmax−Ijmin,
where Ijmin (resp., Ijmax) is the minimum value (resp., maximum value) of indicator number j, and(ii) linear normalization(2)I˜j=Ij-I¯jσj, where I¯j is
the average values of indicator number j for a given agent, and σj is the standard deviation of the indicator
number j for a given agent
(3)σj=Vj,Vj=1n∑k=1n(Ijk−I¯j)2,
where Vj is the variance of indicator
number j for a given agent; n is the number of people monitored by an agent;Ijk is the indicator number j of the person number k.Thereafter we apply our proposal on an example of 3 agents, 3 behaviour indicators,
and 11 people. SupposeI1 is the body temperature, I2 is
the number of getting up/sleeping in one night, and I3 is the number
of entries to the toilets each day.The following table shows the distribution of people (Pi) and indicators (Ij) on
the agents (Ak)
of the system.Table2 shows that agent A1 monitors 2 indicators I1 and I2 on people P1, P2, P3, P4,
P5, and P11. Agent A2 monitors 2 indicators I2 and I3 on people P4, P5, P6, P7,
P8, and P11. A3 monitors 2 indicators I1 and I3 on people P3, P6, P9, P10,
and P11.Table 2
Distribution people/indicators.
People∖IndicatorsI1I2I3P1A1A1—P2A1A1—P3A1, A3̲A1A3̲P4A1A1, A2A2P5A1A1, A2A2P6A3̲A2A2, A3̲P7—A2A2P8—A2A2P9A3̲—A3̲P10A3̲—A3̲P11A1, A3̲A1, A2A2, A3̲This table also shows that people do not have the same indicators (it will
often happen in real situations). For example,P1 has only two
indicators because for this person it is not necessary to test the number of
entries to the toilets. The aim is that each person has the indicators suited
to his case.We assume that the sensors send data to the system on a daily basis. In
reality there are indicators that are more important than others, for example,
body temperature is more important than the outside temperature, so we give a
weight for each indicator; this weight will help us later to form the groups of
agents and to calculate the distance between classes. The most important indicator
will be the one with the largest weight.In our case we give toI1 (body temperature) the weight 3, I2 the weight 2, and I3 the weight 1 (which is the default value).By applying a local classification method (e.g., ISODATA [12]) each agent
builds its partition. Each class is characterized by a midvector calculated by
ISODATA.Preliminary step:construction of partitions (Figure 3).Figure 3
Local classification.The first step is thecall
for participation. It aims to form groups of agents to generalize
the classification. The agents of the system communicate with each other
through the facilitator agent. The process to constitute agents groups for each
agent Ai is as follows:(i)
Ai sends
its indicators to other agents;(ii)
Ai receives
the indicators from other agents;(iii)
for each other agent,Ai calculates the sum of the weights of common indicators (calling S1),
and the sum of the weights of  noncommon
indicators (calling S2);(iv)
ifS1≻S2Ai responds to the agent
concerned;(v)
the agents of a group are
agents who have exchanged messages between them.
In our case, A1 sends I1 and I2. A1 also receives from A2 and A3 their indicators. We find
(4)A1∩A2=I2,A1∩A3=I1,A2∩A3=I3.
And as the weight of I1 is greater than I2 and I3,
A1 chooses A3 to form a group. The result is two groups
of agents. The first group is formed by A1 and A3, and
the second is formed by A2.This second step isthe
acquaintance's group constitution.The third (and last) step is togeneralize the classification. The agents of a group measure
the distances between their classes using the weighted Euclidean distance:
(5)dw(c,c′)=(∑1≤j≤nwj⋅(dj(c,c′))2)1/2.
In which c and c′ are two classes, wj is the weight of the indicator
number j, n is the number of common indicators between the two classes, and dj(c,c′) is the distance between the two midvectors of the two classes according
to the indicator number j.We can apply this formula on the actual values or normalized values of
indicators. In this example we use the actual values. The agentA1 seeks
to each of its classes, the closest classes of its group among other agents.Calculation of distances between classes:(6)dw(CA11,CA31)=3;dw(CA11,CA32)=0;dw(CA12,CA31)=0;dw(CA12,CA32)=3;dw(CA13,CA31)=3;dw(CA13,CA32)=23.
After the calculation of distances, we find that the class CA11 should be merged with CA32, and that CA12 should be merged with CA31. By contrast, CA13 should not be merged with CA31 because there is another class from A1 nearest to CA31.The new classes thus obtained (Figure4) have new midvectors. These
vectors are the averages of the indicators values of people belonging to the
same class.Figure 4
Result of the classification.A person may belong to several classes according to the indicators used.
For example,P3 is classified by A1 and A3 in
a class by itself according to I1 and I2, and it is
classified with P2, P5, P6, and P10 according to I1, I2, and I3.As prospects, we intend to set a minimum threshold for the distance
between classes. This threshold will be based on indicators and their weights. If
the distance between two classes is greater than this threshold, they will not
merge, even if they are close in the sense described above. It will be a more
true-to-life approach.


### 3.4. Relevance of the Multiagent Architecture

This classification is actually multiagent because the classification
result is not the work of a simple agent, as it is the case in other multiagent
systems (choice of the most skilled [13]). It is really a collective work.This multiagent classification answers to the problem of the search of
patterns in an open and dynamic environment. Classical methods do not make it
possible to increase the system scale: for example, when the number of entries changes
(with the addition of a new indicator), all calculation and generation of
classes must be made again.Thus, our method satisfies the requirements of our application because
it does not depend on the type of the indicators and does not require preliminary
categories.The management of the monitored people continues throughout the
functioning of the system, as the agents collect more indicators values. Thus
patterns evolve and the class of people can change.Also an indicator can be deactivated: it corresponds to a data for which it is not essential to
monitor this type of people.


## 3.1. Multiagent and Health

We chose a multiagent approach because these systems proved their
adequacy in many health problems [10]. In this field, medical knowledge to solve
a problem can be distributed in various places. For example, to establish the
medical file of a patient, it is necessary to have analyzes and tests coming
from several hospitals. Agents work in various places, each agent managing a part
of the knowledge.Multiagent architecture is particularly adequate if the problem-solving implies
the coordination of various specialized people (e.g., units of a hospital must collaborate
to establish patient scheduling). Then, the agents have cooperative skills to communicate
and to build together a solution progressively.Moreover, many medical problems are complex and often standard solutions
are not easy to find. A multiagent problem-solving is based on decomposition in
subproblems. Let us take for example organ transplants [11]: when a new organ is
available, the more appropriate recipient must be found very quickly. It can be
located in a very far medical center. Moreover, each hospital keeps the data of
its patients; they are in the waiting list depending on the type of organ. It
would be difficult to conceive and apply a complex centralized system to solve
this coordination problem (e.g., a standard decision aid expert system).Multiagent technology also proved its reliability in medical information
retrieval. A great quantity of medical knowledge is available on the Internet,
and it is necessary to access to the most suitable information. The agents can
be employed to play the mediators between doctors and patients, or between medical
resources. These agents seek information issued from various sources, analyze
selected data, and choose useful information according to the profiles of the
consultants.To conclude, the agents’ autonomy is an adequate paradigm to deploy systems,
in which each component models the behaviour of an independent entity; this
entity has its own knowledge, skills, and individual goals.We recalled the general interest of multiagent systems in the health
field. Now we are going to present the expected functionalities of our system.


## 3.2. Architecture and Functioning

The system is based on a bunch of sensors carried by monitored people or
installed in their homes. Those sensors are, for example, presence and movement
sensors or medical measuring apparatus. The data coming from sensors are
transformed into indicators. Some indicators can also come from human
information: notes of a nurse or patient’s answers to a questionnaire.These indicators will be used by the system to generate its
classification. Their abstraction from data requires a software layer. The set
of sensors and this software layer are out of the scope of our work. It is the result
of projects described in Section2.It is important to note that the functioning of the system is
independent of the type and the number of indicators.Indicators are collected by classification agents constituting the
system. Because the system is strongly distributed, indicators of two people
will not be inevitably collected by the same agent. There can also be some overlaps,
if the same information is collected by several agents.Thus, classification agentsAj have indicators ik concerning several individuals Pi (Figure 1).Figure 1
S(MA)2D architecture.With its indicators, each agent calculates a local, partial
classification. This classification does not take into account all the
indicators and is related to a reduced sample of the population.Since the data inputs are numerical values, any statistical
classification method is applicable.To refine this classification, the agents communicate each other. They
congregate in acquaintances network according to the similarity of the produced
partitions. More precisely, each agent seeks the other agents which made a
classification close to its own. To compute the classes of the collaboratively
determined partition, we designed a restricted cooperation protocol in three
steps: call for participation/acquaintance’s group constitution/multiagent
classification.Section3.3 gives a detailed example of this protocol.There may be several groups of agents. They constitute parallel classifications:
they are views of the same monitored people but according to various criteria
(Figure2).Figure 2
Multiagent classification.


## 3.3. Example

It is assumed that the behaviour indicators have numerical values. These
values can be normalized by several methods as(i) normalization between[0⋯1](1)I˜j=Ij−IjminIjmax−Ijmin,
where Ijmin (resp., Ijmax) is the minimum value (resp., maximum value) of indicator number j, and(ii) linear normalization(2)I˜j=Ij-I¯jσj, where I¯j is
the average values of indicator number j for a given agent, and σj is the standard deviation of the indicator
number j for a given agent
(3)σj=Vj,Vj=1n∑k=1n(Ijk−I¯j)2,
where Vj is the variance of indicator
number j for a given agent; n is the number of people monitored by an agent;Ijk is the indicator number j of the person number k.Thereafter we apply our proposal on an example of 3 agents, 3 behaviour indicators,
and 11 people. SupposeI1 is the body temperature, I2 is
the number of getting up/sleeping in one night, and I3 is the number
of entries to the toilets each day.The following table shows the distribution of people (Pi) and indicators (Ij) on
the agents (Ak)
of the system.Table2 shows that agent A1 monitors 2 indicators I1 and I2 on people P1, P2, P3, P4,
P5, and P11. Agent A2 monitors 2 indicators I2 and I3 on people P4, P5, P6, P7,
P8, and P11. A3 monitors 2 indicators I1 and I3 on people P3, P6, P9, P10,
and P11.Table 2
Distribution people/indicators.
People∖IndicatorsI1I2I3P1A1A1—P2A1A1—P3A1, A3̲A1A3̲P4A1A1, A2A2P5A1A1, A2A2P6A3̲A2A2, A3̲P7—A2A2P8—A2A2P9A3̲—A3̲P10A3̲—A3̲P11A1, A3̲A1, A2A2, A3̲This table also shows that people do not have the same indicators (it will
often happen in real situations). For example,P1 has only two
indicators because for this person it is not necessary to test the number of
entries to the toilets. The aim is that each person has the indicators suited
to his case.We assume that the sensors send data to the system on a daily basis. In
reality there are indicators that are more important than others, for example,
body temperature is more important than the outside temperature, so we give a
weight for each indicator; this weight will help us later to form the groups of
agents and to calculate the distance between classes. The most important indicator
will be the one with the largest weight.In our case we give toI1 (body temperature) the weight 3, I2 the weight 2, and I3 the weight 1 (which is the default value).By applying a local classification method (e.g., ISODATA [12]) each agent
builds its partition. Each class is characterized by a midvector calculated by
ISODATA.Preliminary step:construction of partitions (Figure 3).Figure 3
Local classification.The first step is thecall
for participation. It aims to form groups of agents to generalize
the classification. The agents of the system communicate with each other
through the facilitator agent. The process to constitute agents groups for each
agent Ai is as follows:(i)
Ai sends
its indicators to other agents;(ii)
Ai receives
the indicators from other agents;(iii)
for each other agent,Ai calculates the sum of the weights of common indicators (calling S1),
and the sum of the weights of  noncommon
indicators (calling S2);(iv)
ifS1≻S2Ai responds to the agent
concerned;(v)
the agents of a group are
agents who have exchanged messages between them.
In our case, A1 sends I1 and I2. A1 also receives from A2 and A3 their indicators. We find
(4)A1∩A2=I2,A1∩A3=I1,A2∩A3=I3.
And as the weight of I1 is greater than I2 and I3,
A1 chooses A3 to form a group. The result is two groups
of agents. The first group is formed by A1 and A3, and
the second is formed by A2.This second step isthe
acquaintance's group constitution.The third (and last) step is togeneralize the classification. The agents of a group measure
the distances between their classes using the weighted Euclidean distance:
(5)dw(c,c′)=(∑1≤j≤nwj⋅(dj(c,c′))2)1/2.
In which c and c′ are two classes, wj is the weight of the indicator
number j, n is the number of common indicators between the two classes, and dj(c,c′) is the distance between the two midvectors of the two classes according
to the indicator number j.We can apply this formula on the actual values or normalized values of
indicators. In this example we use the actual values. The agentA1 seeks
to each of its classes, the closest classes of its group among other agents.Calculation of distances between classes:(6)dw(CA11,CA31)=3;dw(CA11,CA32)=0;dw(CA12,CA31)=0;dw(CA12,CA32)=3;dw(CA13,CA31)=3;dw(CA13,CA32)=23.
After the calculation of distances, we find that the class CA11 should be merged with CA32, and that CA12 should be merged with CA31. By contrast, CA13 should not be merged with CA31 because there is another class from A1 nearest to CA31.The new classes thus obtained (Figure4) have new midvectors. These
vectors are the averages of the indicators values of people belonging to the
same class.Figure 4
Result of the classification.A person may belong to several classes according to the indicators used.
For example,P3 is classified by A1 and A3 in
a class by itself according to I1 and I2, and it is
classified with P2, P5, P6, and P10 according to I1, I2, and I3.As prospects, we intend to set a minimum threshold for the distance
between classes. This threshold will be based on indicators and their weights. If
the distance between two classes is greater than this threshold, they will not
merge, even if they are close in the sense described above. It will be a more
true-to-life approach.


## 3.4. Relevance of the Multiagent Architecture

This classification is actually multiagent because the classification
result is not the work of a simple agent, as it is the case in other multiagent
systems (choice of the most skilled [13]). It is really a collective work.This multiagent classification answers to the problem of the search of
patterns in an open and dynamic environment. Classical methods do not make it
possible to increase the system scale: for example, when the number of entries changes
(with the addition of a new indicator), all calculation and generation of
classes must be made again.Thus, our method satisfies the requirements of our application because
it does not depend on the type of the indicators and does not require preliminary
categories.The management of the monitored people continues throughout the
functioning of the system, as the agents collect more indicators values. Thus
patterns evolve and the class of people can change.Also an indicator can be deactivated: it corresponds to a data for which it is not essential to
monitor this type of people.


## 4. Applications Related to Health

Our system builds dynamic classifications of monitored people according
to indicators that depend on the application.This adaptability is the result of two essential characteristics. The
first is the dynamic evolution of classifications—if needed, new
data and new indicators can be added at any moment, and the system is able to
reconfigure its classes and generate new classification patterns. The second is
that the system is generic with respect to indicators and, thus, is able to
function on any type of applications having strongly distributed entries.Such a system is likely to bring solutions to several current problems in the home-monitoring field. Some of
these problems are presented in this section.Monitoring of dependent old people
Organizations of assistance to elderly people often use an evaluation
grid of the dependence degree to determine the service needed by people. The
result of this evaluation is also used to evaluate the cost of taking charge of
someone.The use of our classification system will make it possible to see
whether there is an adequacy between the evaluation of monitored people by the grid and the produced
profile classes. The matching of the two evaluation ways would validate our
approach but also could consolidate the relevance of the grid criteria. In the
contrary case, it will be necessary to re-examine the classification method
and/or the selected indicators.After validation, the system will be
able to follow the evolution of the dependence degree of someone. Thus,
somebody leaving his original pattern to enter a new one could be re-evaluated
by the helper organization, and the assistance could be adapted to his new
behaviours.Detection of global medical problems
A metamonitoring will also make it
possible to detect more global problems. The migration of a lot of people from
a class toward another or the modification of certain characteristics of a class
should indicate a collective event which affects several people; this can
happen, for example, during a heat wave or an epidemic.Remote monitoring of people suffering from
chronic health problems
This help is for already detected people, suffering of cardiac and pulmonary
insufficiencies, asthma, or Alzheimer disease.The possibility of having a global
vision of several monitored people can bring richer and more relevant
information on the follow-up; the distribution in classes and the historic of
the patterns evolution (system training) should allow new people entering the
system to get a better service; in particular, more appropriate alerts according
to the incurring risk should be generated.Preventive control of
high-risk people
In the long term, with the evolution of life ways, we can consider the
monitoring of healthy people with personal or family antecedents relating to a
disease or a medical event.The system will make it possible to
identify evolution diagrams of health parameters and life way (e.g., state-of-the-immune
system, sleeping, nutrition, activity, etc.) who will indicate high risks to
develop diseases.


## 5. Conclusion

We chose to tackle the home-monitoring issue in a more global way rather
than in an only individual-centred way. This collective vision makes it
possible to release individuals’ patterns who will allow the system answering
current health problems.This large-scale and global solution (uninterrupted monitoring of hundreds
of people) requires setting up a strongly distributed and dynamic system. Because
classical classification methods are not adapted to this context, we had to
propose a new distributed classification method.The multiagentS(MA)2D system implements this method. To evaluate its
performances, we randomly generated a great number of numerical vectors of
values and we observed the formation of classes.Now, we have to define the real indicators to take into account. One of
our professional partnersCVital (platform of coordination of care and services to the person) is making a study
about people whom this organism follows. This study will make it possible to
define the number and the types of main indicators.We will also request them to semantically interpret the classes.



---

*Source: 136054-2008-04-17.xml*