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 . 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 : 
 the elderly want to 
 stay at home as long as possible ; they keep the privacy they do not want to 
 lose , it is less expensive 
 than a place in a collective accommodation . 
 
the elderly want to 
 stay at home as long as possible ; they keep the privacy they do not want to 
 lose , it is less expensive 
 than a place in a collective accommodation . 

it aims to help professional home - care 
 teams in their job by thinking up innovative software technologies , more 
 precisely : 

 by increasing the 
 number of old people looked after in their homes with an adaptive and 
 nonintrusive remote assistance , by reassuring family 
 circle .
the system ensures that the monitored person is secure ; so , people 
 around him feel at ease , andby 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 .
nevertheless , patterns of monitored people could be used to 
 estimate the status of someone in relation to their community or to integrate new 
 comings . by increasing the 
 number of old people looked after in their homes with an adaptive and 
 nonintrusive remote assistance , by reassuring family 
 circle .
the system ensures that the monitored person is secure ; so , people 
 around him feel at ease , and by contributing 
 towards its democratization .
the use of simple elements ( e.g. , basic sensors ) 
 minimizes the initial cost of a monitoring system .
we propose a multiagent system that is able to generalize , which builds a classification of monitored 
 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 section 3 , we present the architecture 
 of the system and how it runs .
this dynamically updated classification has the following three 
 main uses : 
 to find certain 
 similarities with the existing tools for evaluating the dependence 
dependence grid 
 of the social services , for example , to get global 
 statistical data about old people looked after in their own homes , andto 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 . to find certain 
 similarities with the existing tools for evaluating the dependence 
dependence grid 
 of the social services , for example , to get global 
 statistical data about old people looked after in their own homes , and 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 .
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 ) . 

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 
 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 , build a database with 
 all abnormal situations detected , andbuild 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 . 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 , build a database with 
 all abnormal situations detected , and build statistics about 
 past activities .
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 .
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 
 a home equipped with a set of 
 sensors and health devices ( presence detectors , wrist arterial pressure sensors , 
 and pulse oximeter),a smart shirt developed by 
 the french company tam with several sensors and electronics embedded in the 
 textile to detect falls , a smart assistant robot for ambulation 
 to secure the displacements and assist the person during transfers , anda 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 . a home equipped with a set of 
 sensors and health devices ( presence detectors , wrist arterial pressure sensors , 
 and pulse oximeter ) , a smart shirt developed by 
 the french company tam with several sensors and electronics embedded in the 
 textile to detect falls , a smart assistant robot for ambulation 
 to secure the displacements and assist the person during transfers , and a software system to gather 
 and analyze the sensors output .
the e - vital project   ( cost - effective health services for interactive 
 continuous monitoring of vital signs parameters ) is a modular and ambulatory telemedicine 
 platform .
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 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 .
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 ) .
we presented three systems which are 
 able to monitor the  elderly  in their own homes .
all 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   project , led by two french 
 research centers .
let us also talk of the european oldes   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 .
we propose a system able to carry out a generalization of profiles ' patterns 
 and to propose a classification of monitored people .
s(ma)d ( 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 .
we chose a multiagent approach because these systems proved their 
 adequacy in many health problems . 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 : when a new organ is 
 available , the more appropriate recipient must be found very quickly .
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 ) .
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 .
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 .
some indicators can also come from human 
 information : notes of a nurse or patient 's answers to a questionnaire .
the set 
 of sensors and this software layer are out of the scope of our work .
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 ,
there can also be some overlaps , 
 if the same information is collected by several agents .
thus , classification agents aj have indicators ik concerning several individuals pi ( figure 1 ) . 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 ,
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 .
they constitute parallel classifications : 
 they are views of the same monitored people but according to various criteria 
 ( figure 2 ) .
these 
 values can be normalized by several methods as ( i ) normalization between [ 0  1 ] 
 ( 1)ij = ijijminijmaxijmin , 
 where ij ( resp . , ij ) is the minimum value ( resp .
, maximum value ) of indicator number j , and ( ii ) linear normalization(2)ij = ij - ijj , where ij is 
 the average values of indicator number j for a given agent , and j is the standard deviation of the indicator 
 number j for a given agent 
 ( 3)j = vj ,  vj=1nk=1n(ijkij)2 , 
 where vj is the variance of indicator 
 number j for a given agent ; n is the number of people monitored by an agent ; 
 ij 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 .
suppose i1 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 .
table 2 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 .
this 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 .
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
in our case we give to i1 ( 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 )
the process to constitute agents groups for each 
 agent ai is as follows : 
 ai sends 
 its indicators to other agents;ai receives 
 the indicators from other agents;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);if s1s1ai responds to the agent 
 concerned;the agents of a group are 
 agents who have exchanged messages between them . 
 in our case , a1 sends i1 and i2 .
as the weight of i1 is greater than i2 and i3 , 
 a1 chooses a3 to form a group .
the first group is formed by a1 and a3 , and 
 the second is formed by a2 .
ai sends 
 its indicators to other agents ; ai receives 
 the indicators from other agents ; 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 ) ; if s1s1ai responds to the agent 
 concerned ; the agents of a group are 
 agents who have exchanged messages between them .
the agents of a group measure 
 the distances between their classes using the weighted euclidean distance : 
 ( 5)dw(c , c)=(1jnwj(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
the agent a1 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 ca1 should be merged with ca3 , and that ca1 should be merged with ca3 by contrast , ca1 should not be merged with ca3 because there is another class from a1 nearest to ca3 .
these 
 vectors are the averages of the indicators values of people belonging to the 
 same class .
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 .
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 ) .
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 .
our system builds dynamic classifications of monitored people according 
 to indicators that depend on the application .
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 .
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 .
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 .
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 . 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 .
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 .
, we randomly generated a great number of numerical vectors of 
 values and we observed the formation of classes .
one of 
 our professional partners cvital ( 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 .
 this research takes place in the s(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 . 
1. INTRODUCTION
2. A SURVEY OF THREE HOME-MONITORING SYSTEMS
3. S(MA)
4. APPLICATIONS RELATED
TO HEALTH
5. CONCLUSION