This research takes place in the
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 [
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.
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
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 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 Section
The system manages a set of patterns of monitored people. 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, 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.
These aspects are discussed in the
last section.
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.
The PROSAFE project [
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, and 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.
The AILISA project [
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 project aims to set up an interdisciplinary platform for the
evaluation of the technologies at the three following levels: technical,
medical, and ethical.
The e-Vital project [
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).
We presented three systems which are
able to monitor the “elderly” in their own homes. Table
Overview of the three projects.
Project | |||
---|---|---|---|
Criteria | PROSAFE | AILISA | e-Vital |
Smart home equipment | Yes | Yes | No |
Equipment is installed in hospitals and residences of elderly people | Health smart homes | ||
Body wear equipment | Yes | Yes | No |
Accelerometer (or GPS) | Smart shirts with fall sensors | ||
Medical equipment | Yes | Yes | Yes |
Digital entries acquisition module | Wrist arterial pressure Pulse oximeter | Appropriate monitoring devices | |
Detected emergencies and supervised risks | Accidents Falls Escapes | Some medical risks Falls | Scheduled care Vital signs defection |
Target | Elderly or handicapped people Patient with Alzheimer disease | Elderly people Handicapped people | People with chronic diseases |
Project range (home, living environment) | At home and in hospital | At home | Living environment (that is at home but also in mobile situations) |
Risk-detection method | Sensors and statistical methods | Mainly hardware | Data management and interpretation made by a multiagent system |
Scale (how many people are concerned) | The system focuses on one patient but the profiling can be used in a more large scale system | The System is providing an individual help | |
Links to personal medical data | Yes | No | Patient’s electronic health record are stored in a hospital database (but this database is only used by the project) |
Medical validation | Tested in three hospitals (three other sites are planned) | Planned in three hospitals | Tests take place in four European pilot sites |
Ethical and psychological aspects | Technical mediation between health caregivers and patients | Technical mediation between caregivers and patients Psychiatric aspects | Not mentioned |
Operational or experimental | Experimental | Experimental | Experimental |
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 [
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.
We propose a system able to carry out a generalization of profiles’ patterns
and to propose a classification of monitored people.
We chose a multiagent approach because these systems proved their
adequacy in many health problems [
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 [
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.
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 Section
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 agents
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.
Section
There may be several groups of agents. They constitute parallel classifications:
they are views of the same monitored people but according to various criteria
(Figure
Multiagent classification.
It is assumed that the behaviour indicators have numerical values. These values can be normalized by several methods as
(i) normalization between
(ii) linear normalization
Thereafter we apply our proposal on an example of 3 agents, 3 behaviour indicators,
and 11 people. Suppose
The following table shows the distribution of people (
Table
Distribution people/indicators.
People | |||
---|---|---|---|
— | |||
— | |||
— | |||
— | |||
— | |||
— | |||
This table also shows that people do not have the same indicators (it will
often happen in real situations). For example,
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 to
By applying a local classification method (e.g., ISODATA [
Preliminary step:
Local classification.
The first step is the
for each other agent,
if the agents of a group are
agents who have exchanged messages between them.
In our case,
This second step is
The third (and last) step is to
We can apply this formula on the actual values or normalized values of
indicators. In this example we use the actual values. The agent
Calculation of distances between classes:
The new classes thus obtained (Figure
Result of the classification.
A person may belong to several classes according to the indicators used.
For example,
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.
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.
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.
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.
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.
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.
The multiagent
Now, we have to define the real indicators to take into account. One of
our professional partners
We will also request them to semantically interpret the classes.