=Paper=
{{Paper
|id=Vol-1122/paper1
|storemode=property
|title=Home Care Expert Systems for Ambient Assisted Living: A Multi-Agent Approach
|pdfUrl=https://ceur-ws.org/Vol-1122/paper1.pdf
|volume=Vol-1122
|dblpUrl=https://dblp.org/rec/conf/aiia/SernaniCPDD13a
}}
==Home Care Expert Systems for Ambient Assisted Living: A Multi-Agent Approach==
https://ceur-ws.org/Vol-1122/paper1.pdf
Home Care Expert Systems for Ambient
Assisted Living: A Multi-Agent Approach
Paolo Sernani, Andrea Claudi, Luca Palazzo,
Gianluca Dolcini, and Aldo Franco Dragoni
Dipartimento di Ingegneria dell’Informazione (DII)
Università Politecnica delle Marche
Via Brecce Bianche 60131 Ancona Italy
{p.sernani, a.claudi, l.palazzo, g.dolcini, a.f.dragoni}@univpm.it
Abstract. The ageing process of population of the First World coun-
tries is increasing the interest towards solutions to improve the quality of
life of elderly or disabled people and their families, providing an econom-
ically sustainable healthcare. Thus Ambient Assisted Living (AAL) and
Ambient Intelligence (AmI) are moving towards the Artificial Intelligence
field to develop modular, adaptable and intelligent systems to cope with
changing needs that characterize the life of people with chronic diseases.
These systems should find smart and simple ways to improve the patient
quality of life, enhancing at the same time the communication between
the system, the assisted person, his family and the medical staff, acting
also as a filter of information that provides useful and significant data.
This paper introduces the basic architecture of an expert system for
AAL: the Virtual Carer (VC). It has to be understood as an IT system
modelling a distributed, reliable and modular sensor network composed
by biometric and ambient sensors, being able to communicate with an
assisted person, to monitor his health conditions and to control the en-
vironment around him. The main goal of the system is to help an elderly
patient with his daily activities ensuring his security. The proposed sys-
tem is based on a multi-agent architecture to ensure its flexibility and
interoperability: new devices or sensors can be added simply adding new
agents, thanks to the standardization of agent communication. The sys-
tem includes also a reasoning part based on Belief-Desire-Intention (BDI)
paradigm, trying to model the behaviours of a human caregiver.
1 Introduction
It is a well-known fact that a shift in the distribution of a country’s population
towards older ages is ongoing in almost every country in the world, to the point
that the world has never seen as aged a population as currently exists globally.
Due to their higher dependency ratio (i.e. the ratio between the number of people
over 65 years old and those of working age), this is becoming a major problem
in Europe, USA and Japan [1, 2].
The increasing median age of the population has significant social and eco-
nomic implications. For example, it results in a rise in the number of chronic
diseases causing the increasing of health-related emergencies and the growing of
the healthcare spending [3]. Ambient Assisted Living (AAL) focuses on these
themes and aims at extending the time older people can live in their home en-
vironment, assisting them with the activities of daily living, promoting the use
of intelligent products and Information Technology (IT) tools to provide remote
care services [4].
Through the AAL Joint Programme, the European Union aims to foster the
emergence of AAL services and systems for ageing well at home, in the commu-
nity, and at work [5]. The AAL Joint Programme defines these key objectives
for AAL:
– to extend the time people can live in their preferred environment by increas-
ing their autonomy, self-confidence and mobility;
– to support maintaining health and functional capability of the elderly indi-
viduals;
– to promote a better and healthier lifestyle for individuals at risk;
– to enhance the security, to prevent social isolation and to support maintain-
ing the multifunctional network around the individual;
– to support carers, families and care organizations;
– to increase the efficiency and productivity of used resources in the ageing
societies.
Every AAL system is based on pervasive devices typically used in Home
Automation systems, and on Ambient Intelligence (AmI) technologies to inte-
grate devices and build a safe environment for the assisted person [4]. The main
goal of AmI is to help people in their daily activities, building around them an
unobtrusive, interconnected, adaptable, dynamic, embedded, and intelligent en-
vironment [6]. Humans can interact with AmI-based systems using natural user
interfaces like speech and gestures. One of the goals of AmI is to allow the user to
interact with an AmI system as he would do with any other human [3]; anyway,
assistive technologies can supplement human caregiving and cannot substitute
it, having the potential to improve the quality of life for both older adults and
their caregivers [7].
AAL and AmI systems have to adapt themselves over time to cope with
the changing needs and health conditions of the assisted person. They need to
perceive variation in habits that can signal health-related problems or stressful
situations. Thus, applying Artificial Intelligence techniques on this domain seems
to be a promising direction for research [8, 9]. Moreover the need of a smart inter-
action between AmI systems and the elderly is guiding the research community
towards the exploration of Human-Computer Interaction (HCI) and Human-
Robot Interaction (HRI) solutions. In such a context elderly people should be
considered the most prominent stakeholders for IT developments [10].
1.1 Our Contribution
In this paper we propose a Multi-Agent System (MAS) for Ambient Assisted
Living: The Virtual Carer. It should be understood as a system to manage a
distributed sensor network composed by ambient and biometric sensors, being
at the same time an interface layer between the network, the assisted person, his
relatives and the medical staff. Furthermore it includes a goal-oriented reasoning
module, i.e. the Virtual Carer Agent (VCA), based on Belief-Desire-Intention
(BDI) paradigm.
The adoption of the MAS architecture derives from the natural features of
these kinds of systems. As stated in [11] the main advantages are:
– distribution;
– modularity;
– robustness.
The MAS architecture allows to distribute software agents and the platforms
that manage them over one or more networks; thus each sensor can be associated
with an agent, making the system resilient to sensor failures and disconnections.
The adoption of the FIPA standards as the Agent Communication Language
(ACL) allows to add and remove sensors and devices in a transparent way,
simply wrapping them with an agent.
In addition to the listed features, we took into account also pro-activeness,
imagining a system that can autonomously take the initiative, guided by goals.
Thus the reasoning module of the system, i.e. the VCA, is based on BDI paradigm.
The VCA’s main goal is to model logical structures, reasoning and behaviours
similar to those of a human caregiver, transforming data from sensor agents in
logical predicates representing its knowledge base. To perform actions on the
monitored ambient and interact with the assisted person, VCA can collaborate
with agents controlling actuators. Thus the system can monitor the health con-
ditions of the assisted person and facilitate his daily activities.
1.2 Paper Structure
The rest of the paper is organized as follows. The next section deals with some
related works on MAS, AAL, HCI and HRI; then the core of the proposed
system is described. Section 4 deals with the description of the whole agency,
describing the implementation of a simple simulation scenario and highlighting
some features of the Virtual Carer. In the end conclusions are drawn and future
work suggested.
2 Related Works
As stated in [12], an agent is an entity that can perceive the environment through
sensors and act on the environment through actuators. In [13] the main prop-
erties of an agent are outlined: autonomy, social ability, reactivity and pro-
activeness indicate that agents operate without external intervention, interact
with other agents using some kind of Agent Communication Language (ACL),
perceive the environment answering to changes and are able to autonomously
exhibit goal-directed behaviours.
BDI agents act on the basis of practical reasoning [14]: their intentions, i.e.
the goals agents are committed to achieve, originate from the intersection be-
tween beliefs, i.e. the agent knowledge about the environment, and desires, i.e.
the set of states representing the environment as desired by agents. MAS and
BDI agents with an appropriate knowledge base can meet the guidelines of AAL
and AmI, indicating the need of unobtrusive, adaptable and dynamic intelligent
environments [6] and requiring technical solutions that are flexible and adapt-
able to individual and changing needs [15]. MAS applications in the AAL domain
are proposed in several scientific contributions. In the system described in [16]
BDI agents carry out specific tasks, as controlling air condition and monitoring
patient’s heartbeat, in patient monitoring scenario. In [17] authors present a
system to monitor patients suffering from dementia, in which a Risk Assessment
Agent, using its knowledge base and information provided by agents responsible
for various ambient sensors, applies a method based on fuzzy logic to predict
risk situations and to trigger proper alarms. The work in [18] is based on Home
Automation and describes a small society of BDI agents able to cope with energy
efficiency issues when different devices are available. In [19] a Smart Home En-
vironment is based on a MAS: a Butler Agent infers the user’s goals to provide
him suitable services, gathering information from the other agents.
A different research area is focused on Internet of Things scenarios for AAL
and remote healthcare [20]. Here, the main problem is interoperability between
different devices and sensors. The multi-agent paradigm offers a way to integrate
such information sources in a manageable knowledge base, thus allowing for a
better use of resources.
Also sensor networks, HCI, HRI and robotics applications are gaining an
increasing attention, finding in AAL a natural field of application. In [21] data
provided by a hierarchy of wireless sensor networks, in a home environment,
are processed with machine learning techniques to monitor the posture and the
health conditions of an elderly. The work in [22] highlights how the movements
of the assisted person can be analysed. Voice interaction seems a natural way to
communicate with the elderly, especially in smart homes ([23]): in [24] a set of
patterns for spoken output are analysed; the acceptance degree of speech synthe-
sis and audio interaction is evaluated in [25], where different English accents are
considered. More in general the evaluation of the adoption of robotic technolo-
gies in healthcare and AAL has been object of several researches: for example
in [26] the gender is recognised as more relevant than the age to asses the ac-
ceptance of a robot to measure the blood pressure of a patient; the work in [27]
defines metrics to evaluate the interaction between robots piloted from remote
and the inhabitants of the environment. In addition, the research on robotics is
providing ever more powerful technologies as Attentive Systems ([28]).
3 System Architecture
The Virtual Carer is a MAS modelling a distributed, reliable and modular sensor
network composed by biometric and ambient sensors, and integrating a BDI core
agent (Virtual Carer Agent, VCA). The Virtual Carer is an IT system able to
communicate with an assisted person, to monitor his health conditions and to
control the environment around him. To cope with a highly variable environment
the VCA models reasoning mechanisms and behaviours similar to those of a
human being following the BDI paradigm. The large amount of information
used by VCA is represented by logical predicates, forming its Knowledge Base
(KB). VCA chooses the right actions to perform on the environment with an
inference engine applied on its KB. The VCA works as follows:
– it analyses data provided by sensors and devices forming the system (i.e. by
agents controlling them) and updates its KB;
– when its KB is updated, VCA generates new knowledge using its inference
engine;
– with backward reasoning rules applied on its KB, VCA selects the main goal
and chooses (through backtracking) the plan to satisfy it;
– VCA carries out the actions of chosen plan, collaborating with agents re-
sponsible for actuators, in order to directly act on the environment.
Fig. 1. Virtual Carer basic architecture
Figure 1 shows the basic architecture of the system. The system is composed
by the BDI Virtual Carer Agent, a Register Agent and a number Actuator
Agents and Sensor Agents.
3.1 Virtual Carer Agent
The main goal of this agent is to provide assistance to the elderly, through the
execution of its plans on the basis of the context (its belief base), according to
the BDI paradigm. Figure 2 shows the Actor Model in the i* language [29].
Fig. 2. Virtual Carer Actor Model
Goals can be satisfied thanks to several tasks: the VCA has to identify the po-
sition of the elderly, his health conditions and to detect anomalies using available
sensors. All the detected anomalies are registered through the Register Agent.
In order to complete these tasks the VCA has to register with the Directory Fa-
cilitator (DF) of the whole agency, to get its services available for other agents.
3.2 Sensor Agents
Sensor Agents can be distinguished in two types: Ambient Agents and Health
Agents. Ambient Agents are responsible for reading the value of ambient sensors
as, for instance, the temperature of a specific room. They check if the recorded
values are in a predetermined range: if the value is outside of the range, the
Ambient Agent communicates the anomaly to the VCA (figure 3). Presence
sensors are managed by Ambient Agents, too. For example, we can consider a
Passive Infrared (PIR) sensor: the Ambient Agent controlling it merely informs
the VCA about the presence of someone in the monitored room. Health Agents,
instead, manage sensors measuring values related to the health conditions of the
assisted person.
Fig. 3. Ambient Sensor Actor Model
3.3 Actuator Agents
These agents are responsible for the activation and deactivation of the devices
composing the system, such as speakers, lights, monitors and so on. As high-
lighted in figure 4, they must be able to receive request messages (typically from
the VCA) in order to execute on/off commands on the devices. Hence, their
main goal is the switching of the state of the controlled device, after a request
is received.
Fig. 4. Actuator Actor Model
3.4 Register Agent
The Register Agent is responsible for the database storage of all the information
provided by sensors and of the anomalies detected by the VCA. Figure 5 contains
the Actor Model for the Register Agent. Its goal is to register anomalies in the
database.
Fig. 5. Register Actor Model
4 Implementation
4.1 Agency
Fig. 6. Sequence diagram representing the interactions between the agents composing
the Virtual Carer System
The typical interactions between the VCA, Sensor Agents, Register Agent
and Actuator Agents are shown in figure 6. The VCA receives alarms from Sensor
Agents when a value is out of the predetermined range; in addition, the VCA
can directly require to read a value because the chosen plan requires data to
satisfy the goal. The Register Agent receives and stores in a database values
from Sensor Agents and anomaly reports from the VCA. The VCA can require
values from the database to the Register Agent. The VCA sends request to the
Actuator Agents in order to execute actions corresponding to the adopted plan.
Sensor and Actuator Agents are implemented using the JADE framework [30]
whilst the BDI agent representing the VCA and the Register Agent are imple-
mented using JASON and the AgentSpeak language [31]. It is important to notice
that the use of JADE framework allows the communication between agents with
FIPA-ACL messages, ensuring the modularity of the system: different devices
can be added any time simply integrating more agents. JASON is used for the
VCA, in order to apply the BDI paradigm, and for the Register Agent, allowing
to store beliefs directly in the database.
4.2 Simulation scenario
Fig. 7. Home map representing the implemented simulation scenario
Figure 7 shows the fictional home map we used in the simulation scenario,
highlighting the arrangement of the available devices in the environment. In order
to test the proposed architecture, preliminary simulations in a that scenario were
carried out.
There are 4 rooms: the bedroom (room 1), the hallway (room 2), the bath-
room (room 3) and the living room (room 4). For each one, a temperature sensor
(T1, T2, T3, T4), a PIR sensor (PIR1, PIR2, PIR3, PIR4) and a device to turn
the light on/off (AL1, AL2, AL3, AL4) are provided. In addition there is a
speaker (SP1, SP2, SP3) for each one of the first three rooms, and a monitor to
visually communicate with the assisted person in room 4. Pressure sensors are
supposed to be in each place where the person can sit or lie down (e.g. couch,
bed, seats), and contact sensors are supposed to be placed on every door and
window, to monitor their open/close state. Body sensors are used to monitor
the vital parameters (e.g. heartbeat, body temperature) of the assisted person.
For each sensor and device in the described scenario the proper Ambient, Health
and Actuator Agents are created.
4.3 Virtual Carer Agent
At the beginning of the simulation the VCA’s belief base includes facts represent-
ing the environment structure and pointing out the available sensors and devices.
Figure 8 shows an example of facts describing how the home environment for
the simulation scenario is divided in rooms.
partOf(couch,hallway).
partOf(bed,bedroom).
partOf(livingRoom,home).
partOf(bedroom,home).
partOf(hallway,home).
partOf(bathroom,home).
Fig. 8. Belief base: composition of the simulation scenario
Another extract of the belief base is shown in figure 9. The listed facts indi-
cate the areas covered by the available devices. For example the video device is
significant for the couch; the speaker 2, instead, covers a whole room.
covers(tv,couch).
covers(speaker1,bed).
covers(speaker2,hallway).
covers(speaker3,bathroom).
Fig. 9. Belief base: room covered by video and audio devices.
In addition to the facts in the belief base, the VCA knowledge base is com-
posed also by rules to infer new knowledge and thus to activate plans. The rules
allow the VCA to localize the elderly in a specific room, to establish which ac-
tuator covers the room and to decide if it has to be activated for an emergency
plan. Some examples of these rules are shown in figure 10.
In this simulation, the VCA has several emergency plans: the one shown in
figure 11 is executed when an Ambient Agent, responsible for a temperature
admissible(D):- inside(A) & videoDevice(D) & covers(D,A).
admissible(D):- inside(A) & audioDevice(D) & covers(D,A).
covers(D,R1):- partOf(R1,R2) & covers(D,R2).
inside(R1):- partOf(R1,R2) & inside(R2).
Fig. 10. An example of the rules in the VCA knowledge base
sensor, reads a value out of the predetermined range. In this case the VCA
communicates the anomaly to the Register Agent and activates the plan ”notify”
in order to evaluate if the monitored person has to be notified with some alarms.
A similar plan is activated when the body temperature of the assisted person is
out of a security range.
+emergency1(V)[source(A)] : true <-
.print("Room temperature out of range");
.print("from sensor ", A);
.print("value ", V);
.time(H,M,S);
.send(register,tell,anomaly(A,V,H,M,S));
.print("Information sent to Register Agent");
?em1(N);
New = N + 1;
-+em1(New);
!notify;
!canc.
Fig. 11. Emergency plan for room temperature
The system was complemented by a BDI agent, the Elderly Agent, modelling
behaviours and activities of the person assisted by the Virtual Carer System. For
simulation reasons the Elderly Agent is able to send messages to other agents
of the system: for example, it informs the PIR agent when it moves in the
controlled room, and sends requests to the Health Sensors to know the value of
specific health parameters. To simulate the different behaviours of the assisted
person, the Elderly Agent was provided with different plans. Figure 12 shows a
plan including the transition from the hallway to the bedroom, and the request
of the body temperature value.
In a first simulation scenario, we modelled the situation in which the assisted
person goes to bed and measures its body temperature. The simulation works
as follows. At first, the Elderly Agent initializes the VCA, providing it the infor-
mation about its initial position; then, after 10 seconds (i.e. the time necessary
to get to bedroom), it informs the PIR Agent of its presence in the final position
(this simulates the detection of the elderly by the PIR sensor). The PIR Agent
controlling the bedroom notifies the VCA about the presence of a person in the
+-scenario5 : true <-
.send(carer,tell,init(hallway));
.wait(10000);
.send(pir1,tell,at);
.send(carer,achieve,queryP(tempB)).
Fig. 12. Elderly agent’s plan example
bedroom; thus VCA has a new belief (i.e. the presence of the assisted person in
room 1), and activates the plan (figure 13) to turn the light on in the bedroom
and to turn it off in the hallway. Finally, the Elderly Agent sends a message
to the VCA to know its body temperature value. VCA activates a plan that
includes all the actions necessary to require the data from the proper Health
Agent and to communicate it to the Elderly Agent.
@ent[atomic]
+enter[source(A)] : true <-
?inside(M);
?spir(A,R);
M \== R;
-+inside(R);
!powerOn(R,M);
.print("light on in ");
.print(R);
.print("light off in ");
.print(M);
-enter[source(A)].
Fig. 13. When the VCA has a new belief ”enter” sent by a PIR sensor, it updates its
belief base with the new position of the elderly and activates the plan ”powerOn”
Beside plans similar to the one just described, we simulated plans without an
explicit request by the Elderly Agent, in which the Virtual Carer had to infer the
right actions. For example, when a window remains open in the bedroom and
the assisted person is sleeping, the decreasing of room and body temperature
should activate the plan to wake up the person and ask him to close the window.
In this scenario, the Ambient Agent associated to the bedroom window informs
the VCA that the window state is ”open”. At a later stage the Ambient Agent
responsible for T1 sensor communicates to the VCA that the detected value
is out of the predetermined range; a similar message is sent by Health Agent.
VCA updates its KB with these new beliefs and activates a plan ending with a
message to the Elderly Agent, asking it to close the window.
4.4 Evaluation
The evaluation of an AAL system that aims at the interaction with the assisted
individual to help him and ensure its security should include several perspectives,
as described in [32]: at least the points of view of end users (elderly people
and their relatives), physicians, medical caregivers and developers (software and
systems engineers) are essential to assess the quality and the effectiveness of such
a system.
At this stage of the work only the developers’ point of view can be consid-
ered: the simulations we carried out are adequate to underline that MAS and
BDI paradigm are useful when applied to AAL contexts. Agents, by their own
nature, can be easily distributed over a network. The MAS approach and the
adoption of communication standards as FIPA-ACL guarantee modularity to the
system, in order to cope with the addition or removal of devices. Several agent-
based frameworks, as JADE and JASON (both adopted for our simulations),
provide techniques to respond to fault-tolerance requirements: the Main Con-
tainer Replication Service (MCRS) ensures the execution of the agent platform,
the Directory Facilitator (DF) Persistence supports the traceability of agent ser-
vices. The BDI paradigm allows to quickly respond to changes in a dynamic
environment: new data from sensors result in the updating of the belief base
allowing the VCA to infer new beliefs and new desires. Thus goals are estab-
lished and plans to achieve them scheduled, using the knowledge base rules and
backtracking mechanisms to select the best plan for the current state resulting
from the belief base.
Of course, the ideal test to fully validate the proposed approach should in-
clude an AAL scenario with sensors and devices deployed in a real home envi-
ronment. In such a scenario the analysis of the effectiveness of the system could
be carried out using participatory evaluations, as in [33].
5 Conclusions
We described a multi-agent architecture to model an expert system for AAL.
The system, named Virtual Carer, has to deal with a dynamic environment,
monitoring the health conditions of an elderly person. To achieve these tasks
the system should model a distributed, reliable and modular sensor network
including also those devices typical of home automation. The core of the proposed
system is a BDI Agent, the Virtual Carer Agent (VCA) and it represents the
reasoning module of the Virtual Carer.
The agents modelling various sensors and devices collaborate to simplify the
daily activities of the assisted person and to trigger alarms when something
is wrong with parameters regarding his health conditions. The multi-agent ap-
proach allows to distribute the agents and the platform over a network. The
adoption of frameworks such as JADE guarantees:
– the communication through FIPA-ACL messages, ensuring the flexibility
of the system. New devices can be added simply integrating agents in the
proposed platform.
– Recovery techniques (such as the DF Persistence or the MCRS) to achieve
fault tolerance goals.
We tested the proposed architecture considering several simulation scenarios
and representing the assisted person using another BDI agent. Several plans were
taken into account, modelling different actions performed by the Elderly Agent
(e.g. movements, direct requests, changing of health parameters). These simula-
tions confirm that MAS and BDI paradigm improve modularity and adaptability
of AAL and AmI systems. The multi-agent approach guarantees the modularity
of the system, whilst the BDI paradigm permits to respond to changes in the
environment and in the needs of the assisted person. Thus this work confirm
the crucial role of Artificial Intelligence to ensure a better quality of life in the
modern society.
As future work more insightful tests have to be conducted: a real AAL sce-
nario, i.e. a daily-used home environment equipped with devices and sensors,
where an assisted person can live, is beyond our possibilities at this stage of
the work, but is the only way to fully validate the proposed approach. Sev-
eral technologies could be integrated in the Virtual Carer system: for example
those for video surveillance described in [34] could be useful with the purpose of
monitoring the conditions of the assisted person, ensuring its security. Moreover
intelligent Cyber-Physical Systems, combining artificial intelligence and physi-
cal subsystems with computing and networking [35], are ideal for the proposed
system.
However the direction that this work seems to undertake is that one of an
interface between the medical personnel and the patient, being a Virtual Nurse,
i.e. a virtual hospital ward at the patient’s home environment. Such a system
should act as a filter of information for the medical staff, monitoring the health
parameters of the person on the basis of his disease and providing only that
information considered significant. The result could be an earlier dehospitaliza-
tion for the patient, following the need of having hospital wards available for
emergencies and trying to provide an economically sustainable healthcare.
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