=Paper=
{{Paper
|id=Vol-2180/paper-14
|storemode=property
|title=HORUS.AI - A Knowledge-based Solution Supporting Health Persuasive Self-Monitoring
|pdfUrl=https://ceur-ws.org/Vol-2180/paper-14.pdf
|volume=Vol-2180
|authors=Mauro Dragoni,Tania Bailoni,Rosa Maimone,Michele Marchesoni,Claudio Eccher
|dblpUrl=https://dblp.org/rec/conf/semweb/DragoniBMME18
}}
==HORUS.AI - A Knowledge-based Solution Supporting Health Persuasive Self-Monitoring==
https://ceur-ws.org/Vol-2180/paper-14.pdf
HORUS.AI - A Knowledge-based Solution Supporting
Health Persuasive Self-Monitoring
Mauro Dragoni, Tania Bailoni, Rosa Maimone,
Michele Marchesoni, and Claudio Eccher
Fondazione Bruno Kessler, Trento, Italy
{dragoni|tbailoni|rmaimone|marchesoni|cleccher}@fbk.eu
Abstract. Automatically monitoring and supporting healthy lifestyle is a recent
research trend, fostered by the availability of low-cost monitoring devices, and it
can significantly contribute to the prevention of chronic diseases deriving from
incorrect diet and lack of physical activity. In this demo we show HORUS.AI, a
platform enabling the monitoring of people behaviors by means of knowledge-
based technologies for persuading them to follow healthy lifestyles.
1 Introduction
Recent studies like [1] shown that living a healthy life prevents cognitive decline,
obesity, disability and death from major chronic diseases (like diabetes, cardiovascular
disease, and several forms of cancer). In the domain of health and well-being, the use
of Information and Communication Technology (ICT)-based motivational systems that
produce user-tailored messages that can be effective tools to persuade and motivate
people to change their behavior toward a healthier lifestyle (adopting and maintaining
correct diet and active living).
However, engaging people in developing and maintaining healthier patterns of liv-
ing is a challenging task. To this end, generating effective personalized recommenda-
tions implies, for example, the justification of given suggestions and the adaptation of
messages in response to the modification of the environment and of the user status.
In this demo, we present a motivational platform for supporting the monitoring of
users’ behaviors and for persuading them to follow healthy lifestyles [3,4,5,6].
Semantic technologies are used for modeling all relevant information and for fos-
tering reasoning activities by combining user-generated data and domain knowledge.
Moreover, the integrated HeLiS ontology [7,8] supports the definition of the dynamic
interfaces used by domain experts for designing the set of rules exploited for the mon-
itoring activity. Contextually, our system aims at inducing the user to follow specific
behaviors and to maintain them over a certain timespan.
2 System Implementation
The system relies on the four (4) layers shown in Figure 1.
Input Layer The Input Layer is responsible for detecting events triggering the platform
activities and accounts for the ability of a persuasive system of sensing the context of
interaction. These events are of two types: (i) data input, where data are sent from the
Fig. 1. The schema of the proposed platform architecture.
Input Layer to the Knowledge Layer, and (ii) context communication, where contextual
information is sent from the Input Layer to the Persuasion Layer that may exploit this
information for persuasive purposes.
Here the distinction between data input and context communications relies on the
use of parameters by the system. Input data represent facts of the world related to the
user’s behavior that trigger knowledge layer rules in the specific domain (e.g., the as-
sumption of meals encouraged or discouraged by the Mediterranean diet recommenda-
tions). Context communication is related to the environment in which the user is acting
(e.g. timing or localization), and provide information to the Persuasion layer allowing
the choice of the most appropriate message generation strategy.
Knowledge Layer To support natural argumentation and (emotional) persuasion and to
allow reasoning on the possible arguments to be put forward, we defined methods for
representing knowledge, for reasoning on it, and for generating natural language and
multi-modal messages (both in monological and dialogical situations). Based on this
consideration, we developed a Knowledge Layer encompassing two kinds of informa-
tion:
– Augmented Domain Knowledge: the structured representation of the domain of
interest including those relations that are relevant for persuasion purposes, such as
the similar-taste relation or the categorization of food properties into negative and
positive ones. Moreover, we included all the concepts supporting the description
of the undesired situations detected by the reasoner and the relationships between
them. These concepts furnish the basis for the arguments included in the feedback
provided to users.
– Monitoring Knowledge: the structured representation of the rules driving the be-
havior change process (i.e. the rules that a user should follow). Here, we defined
the aspects of users’ behaviors that have to be monitored by the platform in order
to produce proper feedbacks (e.g. the categories of foods that should be consumed
and their optimal quantity).
Persuasion Layer In the Persuasion Layer we designed an effective Natural Language
Generation (NLG) persuasive system. We expanded on the idea presented in [9] of a
classification of basic persuasive strategies (what to say, how to say), supporting strate-
gies (i.e., strategies that are meant to give support to a specific claim) and a meta-
reasoning model that works on these strategies (selection and ordering of basic strate-
gies).
The Persuasion Layer exploits the output of the Knowledge Layer (i.e. reasoning
operations) for choosing the persuasive intentions to include the generated messages,
and focuses on the tasks of selecting the arguments to include in the message, to order
them, and to choose the right wording for each argument.
The role of the Persuasion Layer is not limited to the generation of single mes-
sages. Indeed, the application of a persuasion strategy generally requires more than one
interaction with the user. Thus, the Persuasion Layer is also in charge of managing
the relationships between single messages and understanding information provided by
users in order to built a reasonable conversation with the user.
Output Layer Finally, the Output Layer is in charge of closing the loop by providing
the feedback to users. It is represented by one of many devices able to receive the
data produced by the Persuasion Layer and to convey the physical feedback to users.
This layer manages two dimensions of interaction with the user: the Type of feedback
and the Presentation The former determines the optimal way for communicating with
users. This choice is strongly associated with the kind of device used for providing
the feedback. The latter manages how content generated by the Persuasion Layer is
presented to users is relevant for completing the process of supporting the behavior
change. Finally, the output provided by the platform could also be a further request of
inputs. Thus, a connection between the two layers has been implemented.
3 HORUS.AI in Action: What we Will Show During The Demo
The physical demo will be split in two parts: (i) the mobile application freely avail-
able on the markets that will be installed, if requested, by the attendants 1 , and the back-
end used for creating and configuring users’ profiles and for defining the rules exploited
for monitoring purposes. Then, we will show in particular the following modules.
Profile Creator. This set of facilities is thought for creating monitoring profiles that
are then associated with specific users or with groups of them. People enabled to using
these facilities are domain experts (i.e. physicians) because information managed in the
back-end could be related to the clinical profile of users. Thus, only authorized people
can access. We will show how domain experts can set-up groups and users, and how
they can create and assign profiles to them.
Rule Generator. Each profile generated into the platform has to contain one or
more monitoring rules in order to enable the monitoring of the users associated with
1 https://play.google.com/store/apps/details?id=eu.fbk.trec.lifestyle
https://itunes.apple.com/it/app/lifestyle-work/id1351897838?mt=8
each profile. Rules are provided by the domain experts through an interface built on
top of the underlying HeLiS ontology that guides the domain experts to the definition
of each rule. We will show the structure of the interface, how it is populated, how it
responds to the changes of the ontology, how a rule can be defined, and in which format
it is saved within the underlying ontology.
Live Reasoning. Once a user is associated with a profile and such a profile contains
the related set of rules, the system is able to monitor user’s behavior. The monitoring
task is performed through a SPARQL-based reasoner that combines profile information,
rules, and data provided by the users for detecting possible unhealthy behaviors. We will
show how the reasoner works, the steps it performs on the data and we will show its
efficiency within a live environment.
Personalized Feedback. The process ends with the generation of the feedback sent
back to the user. We will show how to install and configure the mobile application linked
to the HORUS.AI platform. The attendants will be associated with a profile and we will
explain them how to provide data. Then, we will show how the HORUS.AI reasoner
generates feedback by taking into account the selected profile and the provided data.
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