1. A Smart Space Application to Dynamically Relate
Medical and Environmental Information
Fabio Vergari, Sara Bartolini Federico Spadini, Alfredo D’Elia, Guido Zamagni,
DEIS Luca Roffia, Tullio Salmon Cinotti
Alma Mater Studiorum - Università di Bologna ARCES
Bologna, Italy Alma Mater Studiorum - Università di Bologna
fabio.vergari, s.bartolini@unibo.it Bologna, Italy
fspadini, adelia, gzamagni, lroffia, tsalmon@arces.unibo.it
Abstract— In this paper we present a cross-domain application for as they feel more secure and untroubled” [5]. One of the
ambient and health monitoring. The system architecture is fundamental problems today's medical and telemedicine systems
intended to be openly extensible in order to fulfil unanticipated face is that the data is typically segregated and each
needs. Our implementation addresses diverse groups, from those sensor/system maintains its own proprietary information store.
requiring heart related monitoring, which could be dependent on This lack of interoperability severely limits the creation of
the environmental conditions, to those who need to maximize innovative applications that could benefit both caregivers and
environmental comfort under specified energy consumption users [6].
constraints. In this application the ambient information is used to
enrich the biomedical data and provide a more complete picture to Moving on from telemedicine to energy and environmental
the information consumers, such as doctors and building domains, a number of viable automation solutions exist, which
superintendents. The cross-domain nature of the scenario requires can help with our home energy use. These systems are capable of
data interoperability, which is ensured by a shared Smart Space. monitoring such factors as temperature, presence, light intensity
The Smart Space represents the information in Resource and other inputs [7]. In addition to these systems, a number of
Description Framework and its semantics are ontology driven. A smart homes have been designed which can collect and process
simple ontology for the addressed information domain is also data regarding user's day-to-day in-home activities [8]. However,
presented. The Smart Space platform is provided by the JTI this data has similar problems to those of medical information,
Artemis SOFIA project. i.e. they typically remain within the walled-garden of the original
system implementation. There are also a significant number of
Information interoperability; smart space; cross-domain
application; ontology; heart rate; discomfort index projects concerned with interoperability of heterogeneous
resources, but they are predominantly focused on applications in
their domain [9], [10].
I. INTRODUCTION AND MOTIVATION
The separation of concerns between health and energy
Health and energy monitoring and management are some of
systems masks interesting opportunities for valuable cross-
today’s pressing social needs and, as such, are becoming primary
domain applications that could have a significant impact. Cross-
technology drivers, with an impact on industry and research
domain applications based on concurrent processing of health,
agendas worldwide. Statistical estimates show that the growing
environmental and energy information could in fact be interesting
and aging population will have a heavy impact on our medical
to different institutions. For example the service manager of an
system. In addition, society is trying to curb its impact on the
unspecified confined area, e.g. a building, an exhibition area, a
environment and bring its energy consumption to sustainable
sport arena, could be willing to dynamically adapt the energy
levels while at least maintaining an acceptable standard of living.
consumption/environmental comfort trade-off to the profile,
The seemingly disjoint nature of these issues implies that they
activity and to the varying biometric parameters of the people in
have historically been looked at separately in industry and
the space. On the other hand a doctor could be interested in
literature.
interpreting the health-parameters of their patients by relating
A number of telemedicine systems have been proposed to them to her patient’s environment and situation. Furthermore,
reduce the amount of time a primary physician must spend with medical researchers interested in defining the safety sanitary
each patient while still allowing a high level of care [1], [2], [3], protocols could be interested in understanding the reaction of
[4]. The wide acceptance of telemedicine is well known in the significant population samples to extreme meteorological events,
daily press, where it is quite common to find statements such as such as heatwaves [11].
”Patients who tried heart telemedicine, do not want to give it up,
978-3-9810801-6-2/DATE10 © 2010 EDAA

2. Figure 1. Proprietary and domain specific approach
As long as health and energy communities keep living in Figure 2. Interoperable approach
separate islands, relying on their own standards and
infrastructures that lack interoperability (Fig. 1), the multi- II. APPLICATION FUNCTIONAL DESCRIPTION
domain nature of the above applications makes them
We claim that if environmental and health data are made
unsustainable due to their high complexity and cost.
interoperable, then they may be abstracted to generate new
In this paper we imagine a different scenario (Fig. 2) where knowledge, and thus effectively reused in innovative applications
information consumers and producers are decoupled and relevant to the benefit of multiple institutions and users. The scenario
information is stored on a shared information search domain devised in our research is depicted in Fig. 3. We partitioned the
accessible by all the relevant actors. With this approach, a wide physical space into “rooms” (room1 and room2 in Fig. 3).
range of cross-domain applications are possible, as long as the Temperature and relative humidity are sensed by Intel® iMote2
data model remains consistent and domain specific knowledge is sensor nodes placed in each room. In our current implementation
represented in an interoperable way. this data is transmitted to the shared information space through a
room PC. Users wear a Zephyr Bioharness BT [1] and a
The shared memory approach has been adopted by SOFIA, smartphone. The Bioharness senses skin temperature, heart rate,
an ongoing European Project lead by NOKIA®* which is respiration frequency, posture angle and an activity index, and
proposing a platform for sharing interoperable information in transmits this to the smartphone which, in turn, feeds the shared
smart environment applications. The platform is called the Open information store over a Wi-Fi connection. As we need user’s
Innovation Platform (OIP) and its goal is to make "information" location information, each room is equipped with an RFID reader
in the physical world available for smart services in embedded [12]. An RFID tag [13] is attached to each user’s
and ubiquitous systems. There are no a-priori restrictions on the smartphone. When a person enters a room, their tag needs to be
application domain - cross-domain as well as domain specific read by the reader that is located by the room entrance. This
applications are equally supported. action is recognized and fed to the shared information space
This paper describes the design and the structure of a cross- through the room-PC. This rather primitive but effective RFID
domain application that relies on the OIP to provide innovative based location system can be swapped out with more viable
services based on the concurrent, dynamic handling of people’s solutions in future deployments. Fig. 3 also shows how data
physiological parameters and their day to day surrounding gathered from the users and from the environment may be used.
environmental conditions. The temperature and relative humidity data are abstracted into the
thermohygrometric index, i.e. a bioclimatic index that is measure
The rest of the paper is organized as follows: Section 2 is the of the perceived discomfort level due to the combined effect of
functional description of the addressed application; Section 3 humidity and temperature conditions. Health monitoring and
describes the OIP, it’s terminology and main features; in Section Alarm management are still rather rudimentary. The first tracks
4 the ontology is designed, the implementation is modelled, and all of a user’s properties, i.e. her health parameters together with
the main software modules having access to the OIP’s shared the thermohygrometric index and the environmental conditions
information domain are detailed; in section 5 usual conclusions of the place where she is located. The alarm generator is meant to
are drawn. detect alarm conditions under specified policies and to publish
them to the benefit of dedicated alarm handlers. Currently the
alarm detection is threshold based and alarms are communicated
visually
*SOFIA (2009-11) is funded through the European JTI Artemis programme
under the subprogramme SP3 “Smart Environments and Scalable Digital
Services”, see http://www.sofia-project.eu

3. Smart Space Access Protocol (SSAP): Physical distribution
of a Smart Space
Smart Space
SIB
KP KP
Knowledge Processor (KP): SIB SIB
Semantic Information Broker (SIB)
Figure 4. OIP logical architecture
will take a further look at the SIB and its interacting entity, i.e.
the KP. The SIB acts as the shared information store for the OIP.
It utilizes the Resource Description Framework (RDF), a triple
based Semantic Web standard for expressing complex data as
directed labelled graphs in combination with an ontology. An
ontology contains all the definitions of the entities used within
the SS and their properties which are also used to relate the
entities with one another. The SIB provides an interface whose
Figure 3. Our scenario
fundamental components are: join, leave, insert, remove, query
Our claim is that along with the shown approach, valuable and subscribe. The protocol used to communicate with the SIB is
applications could be devised. Thanks to the information level entitled the Smart Space Application Protocol, an application
interoperability enabled by the platform (see section 3), the layer protocol based on XML. For a KP to interact with the SIB,
applications are agnostic with respect to device vendors and it must first join the SS then it can insert or query for information
types, as long as the information is available in the OIP. as needed. The interoperability between KPs is provided when
each KP is imbued with the knowledge from the relevant portion
III. LOGICAL ARCHITECTURE OF THE OPEN INNOVATION of the application’s domain ontology.
PLATFORM
IV. APPLICATION DESIGN AND IMPLEMENTATION
The OIP being developed within Sofia aims to make Designing SS applications is a two-step process: the entities
"information" in the physical world available for smart services involved in the application and the relationships between them
in embedded and ubiquitous systems. This implies a shift away must be modelled in an ontology, then the application must be
from the classical focus on interoperability in the physical/service partitioned into distinct KPs. When approaching the design of an
level towards interoperability at the information level. The ontology, the designer must take into account the information
information level can be seen as a set of information producers hierarchy in their application. The information interoperability is
and consumers, shared information and its semantic data model. directly impacted by how expressive the ontology is. One distinct
The OIP architecture is simplicity driven and it is made up of advantage of the OIP is that the ontology used can be sub-classed
three distinct entities (Fig. 4): after design time and new information can still be interpreted
thanks to the deductive closure calculated by the query engine
1) Smart Space (SS) is a named search extent of [14].
information;
When designing our ontology we took an iterative approach:
2) Semantic Information Broker (SIB) is an entity (at we first performed a top-down analysis of our system functional
the information level) for storing, sharing, and requirements. After finding the abstract concepts, we took a
governing the information of one SS; bottom-up view of the system to understand what kinds of sensor
information our hardware would be providing. The resulting
3) Knowledge Processors (KP) is an entity interacting ontology is shown in Fig. 5. Our main entities, i.e. classes, are
with the SIB and contributing and/or consuming Person, Environment, Alarm, Device, and Data. The Person and
content according to a relevant ontology. Environment entities are self-explanatory. Alarms are entities
characterized by an AlarmType, e.g. HeartRateAlarm, and in this
case are related to Environments or to Persons. Devices are
Using this viewpoint, a SS can be made up of a number of objects that can produce data or run KPs and are described by
SIBs which provide information interoperability and at least two their characteristics, e.g. resolution, communication channels,
KPs, one a producer and the other a consumer. In order to better MAC addresses. Data read by a sensor is represented by a literal
understand the application presented in Section 2, the following value, i.e. the reading, a timestamp, the type of measurement, e.g.
heart rate, temperature, humidity, the unit of measure and its
privacy level. By modelling the data class in this way, we

4. ensured that any KP consuming sensor data would be able to take
advantage of new sensor types without having to rethink the KP.
Once our application’s ontology had been defined, the KPs were
identified and modelled as shown in Fig. 6. The following
subsections describe the functionality of the separate KPs in the
system.
A. Environmental Sensing KP
The environmental sensing KP publishes environmental
sensor data to the system. It has a simple user interface to register
the environment, the sensor platform and to associate them.
When humidity and temperature data are inserted into the SIB
they are associated with the room that is being monitored. In a
typical SS application the registration of the
environment and platforms would be performed separately,
however, for simplicities sake the two have been integrated.
B. Physiological Sensors KP Figure 6. KP-SIB interaction
This runs on a Windows Mobile smartphone and has a GUI
which is used to register the device, its data and alarm thresholds.
After configuration, data is captured from a Zephyr BioHarness:
heart rate, skin temperature, respiration rate, posture angle and an D. Location KP
activity index are associated with a user and the information is This KP interfaces with our RFID readers so that when a
inserted into the OIP. person enters a room, they are associated with the new location.
This KP also updates the location of any devices the person may
C. Thom Index KP have in their possession.
This KP calculates the thermohygrometric index for every
environment in the system. As discussed in section 2, this index - E. Alarm Generator and Announcer KP
also called Thom Index - is a derivation of the humidity and Performs a search for all entities that have an associated
temperature in the room and adjusts it to a new temperature that safety threshold and subscribes to the relevant data. When the
is more akin to that of what a person “feels”. The KP subscribes data falls outside of the threshold, an alarm is raised and placed
to any temperature data, performs a check on the unit of measure, in the SIB. This way any other KP wanting to perform some
and inserts the new Thom index data. The information is action is capable of doing so. Furthermore, in this particular
associated with a given environment based on the location of the instance, the same KP visualizes the alarm (Fig. 6).
sensors.
F. Health Care Monitoring KP
Allows the health care service to monitor a patient in real
time. The KP allows the viewer to select from all the people
available and then creates a subscription to all of their relevant
data. It uses all of the data on the SIB, so not just physiological
data is available, but also the person’s environmental information
as well. The KP visualizes instantaneous heart rate, skin
temperature, respiration rate, posture angle and an activity index
in addition to the user’s location, its Thom Index and
environmental data (temperature and humidity). Fig. 7 shows the
UI of this KP. The user’s information is also logged to a file. Fig.
8 shows some data collected from one such session; during this
example the patient is in a home environment walking between a
cellar and a living room. The seemingly low skin temperature is
due to the Bioharness infrared temperature sensor position on the
user’s chest. This KP runs unchanged on a Ubuntu laptop and on
a Maemo tablet (Nokia N810).
Figure 5. Ontology class tree

5. offered by the OIP. Multiple KPs are run on heterogeneous
devices, while each one takes advantage of the shared
information. This type of system can be extended by new
producer KPs as long as the new information produced follows
the ontology used in the original system. As long as this
condition is met, a consumer KP can take advantage of the new
information with little or no modification of its codebase. This
feature ensures the flexibility of the system to future expansion or
modification of the sensor platforms used. We are continuing the
development of this application by integrating it into a larger
system that will offer us the ability to effect the user’s
environment given their alarms and current state. We also hope to
continue this work by analyzing the medical benefits of having a
more holistic view of a patient by performing field tests with
biomedical engineers and clinicians. The continued development
of the OIP will lead to a number of varying new services and
applications. As our everyday devices evolve towards the vision
of the Internet of Things, data interoperability at the information
level will become ever more important.
Figure 7. Screenshot of health care monitoring KP
ACKNOWLEDGMENT
This work was funded by the European Commission, within
the framework of the ARTEMIS JU SP3 SOFIA project
(http://sofia-project.org/) . The authors would like to thank all
project partners who contributed to the definition and
implementation of SOFIA Open Innovation Platform. Section 3
and Fig. 3 are largely based on SOFIA work-in-progress. The
iMote2 devices used in the research were donated to the
University of Bologna by Intel Lab during a previous research in
2006.
NOKIA is a trademark of Nokia® Corporation.
Intel and iMote2 are trademarks of Intel® Corporation.
Other names and brands may be claimed as the property of their
respective owners.
REFERENCES
[1] Zephyr, 1 Sept. 2009 <http://www.zephyr-technology.com/>
[2] Telcomed Advanced Industries Ltd., 1 Sept. 2009
<http://www.telcomed.ie/>
[3] Alive Technologies , 1 Sept. 2009 <http://www.alivetec.com/>
[4] F. Spadini, F. Vergari, L. Nachman, C. Lamberti, T.S. Cinotti, "A wireless
and context-aware ECG monitor : an iMote2 based portable system",
Computers in Cardiology 2008, 14-17 Sept. 2008 pp. 997-1000
[5] Elena Meli “Più sereni col controllo telematico” Interview to Prof.
Figure 8. Health care monitoring session: Thom index, respiration rate, Massimo Santini, Head of Cardiology Division of San Filippo Neri
heart rate and skin temperature Hospital in Rome, Corriere della Sera, Sept. 6, 2009
[6] S. Wagner, "Towards an open and easily extendible home care system
infrastructure", Pervasive Computing Technologies for Healthcare, 2008.
PervasiveHealth 2008. Second International Conference on, Jan. 30 2008-
Feb. 1 2008 pp. 42-45
V. CONCLUSIONS
[7] Siemens-Desigo. Siemens AG, 1 Sept. 2009
In this paper we demonstrated an application where <http://www.buildingtechnologies.siemens.com/bt/global/en/products_syst
information consumers and producers are decoupled and relevant ems/building_comfort_hvac/home_and_building_automation/desigo/Pages
/desigo_home.aspx>
information is stored on the shared information search domain

6. [8] N. Noury, G. Virone, P. Barralon, J. Ye, V. Rialle, J. Demongeot, "New 2008, edited by David Arnold, Franco Niccolucci, Daniel Pletinckx, Luc
trends in health smart homes" Enterprise Networking and Computing in Van Gool
Healthcare Industry, 2003. Healthcom 2003. Proceedings. 5th International [11] S. Zauli Sajani, G. Garaffoni, C. A. Goldoni, A. Ranzi, S. Tibaldi, P.
Workshop on , 6-7 June 2003 pp. 118-127 Lauriola, "Mortality and bioclimatic discomfort in Emilia-Romagna, Italy"
[9] I. Roussaki, I. Papaioannou, D. Tsesmetzis, J. Kantorovitch, J. Kalaoja, and J Epidemiol Community Health - 2002, vol 56 pp 536–537
R. Poortinga, "Ontology Based Service Modelling for Composability in [12] NanoUHF RFID Reader, 1 Sept. 2009 <http://www.tagsense.com/>
Smart Home Environments", Constructing Ambient Intelligence, 2008, vol.
11 pp. 411-420. [13] UHF RFID TAGS, 1 Sept. 2009 <http://www.power-id.com/>
[14] Ora Lassila, "Taking the RDF model Theory out for a spin" In Ian
[10] N. Ryan, P. Mohr, D. Manzaroli, G. Mantovani, S. Bartolini , A. D’Elia,
Horrocks and James Hendler, editors, Proceedings of the First International
M. Pettinari, L. Roffia, L. Sklenar, F. Garzotto, T. Salmon Cinotti (2008)
Semantic Web Conference, ISWC 2002, Sardinia, Italy, number 2342 in
"Interoperable multimedia mobile services in cultural heritage site" in
Lecture Notes in Computer Science, pp. 307–317. Springer-Verlag,
EPOCH Conference on Open Digital Cultural Heritage Systems, Rome,
Heidelberg, Germany, June 9 – 12, 2002.