Activity Context Representation — Techniques and Languages: Papers from the 2011 AAAI Workshop (WS-11-04)
CARe: An Ontology for Representing Context
of Activity-Aware Healthcare Environments
Marcela D. Rodríguez1, Mónica Tentori1, Jesús Favela2, Diana Saldaña1, Juan-Pablo García1
1
Master and Doctoral Program of Computer Science, Autonomous University of Baja California
2
Computer Science Department, CICESE Research Center
{marcerod,mtentori}@uabc.edu.mx, favela@cicese.mx, {pablo.garcia,dianasj}@uabc.edu.mx
design approach which enabled us to identify the concepts
and relationships that characterize nurses’ activities in a
hospital [Tentori,11], and elders activities of daily living
carried at their homes [García-Vázquez, 10]. The CARe
ontology is a knowledge base consisting of a history of
users activities and information that shapes the way an
activity-aware application will behave [García-Vázquez,
10]. To facilitate the use of the CARe ontology we have
specialized the SALSA agent [Rodríguez, 05] framework
with customizable activity-aware mechanisms that enable
autonomous agents to infer and represent activities of
hospital workers and activities of daily living of elders
[Tentori, 11].
Abstract
Representing computational activities is still an open
problem in the field of Activity-Aware Computing. In this
paper, drawn from our experiences in developing activityaware applications in support of two populations: nurses
working in hospitals and elders living independently; we
defined the Context Aware Representational (CARe) model.
CARe is an ontology that enables the representation and
management of computational activities. We illustrate,
through application scenarios, that the CARe ontology is
flexible enough to enable developers to customize the
computational representation of healthcare activities.
Introduction
Activity-aware computing allows smart environments to
provide
continuous
activity
awareness
and
opportunistically offer assistance to support users’ current
activities [Tentori, 08]. One of the biggest challenges for
developing activity-aware applications includes the
representation of context in the form of activities.
Although, some projects have extensively researched how
to manage computational activities [Bardram, 07] and how
to infer activities [Tentori, 08]; little has been said
regarding their representation. Indeed, there are still open
challenges including the identification of the characteristics
of human activities, how these characteristics could be
matched to its computational representation and how this
representation could be tailored for different applications
domains.
Building from in-depth fieldwork and the development
of several activity-aware applications, we developed the
CARe ontology as an approach to cope with the
abovementioned problems. To reach this end, we carried
out two qualitative studies following an activity-centered
Related Work
Several research works held that the foundations of
Activity Theory could be used to produce context
classification systems. For instance, [Cassens, 06] stated
that contextual knowledge can be categorized on personal
context (information about the subjects), task context (the
objects towards which activities are directed and the rules),
spatio-temporal context (the community), environmental
context (mediating artifacts) and social context (division of
labour). Similarly, [Kaenampornpan,04] identified from
the Activity Theory foundations, the key elements and
their relationships that should be modeled; additionally,
they argument that time is a crucial element that should be
modeled to enable the creation of activity histories in order
to enable context-aware applications to predict future
user’s actions.
Other works have focus on providing development
architectures that incorporate efficient mechanisms to
model context by introducing two-layered ontologies to
enable context-aware applications to access and interpret
context information [Gu, 05] [Bochini,07] [Soldatos, 07].
Compared to these ontologies that were designed to
Copyright © 2011, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
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represent the general concepts that characterize users,
network, devices and applications of a ubiquitous
computing environment, the CARe ontology was designed
to represent the context of the activity in which the user is
engaged, which is supported by a computational unit
designated as e-Activity.
providing supervisors with awareness of nurses’ activities:
“The nurse Letty begins her shift by following the
scheduled tasks in her activity-aware assistant (Figure 1a).
After a few hours, while Letty is finishing administering
medicines, the oncologist, Dr. Guillen, arrives to examine
the patient in room 226. Letty’s activity-aware assistant
suggests her to join Dr. Guillen. Letty suspends her current
activity and moves to room 226 to help Dr. Guillen with
the medulla extraction. Once they start the surgical
procedure, the activity-aware assistant in Letty’s phone
automatically reschedules her remaining tasks (Figure 1b).
After a while, the system notifies the head nurse, Carmen,
about Letty’s suspended activity. Carmen consults Letty’s
activity timeline (Figure 1c) and decides to assign the
suspended task to an available nurse”. The smart
application in this scenario reacts and adapts its behavior
based on the computational representation of Letty and Dr.
Guillen’s activities.
Qualitative Studies
In the next sections we present the results of two
qualitative studies that led to the design of activity-aware
application for healthcare.
The Routines of Daily Hospital Care
We analyzed data captured during a nine-months
workplace study to understand activities’ hierarchy,
characteristics, and execution model [Tentori,11].
Specifically, we looked into nursing activities, actions, and
operations. We identified six actions that nurses executed
on a daily basis as activities of daily care (ADCs) (Figure
1). For instance, a nurse begins her shift at 7:00 a.m. by
taking her patients’ vital signs. Then, she administers
medicines and monitors patients’ diets. Around 10:00 a.m.,
the nurse calculates the patients’ liquid balance by
weighting each patient’s urine bag and measuring the
liquid output. She also cleans and bathes patients.
Meanwhile, the physicians start their rounds in the ward,
and the nurse makes sure the patients’ information is
available for clinical case assessment. When the rounds
conclude, the nurse completes the ADCs by carrying out
the physicians’ new instructions. This scenario illustrates
how the ADCs are a central part of the patients’ clinical
care and are executed as a routine. Supervisors closely
monitor these routines, that we called the routines of daily
care (RDC) for managing the work rhythms in hospitals.
Understanding the Elders’ Medication Routines
Ambient assited living environments aimed at allowing
elders to “age in place”. These environments help elders to
carry out the activities of daily living, such as managing
their medication –a critical activity for independent living.
To identify the kind of support that the elderly may need
for medicating in their homes, we carried out a qualitative
study following the activity-centered design approach,
which allowed us to understand and model how elders
perform these activities [García-Vázquez et al, 10]. It
consisted of 40-minute semi-structured and contextual
interviews based on the MedMaIDE index, which is an
instrument to determine the deficiencies of elders to adhere
to their medication prescription [Orwing, 96]. The
participants were 17 elders ranging in age from 63 to 86
years old, who were able to live independently in their
homes. The study results show that elders have created
their own strategies for adhere to their medication
prescription, such as having a specific place to medicate,
maintaining notes for indicating the purpose of taking the
medicines and visiting periodically their doctor for refilling
their medicines. Modeling elders’ medication management
routines enabled us to identify and represent how elders
carry out actions using supporting resources that help them
to accomplish their medication routines, including the
community that helps them to refill their medications, and
the objects or tools used to take their medicines. Thus, we
identified the actions sequence followed by elders when it
is time for medicating. For instance, elders move to the
place where they usually medicate, such as the kitchen;
then they start to perform different actions by interacting
with the objects or tools they need to medicate such as
medicine container. We also identified that elders need
help from their children to periodically visit their doctor
Figure 1 The RDC Compass System (a) A nurse’s list of
pending tasks (b) The timeline view of nurses’ executed
activities and (c) A nurse’s routing visualization plot
Our results helped us to envision scenarios that show
how activity-aware computing could augment smart
hospital environments. For instance, in the following
scenario, we illustrate how scheduling and reminder
services aware of nurses’ activities can help them to
perform their activities of daily care (ADC) while
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and get their medicines. The understanding we gained from
the elders’ medication routines, helped us to envision
scenarios which illustrates how activity-aware computing
could augment the elders’ home environments to assist
them: “Mrs. Maria is 72 years old. It is noon and while
she is preparing her meals, her wearable notification
display generates an audio notification to make her aware
that it is time to take her medicines (figure 2a). When
Maria approaches her medicine dispenser the e-Activity in
her wearable display moves to the dispenser to emphasize
the two containers with the medicines that she need to take
(figure 2b)”.
then decide to interrupt his activity, which will cause the eActivity to move to a suspended state, such as the nurse
Letty suspends her current activity to help Dr. Guillen. As
a consequence, a user may also resume an e-Activity when
going back and forth from one activity to another
[Bardram, 07]. The main elements of these models are: the
activity executed by the user, its attributes and execution
context.
The CARe Ontology
The Context Aware Representation of e-Activities (Figure
3), CARe was implemented as an ontology that represents:
Executed Activity. As described in scenario 2, an
Activity is composed of Actions or sub-Activities; for
example, when the elder is going to perform her
medication routine, she first goes to her medicine
dispenser, selects the appropriate doses, and then takes a
glass of water to take her pills. Additionally, an activity
can be linked to other activities, either sequentially or
given a pre-specified order ―i.e., when a nurse gives
medication to a patient following a physician’s diagnostic
plan.
Execution context. The target device that allocates an eActivity determines its execution context. These devices
may be objects available in the environment which could
be categorized as: Basic Objects, if they are indispensable
to execute the activity, i.e. the pills containers and the
medical card used to schedule the next visit to the doctor,
and as Complementary Objects which enhance the
execution of an activity, such as the elder using a bracelet
to receive timely reminders for taking her medicines. The
CARe ontology represents the objects users employ when
executing an activity. This enables activity-aware
applications to determine the activity being executed and
decide what e-Activity would support and where it should
run (i.e. medicines containers execute e-Activity to aid
elders to take their pills in order to prevent them from
taking inappropriate doses). Thus, an e-Activity, allocated
in an object, has an execution State indicating if it is active,
suspended, resumed, or has moved.
Activity Attributes and Properties. From our studies we
identified that activities are characterized by the following
Attributes: the Person who executes the activity, the
StartTime and FinishTime of the activity and the Location
where the activity is being executed. An activity may have
properties, such as the Frequency of executing it, the
Schedule followed, and Community, which indicates that
other persons participated in the activity. Developers can
customize the CARe ontology by adding domain-centric
properties, which are particular characteristics of a specific
application domain. Attributes and Properties are used to
determine if a CARe Rule has been met.
a)
b)
Figure 2. Activity-aware system providing aids for
medicating on different objects: a) Mobile medication
reminder; b) Medicine container providing ambient aids.
e-Activity: a Computational Representation of
Human Activities
In the aforementioned application scenarios, an e-Activity
is the computational representation of the user’s activity,
and it is defined by its attributes, its execution model and
behavior [Tentori, 11]. The attributes of an e-Activity are
essential characteristics that determine its uniqueness and
depict its execution context. Such attributes include user
and environmental contextual information and the rules
that define how the environment expects users to act when
some contextual conditions are met. The e-Activity
attributes include contextual information regarding who is
performing the action, the location in which it is being
executed, and the used objects or artifacts. Thus, an eactivity follows an execution model according to the way
users execute their activities in real life. For example, to
enable an elder to independently perform their medication
activity, an e-Activity is aware of her context to
autonomously activate an action plan composed by a set of
actions or operations that illustrate how such activity must
be executed, i.e the e-Activity jumps to the dispenser to
provide ambient aids that indicate what medicines to take.
An e-Activity may have different states according to their
execution model [Bardram, 07]. For instance, a user
perceives interruptions or changes in the environment, and
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Figura 3. CARe-Ontology
Agent-based Architecture for Developing Activity-Aware
Systems for Assisting Elderly. J. UCS 16(12):1500-1520.
Gu, T.; Keng-Punga, K.; and Da-Qing, Z.; 2005. A serviceoriented middleware for building context-aware services. J. of
Network and Computer Applications 28: 1–18.
Rodríguez, M. D.; Favela, J.; Preciado, A.; and Vizcaino, A.,
2005. Agent-based ambient intelligence for healthcare. AI
Communications 18(3):201-216.
Tentori, M.; and Favela, J. 2008. Activity-aware computing in
Healthcare. IEEE Intelligent Systems 7 (2): 51-57
Tentori, M.; Rodríguez, M .D.; and Favela, J. (in press). An
Agent-based middleware for the design of activity-aware
applications. IEEE Intelligent Systems
Cassens, J.; and Kofod-Petersen, A. 2006. Using Activity Theory
to Model Context Awareness: a Qualitative Case Study. Proc. of
the 19th International Florida Artificial Intelligence Research
Society Conference, 619-624: AAAI Press,
Kaenampornpan, M., O'Neill, E., 2004. Modelling Context: An
Activity Theory Approach. Proceedings of Ambient Intelligence:
Second European Simposium, EUSAI, Ed. Springer, pp. 367-374.
Orwing, D.; Brandt, N.; and Gruber-Baldini, A. 2006. Medication
management assessment for older adults in the community, The
Gerontologist, 2: 661-668.
Soldatos, J.; Dimakis, N.; Stamatis, K.; and Polymenakos, L.
2007. A breadboard architecture for pervasive context-aware
services in smart spaces: middleware components and prototype
applications. Personal & Ubiquitous Computing (1):193–212.
The CARe ontology was implemented with the OWL
language enabling applications that were not developed on
the top of SALSA to access it. For instance, an Electronic
Health Record System may be updated based on the CARe
ontology content.
Conclusions and Future Work
Developing representational models for domain specific
environments may facilitate implementing activity-aware
applications that not only provide opportunistic services
and information, but that learn from and adapt to users
behaviors changes. Considering this, we plan to extend the
proposed activity-aware mechanisms to determine how the
CARe ontology facilitates the identification of users
behavior patterns from their activities histories. This will
facilitate the development of activity-aware applications
that infer risks associated with an activity (i.e an elder
taking a long nap) or to adapt them to new adopted
behaviors and routines changed by the doctor.
References
Bardram, J.; and H.B. Christensen, 2007. Pervasive Computing
Support for Hospitals: An Overview of the Activity-Based
Computing Project. IEEE Pervasive Computing 6(1): 44–51.
Bochini, C.; Curino, C.; and Quintarelli, E. 2007. A data-oriented
survey of context models. ACM SIGMOD Record 36(4):19-26.
García-Vázquez, J.; Rodríguez, M.D.; Tentori, M.; SaldañaJimenez, D.; Andrade, A.G.; and Espinoza, A.N., 2010. An
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