This work presents the Mining Minds Context Ontology, an
ontology for the identification of human behavior. This ontology comprehensively models high-level context based on low-level information, including the user activities, locations, and emotions. The Mining Minds Context Ontology is the means to infer high-level context from the low-level information. High-level contexts can be inferred from unclassified contexts by reasoning on the Mining Minds Context Ontology. The Mining Minds Context Ontology is shown to be flexible enough to operate in real life scenarios in which emotion recognition systems may not always be available. Furthermore, it is demonstrated that the activity and the location might not be enough to detect some of the high-level contexts, and that the emotion enables a more accurate high-level context identification. This work paves the path for the future implementation of the high-level context recognition system in the Mining Minds project.
High-Level Context Inference for Human Behavior Identication
1. Claudia Villalonga, Oresti Banos,
Wahajat Ali, Taqdir Ali, Asif Rassaq,
Sungyong Lee, Hector Pomares, Ignacio Rojas
International Work-conference on
Ambient Assisted Living an Active
Ageing (IWAAL 2015), Patagonia,
Chile, December 1-4, (2015)
High-Level Context Inference for
Human Behavior Identification
2. /“The Slow-Moving Public Health Disaster”
Diseases linked to lifestyle choices are
currently the biggest cause of death
worldwide:
• Cardiovascular conditions, cancers, chronic respiratory
disorders, obesity and diabetes, represent more than 60% of
global deceases, half of which are of premature nature
• Most of these diseases are fairly associated to common risk
factors, namely, tobacco and alcohol use, unwholesome diet
and physical inactivity
• This "lifestyle disease" epidemic causes a much greater
public health threat than any other epidemic known to man
• Millions of lives could be saved if the world over the
next decade invests $1-3 per person on promoting
healthier habits
Global targets for prevention and control of
“lifestyle diseases” to be attained by 2025
Source: WHO, “Global status report on noncommunicable diseases 2014,” World Health Organization, Tech. Rep., 2014.
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3. /Mining Minds in a nutshell
“Collection of innovative
services, tools, and
techniques, working
collaboratively to
investigate on human's
daily-life routines data
generated from
heterogeneous resources,
for personalized wellbeing
and healthcare support”
3
6. /Context Ontology: High-Level Context Classes Definition 6
Activity and Location
(Emotion is not required)
Activity, Location and
Emotion (if available)
Activity, Location and
Emotion (mandatory)
None of the other Contexts
and sedentary Activity
7. /Context Ontology: Examples of High-Level Context Instances 7
Activity, Location and
Emotion
Activity, Location and
Emotion
Activity and Location,
without Emotion
Activity and Location,
without Emotion
8. /Context Ontology: Examples of High-Level Context Instances 8
Activity, Location and
Emotion
Activity and Location,
without Emotion
16. /Conclusions
• Design and implementation of a framework for the online identification of
high-level context based on low-level information (activities, locations,
and emotions)
• Definition of an ontology for the comprehensive and holistic identification
of human behavior:
• activity and location information might not be enough to detect some of the high-
level contexts
• emotion enables a more accurate high-level context identification
• Flexible methodology and ontology to operate in real life scenarios in
which recognition systems may not always be available
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'Lifestyle' diseases linked to unhealthy habits kill millions of people prematurely
Context ::: class
Sitting ::: subclass (these are disjoint)
hasActivity ::: object property
hasStartTime ::: data property
Literal (string), and concretely the time has the W3C standard format XML schema
These are our defined classes (only defined classes can be used for the classification). HLC classes are “defined” (i.e., both necessary, “e.g., hasUser some User”, and sufficient conditions, “e.g., hasActivity some Sitting”) while LLC classes are simply “described” (i.e., only necessary conditions, “e.g., hasUser some User”).
Due to the “open world assumption”: you cannot assume that something does not exist if you do not explicitly state that it does not exist
Some – existencial restriction ::: it must exist
Only – universal restriction ::: if it exists, it can only be of the given type (e.g., the property (“hasActivity”) can only relate to an instance which is a member of class “Sitting” and not at the same link to another instance of type “Walking”)
Once we have defined our ontology, how can we use it? The idea is to create an instance of this context whenever a new context is experienced by the user. What we do is to set in this instance the low-level contexts that are taking place. In this case for example the emotions… By applying reasoning techniques we can identify the high-level context, in this case Amusement.
Instance of the “Context” class, i.e., parent class.
(Left) Type assertions | (Right) Property assertions
Assertion of the values of the properties. E.g., hasActivity act_sitting (where act_sitting is an instance of the LLC class Sitting)
Due to the Open World Assumption, type assertions are used as closure axioms to indicate something does not exist. E.g., the value of the “hasActivity” property is ONLY Sitting. Or the emotion does not exist (not (hasEmotion some Emotion))