Project name

Claudia Villalonga, Oresti Banos, Wahajat Ali, Taqdir Ali, Asif Rassaq, Sungyong Lee, Hector Pomares, Ignacio RojasInternational 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

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The Slow-Moving Public Health DisasterDiseases 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 natureMost of these diseases are fairly associated to common risk factors, namely, tobacco and alcohol use, unwholesome diet and physical inactivityThis "lifestyle disease" epidemic causes a much greater public health threat than any other epidemic known to manMillions 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 2025Source: WHO, Global status report on noncommunicable diseases 2014, World Health Organization, Tech. Rep., 2014.2

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'Lifestyle' diseases linked to unhealthy habits kill millions of people prematurely2

Mining Minds in a nutshellCollection 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

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Context Information Curation LayerHigh Level Context-AwarenessLow Level Context-AwarenessSensory Data RouterInertial ActivityRecognizerActivity UnifierAudioActivityRecognizerVideoActivityRecognizerEmotion UnifierLocation UnifierContext Ontology ManagerHigh-Level Context ReasonerHigh-Level Context BuilderPhysiological EmotionRecognizerVideo EmotionRecognizerAudio EmotionRecognizerInertial Location DetectorVideoLocation DetectorGeopositioning Location DetectorContext OntologyStorageHigh-Level Context NotifierClassificationFeature ExtractionSegmentationPreprocessingInput AdapterOutput AdapterClassificationFeature ExtractionSegmentationPreprocessingInput AdapterOutput AdapterClassificationFeature ExtractionSegmentationPreprocessingInput AdapterOutput AdapterClassificationFeature ExtractionSegmentationPreprocessingInput AdapterOutput AdapterClassificationFeature ExtractionSegmentationPreprocessingInput AdapterOutput AdapterClassificationFeature ExtractionSegmentationPreprocessingInput AdapterOutput AdapterInertial Navigation TrackingFeature ExtractionSegmentationPreprocessingInput AdapterOutput AdapterVideo TrackingFeature ExtractionSegmentationPreprocessingInput AdapterOutput AdapterGPS TrackingFeature ExtractionSegmentationPreprocessingInput AdapterOutput AdapterActivity NotifierEmotion NotifierLocation NotifierContext SynchronizerContext InstantiatorContext MapperContext VerifierContext Classifier

Context Query GeneratorContext HandlerOntology Model Manager

High Level ContextLow Level ContextMultimodal Data

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Mining Minds Context Ontology5

Context Ontology Metrics:9 High-Level Contexts16 Activities (Low-Level Context)8 Locations (Low-Level Context)8 Emotions (Low-Level Context)

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Context ::: classSitting ::: subclass (these are disjoint)hasActivity ::: object propertyhasStartTime ::: data property

Literal (string), and concretely the time has the W3C standard format XML schema

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Context Ontology: High-Level Context Classes Definition6

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

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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 existOnly 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)

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Context Ontology: Examples of High-Level Context Instances7

Activity, Location and EmotionActivity, Location and EmotionActivity and Location, without EmotionActivity and Location, without Emotion

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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)) 7

Context Ontology: Examples of High-Level Context Instances8

Activity, Location and EmotionActivity and Location, without Emotion

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High Level Context-AwarenessHLCA Operation9LLCAActivity RecognizerEmotion RecognizerLocation DetectorHigh-Level Context BuilderHigh-Level Context ReasonerHigh-Level Context NotifierContext Ontology ManagerContext OntologyStorage

Ontology Model ManagerContext Query GeneratorContext HandlerData Curation Layeract_sitting rdf:type Sitting .act_sitting hasStartTime 2015-08-10T11:05:30^^dateTime .act_sitting isContextOf user9876 . loc_office rdf:type Office .loc_office hasStartTime 2015-08-10T11:04:55^^dateTime .loc_office isContextOf user9876 . ctx rdf:type Context .ctx hasActivity act_sitting .ctx hasLocation loc_office .ctx hasEmotion emo_boredom .ctx isContextOf user9876 .

ctx hasStartTime 2015-08-10T11:05:30^^dateTime .ctx rdf:type hasActivity only ({act_sitting}) .ctx rdf:type hasLocation only ({loc_office }) .ctx rdf:type hasEmotion only ({emo_boredom}) .ctx rdf:type Context .ctx rdf:type OfficeWork .ctx hasActivity act_sitting .ctx hasLocation loc_office .ctx hasEmotion emo_boredom .ctx isContextOf user9876 .

ctx hasStartTime 2015-08-10T11:05:30^^dateTime .ctx rdf:type hasActivity only ({act_sitting}) .ctx rdf:type hasLocation only ({loc_office }) .ctx rdf:type hasEmotion only ({emo_boredom}) .emo_boredom type Boredom . emo_boredom hasStartTime 2015-08-10T11:05:12^^dateTime .emo_boredom isContextOf user9876 .

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HLCA Operation1011:05:3011:08:0011:06:4511:05:5011:07:00User 9876llc_1777 Sittingllc_1780Walkingllc_1778Officellc_1779Boredomllc_2501Sittingllc_2500MallUser 5555llc_2502HappinessContext Ontology ManagerContext OntologyStorage

Ontology Model ManagerContext Query GeneratorContext Handler

Context Mapper

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HLCA Operation1111:05:3011:08:0011:06:4511:05:5011:07:00User 9876llc_1777 Sittingllc_1780Walkingllc_1778Officellc_1779Boredomllc_2501Sittingllc_2500MallUser 5555llc_2502HappinessContext SynchronizerLLC instances starting within the window: llc_1777LLC instances ending within the window: -Order chronologicallyConcurrent LLC for llc_1777: llc_1778 and llc_1779 Notify Context Instantiator1234511:05:1511:05:30

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HLCA Operation1211:05:30User 9876llc_1777 Sittingllc_1778Officellc_1779Boredomhlc_0001

Trigger LLC

Concurrent LLCUnclassified HLC11:05:30User 9876OIINPUT:

OUTPUT:Context Instantiator

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HLCA Operation13HLC Reasoner: 11:05:3011:08:0011:06:4511:05:5011:07:00User 9876llc_1777 Sittingllc_1780Walkingllc_1778Officellc_1779Boredomllc_2501Sittingllc_2500MallUser 5555llc_2502Happinesshlc_0002OfficeWorkhlc_0001OfficeWorkhlc_0101Inactivityhlc_0003Unknownhlc_0102Amusement

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HLCA Operation14HLC Notifier: 11:05:3011:08:0011:06:4511:05:5011:07:00User 9876llc_1777 Sittingllc_1780Walkingllc_1778Officellc_1779Boredomllc_2501Sittingllc_2500MallUser 5555llc_2502Happinesshlc_0002OfficeWorkhlc_0001OfficeWorkhlc_0001OfficeWorkhlc_0101Inactivityhlc_0101Inactivityhlc_0003Unknownhlc_0102Amusementhlc_0002OfficeWork

Context Ontology ManagerContext OntologyStorage

Ontology Model ManagerContext Query GeneratorContext Handler

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Demo15

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ConclusionsDesign 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 contextsemotion enables a more accurate high-level context identificationFlexible methodology and ontology to operate in real life scenarios in which recognition systems may not always be available16

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Thank you for your attention. Questions?

Claudia VillalongaUbiquitous Computing Lab (UCLab)Kyung Hee University (KHU), South KoreaEmail: cvillalonga@oslab.khu.ac.kr

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