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Contract “Services To The Home”. Autonomic System. Health care part Hospital / doctor Specialized organization Remote diagnosis. Service Provider. EEG. Knowledge. Monitor. Respiratory rate. Analyze. Temperature. ECG. Autonomic computing part - PowerPoint PPT Presentation
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Division of IT Convergence Engineering
POSTECH’s U-Health Monitoring and Support Smart Home for the Elderly
Improve quality of life & reduce healthcare costs Enhance comfort and safety of elderly at home Reduce stress and burden to family members Design & testing of interface circuits with sensor elements Progress in intelligent sensing, monitoring, and semantic
decision making Synergistic application of ICT, BT and NT for health and
social well-being of humanity, especially elderly persons
Demo Smart Home at POSTECH
General Research Framework
Smoke Detection
Autonomic Computing Part
Importance & Motivation
Allow elderly to live independently and safely
Significant reduction in health-care costs Many aspects of elderly well-being and
safety can be automated Early detection of health problem early
treatment less expensive and better health
Improve chronic and geriatric care at home Bring healthcare to remote locations & in
poor countries through convergence application of ICT, NT and BT
ServiceProvider
Autonomic System
Data Network
Data Network
Healthcare Providers( Control System ) Medical Wireless
Sensors Network
Sensor
Sensor
Sensor
Monitor Monitor
Analyze Analyze
Plan Plan
Execute Execute
Knowledge
Home Gateway
NetworkedAppliances
Cellular Network
SmartHome
Service
Smart Home
Services
Contract“Services To The Home”
ContractU-HealthService
SpO2
MotionSensor
RFIDTag
Appliance Control
Heart Beats Sensor
Presence
SpO2
Sensor
Open Services Framework
KnowledgeBase
AIRules
Reasoning
Decision
Making Core
OS
Platform
Physical
Environmen
t
Sensors Actuators
Monitoring Analyze Execute
Smart Home + Humans
Plan
Smart Home Components
Health care partHospital / doctor
Specialized organization Remote diagnosis
Autonomic computing partInformation filtering /
aggregationSituation / context modeling
Intelligence reasoningDecision making
Home networking partInformation gathering
Service discoveryAppliance discovery
Sensing partActuator
Home control unit
Home automation
Smart Home
Bio sensorEnvironment
sensor
Light Path forObstacles avoidance
ECG SensorWireless
Sensor Base
Conclusions
Research Challenges
Environment discovery Addressing and routing Self-organisation and Self-healing Networks composition and mobility Virtualization
Context modeling Self -detection algorithms Self-diagnosis algorithms Intelligent sensing and monitoring Learning
Compatibility with existing techniques and healthcare models. Interdisciplinary collaboration Social/Societal implications
$$$$$
Compatibility with low-cost standardized manufacturing
Intelligent sensors & actuators design
Ultra low power design of integrated
circuits and systems Best cost-performance
reliability
Autonomic decision-making
Home Network Part
Ontology model for U-health smart home
1. Requirementsa. Semantically Rich Knowledge Base: Capture concepts and relationshipsb. Dynamically Updateable Knowledge Base: Enhance with new information during lifecyclec. Context awareness: Situation awareness in smart home & identify specific contexts.d. Support semantic reasoning: Infer new facts and update Knowledge Base.
2. SHOM (Smart Home Ontology Model)a. Using OWL (Web Ontology Language) to define classes and relations between themb. OWL-DL (Description Logic) based on SHOIN Description Logic. OWL-DL ensure
decidability c. Concepts related to Smart Home Network, Appliances, Humans.
3. Decision Makinga. Data gathering through medical and environment sensors.b. Data aggregation, fusion and filteringc. Inferred information using First-order Engine
WBAN ( Wireless Body Area Network)
Information-based sensor scheduling to improve energy efficiency and low latency
Motivation
• Specific disease Determine relevant parameters of symptoms.
• High-level information Key relations among these symptoms.
• Determine best body sensor(s) for specific parameters of symptoms.
• Quantify sensing and communication operations for diagnosis.
• Propose cooperative diagnosis models for different body sensors.
Approach
• An Information-based probabilistic relation model
• A Cost function over the energy expenditure
• A correlation model between utility gain and energy loss
Internet
PulseECGTemperature
SpO2
Accelerometer
Respiratory rate
NetworkCoordinator
ZigBee
GPRS
caregiver
Emergency
Medical Server
EEG
Hui Wang1, Hyeok-soo Choi2, Nazim Agoulmine1,3, M. Jamal Deen1,4 and James Won-Ki Hong1
1ITCE, POSTECH, Pohang, South Korea2Computer Science & Engineering, POSTECH, Pohang, South Korea
3Computer Science, University of Evry Val d’Essonne, France4ECE Department, McMaster University, Hamilton, Ontario, Canada
Algorithm System Architecture
Statistical AnalysisStatistical Analysis
Knowledge BaseKnowledge Base
Decision MakingDecision Making
Coord
inato
r
subject to
Sensor 3
Sensor 2
Sensor 1
Data
Actuators
Start Start
Initialization Initialization
Sensor selectionSensor
selection
Wait for information
Wait for information
Update knowledge
Update knowledge
FinishFinish
Knowledge good enough?
Knowledge good enough?
Yes
No
How the world evolves? context
How the world evolves? context
Situation assessmentSituation
assessment
Revise goalsRevise goals GoalsGoals
Generate/revise decision rules (SHOM)
Generate/revise decision rules (SHOM)
Sensor tasking/actio
n
Sensor tasking/actio
n
Information utility
Information utility
Compute initial
knowledge
Compute initial
knowledge
Send information
query
Send information
query