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