Basim Majeed, Ben Azvine, Steve Brown (BT) Trevor Martin (University of Bristol) Intelligent Systems for Telecare.

Basim Majeed, Ben Azvine, Steve Brown (BT)

Trevor Martin (University of Bristol)

Intelligent Systems for Telecare

Presentation Outline

• Introduction

• Centre for Care in the Community

• Well-being Monitoring

• Intelligent Data Analysis

• Questions

IntroductionEffects of the demographic shift

2.0

2.5

3.0

3.5

4.0

4.5

1995 2005 2015 2025 2035 2045 2055Year

Rat

io P

erso

ns

Ag

ed 1

6-64

to

65+

10

15

20

25

30

35

40

45

50

UK

Lo

ng

Ter

m H

ealt

hca

re C

ost

(£B

)

Support Ratio 1 UK Long TermHealthcare Cost 2

1. Office for National Statistics, 2002.2. Royal Commission Report into Long Term Care, 1999.

Introduction

• Intelligent Telecare provides new ways of enabling elderly or vulnerable people to maintain their independence and live in their own homes for longer.

• BT is leading a DTI sponsored group of academic partners to develop an Intelligent monitoring system for telecare.

• System uses a range of low cost sensors placed in the home to monitor a person's activity and build up a picture of their behaviour.

• Target users are care professionals within social services (initially at Liverpool).

Centre for Care in the Community

Three Research Teams

• Domain Specific Modelling

• Sensor Network

• Intelligent Data Analysis

DTI/BT Funded

Well-being monitoringIdentifying the activities

Identifying the activities to monitor

• Older population split into ‘priority groups’ identified by the DoH

• Relevant activities identified for each priority group

• A core set of activities relevant to physical, mental and social elements of well-being has been identified

• Specific questions for each activity have been identified

Core activity monitoring set for the physically frail priority group

• Leaving & returning home (Social interaction)

• Visitors (Social interaction)

• Preparing food & eating appropriately (*ADL’s)

• Sleeping (*ADL’s)

• Leisure activities (Personal goals)

• Personal appearance (Personal goals)

*Activities of Daily Living

Client home

Kitchen

Lounge

cupboard

Back door

TV

Coffeetable

‘Radio’chair

Fire

plac

e

Fridge/freezer RMU

Gas

Oven/hob

Sofa &

armchairs

Window sill

sink drainer

‘TV’’chair

Spare bedroom

Master bedroom

Bath

W.C.

Basin

BathroomLand

ing

Double bed

War

drob

es

War

drob

es

Draws

Pile of various objects

Intelligent Data AnalysisConverting sensor data into Activity information

Target Group

Well-being concepts

Sleep monitoring

Visitor detection

…….

Domain Knowledge

Provide Answers to Core Activity Questions

1 4 7

Well-Being

Time Horizon

2.00

2.50

3.00

3.50

Wak

e In

terv

als

per

Nig

ht

Data from a Sensor Network

System Overview

The Challenge• People are different, fickle, unpredictable, and unlike physical systems

that have known responses to external influences

• The challenge is to provide a system that can adapt to changes

• These characteristics are very important in well-being monitoring, more so than e.g. consumer behaviour analysis

• Cause No Harm!

• Our Approach: Assist the carer in decision making by providing an easy-to-configure system that hides the complexity of the analysis

System Overview

Inte

rfac

e to

Se

ns

or N

etw

ork

User Interface

Sensor Object Management

AnalysisAlgorithms

Raw Data Pre-processing

Pre-Processed Data ReportData

Sensor Information

Report Generator

Date Time Sensor ID Value

07-May-04 00:00:13 70 FALSE07-May-04 00:00:14 70 TRUE07-May-04 00:00:22 70 FALSE07-May-04 00:00:23 70 TRUE07-May-04 00:00:24 72 TRUE07-May-04 00:00:47 70 FALSE07-May-04 00:00:48 70 TRUE07-May-04 00:47:25 72 TRUE07-May-04 00:47:41 72 TRUE07-May-04 00:47:42 70 FALSE07-May-04 00:47:46 72 TRUE07-May-04 00:47:54 72 TRUE07-May-04 00:47:57 100 TRUE07-May-04 00:48:06 71 TRUE07-May-04 00:48:09 100 TRUE

Raw Data:• Contains logical inconsistencies

• Subject to intermittent errors

• Huge amount of events captured

• Decision making needs abstract data:

when, where and what

Pre-processing Level - I

• Grouping of similar events into blocks

• Categorising sensors into location and activity types

• Capturing additional sensor information e.g. activity levels, silence

• Abnormal toggle sensor detection

• Sensor location consistency checkIn

terface to S

enso

r Netw

ork

User Interface

Sensor Object Management

AnalysisAlgorithms

Raw Data Pre-processing

Pre-Processed Data ReportData

Sensor Information

Report Generator

Date Name SensorID Start Time Duration Comment06-May-04 BEDROOM_PIR 72 22:51:33 01:56:21 location07-May-04 BED_OCCUPANCY 70 00:00:14 00:00:08 activity_T07-May-04 BED_OCCUPANCY 70 00:00:23 00:00:24 activity_T07-May-04 BED_OCCUPANCY 70 00:00:48 00:46:54 activity_T

07-May-04 SILENCE 110 00:00:49 00:46:35BED_OCCUPANCY-

BEDROOM_PIR07-May-04 LANDING_PIR 100 00:47:57 00:00:00 location07-May-04 BATHROOM_PIR 71 00:48:06 00:00:00 location07-May-04 LANDING_PIR 100 00:48:09 00:00:00 location

Pre-processing Level - II

Interface to

Sen

sor N

etwo

rk

User Interface

Sensor Object Management

AnalysisAlgorithms

Raw Data Pre-processing

Pre-Processed Data ReportData

Sensor Information

Report Generator

• Using Fuzzy values for start time and duration of sensor event blocks

• Converts time axis into a more meaningful and manageable number of regions

• Allows reasoning with Fuzzy rules

• Duration membership functions are learnt for each sensor and each client

Bed Occupancy

Kitchen Occupancy

Analysis Techniques

• Provide answers to core activity questions

– Abstraction of sensor events into daily activities

– Identify key points in the data sequence (e.g. data silences)

– Analyse surrounding sensor data to classify activities (e.g. bed in use - asleep)

– Trend analysis

Interface to

Sen

sor N

etwo

rk

User Interface

Sensor Object Management

AnalysisAlgorithms

Raw Data Pre-processing

Pre-Processed Data ReportData

Sensor Information

Report Generator

Visitor Activity:

Challenge: No identification sensors (non-intrusive

sensing)

To infer the existence of visitors:

• Various metrics are used to describe regions of

activity

– Activity Levels

– Delay in activity level changes

– Non-adjacent rooms activity

• Regions between entrance events are then

compared

• Changes are used to accumulate evidence of visitor

activity

Activity Specific Analysis

• Use of Activity Levels for visitor evidence accumulation:

• Average level of activity between door events

• The delay after a door event before behaviours show change

• Region B clearly has a higher rate of activity

• Sliding a window W across region B shows that the change in activity level occurs at the beginning of region B

VisitorActivity

• A Changing Rooms measure comprises:

• The rate at which the location of activity changes

• The proportion of changes between non-adjacent rooms

• The longest sequence of consecutive non-adjacent changes

All Room Changes Non-Adjacent Changes

Non-Adjacent Bursts

• Fuzzy rules are used to accumulate the evidence of a visit

VisitorActivity

Training to obtain fuzzy membership functions

• At each door event in the training set the changes in activity level are recorded

• The results are split into ordered negative and positive changes

• Each half is split into three, using their 1st & 3rd quartiles to define fuzzy sets

• The fuzzy sets of each half are combined, then averaged with initial sets

Big Rise

Big Rise

Q1Q3Q1 Q1 Q1Q3 Q3 Q3

0 0 -1 1 1 -1

RiseSteadyBig Fall Fall

Act. Level Change Act. Level Change

A AB C D EF CD E F

Activity Level Change

1 -1

Big RiseRiseSteadyBig Fall Fall

Activity Level Change 1 -1

RiseSteadyBig Fall Fall

Activity Level Change

Initial Sets (Equally Sized)

Negative and Positive Changes Combined Sets (Equal Data Share)

Final Sets (Averaged)

VisitorActivity

Important Notes:

• Data splits are {A: 40%, B: 40%, C: 20%}, {D: 20%, E: 40%, F: 40%}

• Equally sized sets are not sensitive to an individual’s data spread

• Sets using equal data can be over-sensitive when data spread is uneven

• Averaged sets used are a compromise, avoiding both problems

• The same style of training is applied to the changing rooms measure

VisitorActivity

User GUI

User GUI

Deployment

Client- Web browser based - Thin client- Multi-user- Pure HTTP/HTTPS communication 

Back End- Extensive calculation on server  - SQL database driven

SQL

App. Server

DBThin client

High Level Application objects over HTTP