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Basim Majeed, Ben Azvine, Steve Brown (BT)
Trevor Martin (University of Bristol)
Intelligent Systems for Telecare
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Presentation Outline
• Introduction
• Centre for Care in the Community
• Well-being Monitoring
• Intelligent Data Analysis
• Questions
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IntroductionEffects of the demographic shift
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1995 2005 2015 2025 2035 2045 2055Year
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UK
Lo
ng
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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.
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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).
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Centre for Care in the Community
Three Research Teams
• Domain Specific Modelling
• Sensor Network
• Intelligent Data Analysis
DTI/BT Funded
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Well-being monitoringIdentifying the activities
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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
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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
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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
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Intelligent Data AnalysisConverting sensor data into Activity information
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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
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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
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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
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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
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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
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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
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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
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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
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• 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
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• 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
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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
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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
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User GUI
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User GUI
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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
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