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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 on theme: "Basim Majeed, Ben Azvine, Steve Brown (BT) Trevor Martin (University of Bristol) Intelligent Systems for Telecare."— Presentation transcript:

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

2 Presentation Outline Introduction Centre for Care in the Community Well-being Monitoring Intelligent Data Analysis Questions

3 Introduction Effects of the demographic shift Year Ratio Persons Aged to UK Long Term Healthcare Cost (£B) Support Ratio 1 UK Long Term Healthcare Cost 2 1.Office for National Statistics, Royal Commission Report into Long Term Care, 1999.

4 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).

5 Centre for Care in the Community Three Research Teams Domain Specific Modelling Sensor Network Intelligent Data Analysis DTI/BT Funded

6 Well-being monitoring Identifying the activities

7 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

8 Core activity monitoring set for the physically frail priority group Leaving & returning home (Social interaction) Visitors (Social interaction) Preparing food & eating appropriately (*ADLs) Sleeping (*ADLs) Leisure activities (Personal goals) Personal appearance (Personal goals) *Activities of Daily Living

9 Client home Kitchen Lounge cupboard Back door TV Coffee table Radio chair Fireplace Fridge/ freezer RMU Gas Oven/hob Sofa & armchairs Window sill sink drainer TV chair Spare bedroom Master bedroom Bath W.C. Basin Bathroom Landing Double bed Wardrobes Draws Pile of various objects

10 Intelligent Data Analysis Converting sensor data into Activity information

11 Target Group Well-being concepts Sleep monitoring Visitor detection ……. Domain Knowledge Provide Answers to Core Activity Questions Data from a Sensor Network System Overview

12 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

13 System Overview Interface to Sensor Network User Interface Sensor Object Management Analysis Algorithms Raw Data Pre-processing Pre-Processed DataReport Data Sensor Information Report Generator

14 Raw Data: Contains logical inconsistencies Subject to intermittent errors Huge amount of events captured Decision making needs abstract data: when, where and what

15 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 check Interface to Sensor Network User Interface Sensor Object Management Analysis Algorithms Raw Data Pre-processing Pre-Processed DataReport Data Sensor Information Report Generator

16 Pre-processing L evel - II Interface to Sensor Network User Interface Sensor Object Management Analysis Algorithms Raw Data Pre-processing Pre-Processed DataReport Data 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

17 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 Sensor Network User Interface Sensor Object Management Analysis Algorithms Raw Data Pre-processing Pre-Processed DataReport Data Sensor Information Report Generator

18 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

19 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 Visitor Activity

20 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 ChangesNon-Adjacent Changes Non-Adjacent Bursts Fuzzy rules are used to accumulate the evidence of a visit Visitor Activity

21 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 Q1Q1 Q3Q3 Q1Q1 Q1Q1 Q1Q1 Q3Q3 Q3Q3 Q3Q RiseSteadyBig Fall Fall Act. Level Change AABCDEFCDEF Activity Level Change 1 Big RiseRiseSteadyBig Fall Fall Activity Level Change 1 RiseSteadyBig Fall Fall Activity Level Change Initial Sets (Equally Sized) Negative and Positive ChangesCombined Sets (Equal Data Share) Final Sets (Averaged) Visitor Activity

22 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 individuals 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 Visitor Activity

23 User GUI

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25 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 DB Thin client High Level Application objects over HTTP

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