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A Bayesian Filter Approach to Modelling Human Movement Patterns for First Responders Within Indoor Locations

Eoghan Furey, Kevin Curran, Paul Mc Kevitt

Intelligent Systems Research Centre, University of Ulster Magee, Derry, Northern Ireland


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This research creates a system that enhances Wi-Fi tracking capability in an indoor environment

HABITS (History Aware Based Wi-Fi Indoor Tracking System) enables real-time continuous tracking in areas where this was not previously possible due to signal black spots

Historical movement patterns and probability will facilitate this


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Information first responders can use This system has the ability to inform first

responders of the locations of the inhabitants of a building

HABITS also gives indications of where the inhabitants are intending to go in the short (a few seconds), medium (end of the current journey) and long (later that day or week) term


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Positioning Systems Positioning is a process to obtain the spatial position

of a target Location Based Services (LBS) are required which

work in an indoor environment. Large public buildings; universities, hospitals and shopping centres

Due to the poor performance of Satellite and Cellular systems indoors, a separate system is required

802.11 Wi-Fi networks as specified by the IEEE are available in many large buildings. The signals transmitted by the Access Points (APs) provide a readily available network of signals which may be used for positioning


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Related Research• Indoor Tracking

–ActiveBadge – Olivetti Research (Ward et al., 1997)–RADAR – Microsoft Research (Bahl & Padmanabhan,

2000)–PlaceLab – Intel Research (LaMarca et al., 2005)–Ekahau (Inc, 2004) – Current market leader

• Modelling Movement patterns– Zhou (2006); Petzold et al.(2006); Song et al.(2010)


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802.11 b/g Wi-Fi Network Installation

• When designed for Data Communication– Data transfer rate– Quality of Service– Cost

• When designed for Indoor Tracking– Treble number of Access Points (AP)– AP placement in zig-zag pattern

Conflict of Interest!


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Signal strength map

Black spots


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Context of HABITS

Ekahau

enhanced

with HABITS


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Node positions in a house

Kitchen

Bathroom

Living RoomBedroom

Front Door

Kitchen

Bathroom

Living RoomBedroom

Kitchen

Bathroom

Living RoomBedroom

Front Door


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Connected graph with node connections

Kitchen

Bathroom

Living RoomBedroom

DP1

DP2

DP3

Front Door

Kitchen

Bathroom

Living RoomBedroom

DP1

DP2

DP3

Front DoorFront Door


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Connected graph with node connections

2

8

61

3

47

5

2

8

61

3

47

55


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Adjacency matrix for nodes in example house

1

0

1

0

1

0

0

0

7 8654321

00000008

11010007

00000006

00010005

00101004

00010113

00001002

00001001

1

0

1

0

1

0

0

0

7 8654321

00000008

11010007

00000006

00010005

00101004

00010113

00001002

00001001


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Zones for recording movement history


Magee Campus

Zones represented as graph nodes

MS Ground Floor

MS First Floor

1

2

3

6

4

5

78

9

10

14 17

15

1916

1813

11

12


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Initial Transition Matrix between nodes

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1 0 0.167 0 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0 0

2 0.667 0 0.077 0 0.019 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 0 0.667 0 0 0.157 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 0.314 0 0 0 0 0 0 0 0 0 0.667 0 0 0 0

5 0 0.167 0.923 0.6 0 0.571 0.2 0.045 0 0 0 0 0 0 0 0.013 0 0 0

6 0 0 0 0 0.314 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0.157 0 0 0.045 0 0 0 0 0 0 0 0 0 0 0

TO 8 0 0 0 0 0.019 0 0.8 0 1 0.071 0 0 0 0 0 0 0 0 0

9 0 0 0 0 0 0 0 0.364 0 0 0 0 0 0 0 0 0 0 0

10 0 0 0 0 0 0 0 0.545 0 0 0 0 0 0 0 0 0 0 0.43

11 0.167 0 0 0 0 0 0 0 0 0 0 0.571 0.143 0.043 0 0.013 0 0 0

12 0 0 0 0 0 0 0 0 0 0 0.125 0 0.143 0.043 0 0.053 0 0 0

13 0 0 0 0 0 0 0 0 0 0 0.125 0.143 0 0.043 0 0.053 0 0 0

14 0 0 0 0 0 0 0 0 0 0 0.125 0.143 0.143 0 0 0.053 0 0 0

15 0 0 0 0.4 0 0 0 0 0 0 0 0 0 0 0 0.267 0 0 0

16 0 0 0 0 0.019 0 0 0 0 0 0.125 0.143 0.571 0.869 0.333 0 0.16 0.556 0.015

17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.053 0 0.167 0.061

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.48 0.8 0 0.492

19 0 0 0 0 0 0 0 0 0 0.571 0 0 0 0 0 0.013 0.04 0.278 0

OUT 0.167 0.429 0.357

FROM


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Distance (Travel Time) between nodes

MS Ground Floor

MS First Floor

1

2

3

6

4

5

7

8

9

10

14 17

15

1916

1813

11

12

97

9.5

5

5.5 8

3

4

5

4 210.5

9

9.5

5

5

30

5

3.5

7.5

4

8

3

4.5

5.5

5 2.5

4

8

8

2


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Wait nodes, Transition Nodes & ExitsToilet Kevin’s Office

MS Ground Floor

MS First Floor

1

2

3

6

4

5

7

8

9

10

14 17

15

1916

1813

11

12

Eoghan Desk

Lecture Theatre

Reception/Mail Room

Board RoomDirectors Office

Canteen

Car Park Exit

Main Exit

Smokers Exit

Transition Node

Exit/Wait Node

Wait Node

Transition NodeTransition Node

Exit/Wait NodeExit/Wait Node

Wait NodeWait Node


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HABITS operational scenario

xt-2 xt-1

xt(i)

xt(j)

3 4

5

62

1

xt-3

xt-2 xt-1

xt(i)

xt(j)

3 4

5

62

1

xt-3


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MS Ground Floor

Preferred Paths – Car park to Desk

Toilet

Kevin’s Office

MS First Floor

1

2

3

6

4

5

78

9

10

14 17

15

1916

1813

11

12

Eoghan Desk

Lecture Theatre

Reception/Mail Room


Canteen

Car Park Exit

Main Exit

Smokers Exit

Transition Node

Exit/Wait Node

Wait Node



Wait NodeWait Node


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MS Ground Floor

Preferred Paths – Desk to Kevin’s Office

Car Park Exit

MS First Floor

1

2

3

6

4

5

78

9

10

14 17

15

1916

1813

11

12

Eoghan Desk

Lecture Theatre

Reception/Mail Room


Toilet

Kevin’s Office

Canteen

Main Exit

Smokers Exit

Transition Node

Exit/Wait Node

Wait Node



Wait NodeWait Node


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Preferred Paths – Desk to Toilet

MS Ground Floor

MS First Floor

1

2

3

6

4

5

7

8

9

10

14 17

15

1916

1813

11

12

Eoghan Desk

Lecture Theatre

Reception/Mail Room


Toilet Kevin’s Office

Canteen

Car Park Exit

Main Exit

Smokers Exit

Transition Node

Exit/Wait Node

Wait Node



Wait NodeWait Node


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Preferred Paths – Desk to Canteen

MS Ground Floor

MS First Floor

1

2

3

6

4

5

7

8

9

10

14 17

15

1916

1813

11

12

Eoghan Desk

Lecture Theatre

Reception/Mail Room



Canteen

Car Park Exit

Main Exit

Smokers Exit

Transition Node

Exit/Wait Node

Wait Node



Wait NodeWait Node


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Preferred Paths – Desk to Main Exit

MS Ground Floor

MS First Floor

1

2

3

6

4

5

78

9

10

14 17

15

1916

1813

11

12

Eoghan Desk

Lecture Theatre

Reception/Mail Room



Canteen

Car Park Exit

Main Exit

Smokers Exit

Transition Node

Exit/Wait Node

Wait Node



Wait NodeWait Node


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Long term predictions for User 1

Long term predictions

36

21

64

78

0

10

20

30

40

50

60

70

80

90

Observed twice Observed three or more times

Number of Times PP observed

Per

cent

age

Incorrect

Correct


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HABITS Application in Emergencies Where are the people now? Where were they going? Where will they be in the future? Knowledge of where users are likely to go also

gives knowledge of where they are Not likely to go! Potentially as useful!


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Conclusion and future work We conclude that HABITS improves on the

standard Ekahau RTLS in term of accuracy (overcoming black spots), latency (giving position fixes when Ekahau cannot), cost (less APs are required than are recommended by Ekahau) and prediction (short, medium and longer term predictions are available from HABITS). These are features that no other indoor tracking system currently provides.


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Thank you for your attention.

Questions/Comments

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