Magee Campus
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
http://isrc.ulster.ac.uk
Magee Campus
 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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Magee Campus
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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Magee Campus
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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Magee Campus
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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Magee Campus
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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Magee Campus
Signal strength map
Black spots
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Magee Campus
Context of HABITS
Ekahau
enhanced
with
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HABITS
Magee Campus
Node positions in a house
Bedroom
Living Room
Kitchen
Bathroom
Front Door
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Magee Campus
Connected graph with node connections
Bedroom
Living Room
DP1
DP3
Kitchen
DP2
Bathroom
Front Door
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Magee Campus
Connected graph with node connections
1
6
3
7
4
2
8
5
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Magee Campus
Adjacency matrix for nodes in
example house
1
2
3
4
5
6
7
8
1
0
0
1
0
0
0
0
0
2
3
4
5
6
7
8
0
1
0
0
0
0
0
0
1
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
1
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
1
0
1
0
0
0
0
0
1
0
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Magee Campus
Zones for recording movement history
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Magee Campus
Zones represented as graph nodes
14
17
11
19
16
12
13
18
15
MS First Floor
6
1
2
5
3
MS Ground
Floor
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7
10
8
9
4
Magee Campus
Initial Transition Matrix between nodes
FROM
TO
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
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
OUT
0
0
0
0
0
0
0
0
0
0.571
0
0
0
0
0
0.013
0.04
0.278
0
0.167
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0.429
0.357
Magee Campus
Distance (Travel Time) between nodes
14
9
7
11
8
5.
5
5
9.
5
8
12
8
13
17
4
4
5
9.
5
3
15
MS First Floor
1
2
18
5
3.
5
5.
5
2
3
MS Ground Floor
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5
30
6
4.
5
19
10.5
16
5
9
2
7
4
5
5
4
7.
5
8
2.
5
10
8
3
4
9
Magee Campus
Wait nodes, Transition Nodes & Exits
Toile
t
Kevin’s Office
14
17
11
19
16
Board Room
12
13
Directors Office
18
15
Eoghan Desk
MS First Floor
Main Exit
6
1
Smokers
Exit
Reception/Mail Room
7
2
10
Car Park Exit
8
5
9
Canteen
3
MS Ground Floor
4
Lecture Theatre
Wait Node
Transition Node
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Exit/Wait Node
Magee Campus
HABITS operational scenario
xt-3
xt(i)
1
xt-2
xt-1
2
3
4
5
6
xt(j)
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Magee Campus
Preferred Paths – Car park to Desk
Toile
t
Kevin’s Office
14
17
11
19
16
Board Room
12
13
Directors
Office
18
15
Eoghan Desk
MS First Floor
Main Exit
6
1
Smokers
Exit
2
7
5
MS Ground Floor
10
8
Car Park Exit
9
Canteen
3
Reception/Mail
Room
4
Lecture
Theatre
Wait Node
Transition Node
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Exit/Wait Node
Magee Campus
Preferred Paths – Desk to Kevin’s Office
Toile
t
Kevin’s Office
14
17
11
19
16
12
Board Room
13
Directors
Office
18
15
Eoghan Desk
MS First Floor
Main Exit
6
1
Smokers
Exit
2
10
8
9
Canteen
MS Ground Floor
Car Park Exit
7
5
3
Reception/Mail
Room
4
Lecture
Theatre
Wait Node
Transition Node
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Exit/Wait Node
Magee Campus
Preferred Paths – Desk to Toilet
Toilet
Kevin’s Office
14
17
11
19
16
Board Room
12
13
Directors Office
18
15
MS First Floor
Eoghan Desk
Main Exit
1
Smokers
Exit
Reception/Mail Room
6
7
2
10
Car Park Exit
8
5
9
Canteen
3
MS Ground Floor
4
Lecture Theatre
Wait Node
Transition Node
http://isrc.ulster.ac.uk
Exit/Wait Node
Magee Campus
Preferred Paths – Desk to Canteen
Toile
t
Kevin’s Office
14
17
11
19
16
Board Room
12
13
Directors
Office
18
15
MS First Floor
Eoghan Desk
Main Exit
6
1
Smokers
Exit
Reception/Mail Room
7
2
10
Car Park Exit
8
5
Canteen
3
MS Ground Floor
9
4
Lecture
Theatre
Wait Node
Transition Node
http://isrc.ulster.ac.uk
Exit/Wait Node
Magee Campus
Preferred Paths – Desk to Main Exit
Toilet
Kevin’s Office
14
17
11
19
16
12
Board Room
13
Directors
Office
18
15
MS First Floor
Eoghan Desk
Main Exit
6
1
Smokers
Exit
2
7
5
Canteen
3
MS Ground
Floor
Reception/Mail
Room
10 Car Park Exit
8
9
4
Lecture
Theatre
Wait Node
Transition Node
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Exit/Wait Node
Magee Campus
Long term predictions for User 1
Long term predictions
90
78
80
70
64
Percentage
60
50
40
Incorrect
36
Correct
30
21
20
10
0
Observed twice
Observed three or more times
Number of Times PP observed
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Magee Campus
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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Magee Campus
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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