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![Page 1: Http://isrc.ulster.ac.uk Magee Campus A Bayesian Filter Approach to Modelling Human Movement Patterns for First Responders Within Indoor Locations Eoghan.](https://reader035.fdocuments.us/reader035/viewer/2022081603/56649cb85503460f9497de4b/html5/thumbnails/1.jpg)
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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
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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
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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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
Board RoomDirectors Office
Toilet
Kevin’s Office
Canteen
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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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
Board RoomDirectors Office
Toilet Kevin’s 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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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
Board RoomDirectors Office
Toilet Kevin’s 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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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
Board RoomDirectors Office
Toilet Kevin’s 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
![Page 24: Http://isrc.ulster.ac.uk Magee Campus A Bayesian Filter Approach to Modelling Human Movement Patterns for First Responders Within Indoor Locations Eoghan.](https://reader035.fdocuments.us/reader035/viewer/2022081603/56649cb85503460f9497de4b/html5/thumbnails/24.jpg)
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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