Eoghan Furey, Kevin Curran, Paul Mc Kevitt Intelligent Systems Research Centre,

1. 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

2. 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

3. 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

4. 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

5. 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)

6. 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!

7. Black spots Signal strength map

8. Context of HABITS

9. Node positions in a house

10. Connected graph with node connections

11. Connected graph with node connections

12. Adjacency matrix for nodes in example house

13. Zones for recording movement history

14. 14 17 11 19 16 12 13 18 15 6 1 7 10 2 8 5 9 3 4 Zones represented as graph nodes MS First Floor MS Ground Floor

15. Initial Transition Matrix between nodes

16. 14 17 11 19 9 9 16 7 4 2 4 9.5 10.5 8 5.5 12 13 5 8 18 5 15 9.5 5 3 8 5 30 MS First Floor 5 6 1 7.5 4.5 7 10 3.5 4 3 5.5 2 8 8 5 4 2 5 2.5 9 3 4 MS Ground Floor Distance (Travel Time) between nodes

17. Toilet Kevin’s Office 14 17 Wait nodes, Transition Nodes & Exits 11 19 16 12 13 Board Room 18 15 Directors Office MS First Floor Eoghan Desk Main Exit Reception/Mail Room 6 1 7 10 Car Park Exit Smokers Exit 2 8 5 Canteen 9 3 Lecture Theatre 4 MS Ground Floor

18. HABITS operational scenario

19. Toilet Kevin’s Office 14 17 Preferred Paths – Car park to Desk 11 19 16 12 13 Board Room 18 15 Directors Office MS First Floor Eoghan Desk Main Exit Reception/Mail Room 6 1 7 10 Car Park Exit Smokers Exit 2 8 5 Canteen 9 3 Lecture Theatre 4 MS Ground Floor

20. Toilet Kevin’s Office 14 17 Preferred Paths – Desk to Kevin’s Office 11 19 16 12 13 Board Room 18 15 Directors Office MS First Floor Eoghan Desk Main Exit Reception/Mail Room 6 1 7 10 Car Park Exit Smokers Exit 2 8 5 Canteen 9 3 Lecture Theatre 4 MS Ground Floor

21. Toilet Kevin’s Office 14 17 Preferred Paths – Desk to Toilet 11 19 16 12 13 Board Room 18 15 Directors Office MS First Floor Eoghan Desk Main Exit Reception/Mail Room 6 1 7 10 Car Park Exit Smokers Exit 2 8 5 Canteen 9 3 Lecture Theatre 4 MS Ground Floor

22. Toilet Kevin’s Office 14 17 Preferred Paths – Desk to Canteen 11 19 16 12 13 Board Room 18 15 Directors Office MS First Floor Eoghan Desk Main Exit Reception/Mail Room 6 1 7 10 Car Park Exit Smokers Exit 2 8 5 Canteen 9 3 Lecture Theatre 4 MS Ground Floor

23. Toilet Kevin’s Office 14 17 Preferred Paths – Desk to Main Exit 11 19 16 12 13 Board Room 18 15 Directors Office MS First Floor Eoghan Desk Main Exit Reception/Mail Room 6 1 7 10 Car Park Exit Smokers Exit 2 8 5 Canteen 9 3 Lecture Theatre 4 MS Ground Floor

24. Long term predictions for User 1

25. 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!

26. 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.

27. Thank you for your attention. Questions/Comments