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Magee Campus A Bayesian Filter Approach to Modelling Human Movement Patterns for First Responders Within Indoor Locations Eoghan.

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Presentation on theme: "Magee Campus A Bayesian Filter Approach to Modelling Human Movement Patterns for First Responders Within Indoor Locations Eoghan."— Presentation transcript:

1 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

2 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

3 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

4 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  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 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)

6 Magee Campus Predictive Localisation Research Researcher/ Company CategoryTechnologyTechnique Accuracy Reported Constraints/ Comments Simmons et al. (2006) Road trafficGPSHMM99% Only 1 option 95% of the time Krumm (2008)Road trafficGPSMarkov model98% Requires a high order Markov model Inrix (2011)Road TrafficGPSUnknown Crowd Sourcing Ashbrook and Starner (2002) Pedestrian Outdoor GPSMarkov model70-80% Small number of nodes Sang-Jun (2004) Pedestrian Outdoor GPSSOMAcceptableNo figures available Sense Networks (2011) Pedestrian Outdoor GPS/Mobile phone Minimum Volume Embedding (MVE)/Bayesian out trees UnknownCrowd Sourcing Gonzales et al. (2008) PedestrianMobile phoneStatistical AnalysisN/A Only looked for repeating patterns Bahl and Padmanabhan (2000) Pedestrian Indoor Wi-Fi Viterbi and Nearest Neighbour 2.4 m improvement For immediate location estimate only Vintan et al. (2004) Pedestrian Indoor RFIDNeural Network 92% (pre training) Journeys from base node not considered Petzold et al. (2006) Pedestrian Indoor RFID Branch prediction techniques 59% (without) and 98% (with pre training Journeys from base node not considered Gellert & Vintan (2006) Pedestrian Indoor RFIDHMM84% Journeys from base node not considered Path Intelligence (2011) Pedestrian Indoor Mobile phoneUnknown Anonymous data

7 Magee Campus 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!

8 Magee Campus Need 1 for HABITS  With only three APs at one side a number of areas remain outside of the good coverage zones. In these other areas, standard RTLS will lose their accuracy

9 Magee Campus Need 2 for HABITS  With the addition of extra infrastructure (two more APs on the other side of the corridor), the majority of the corridor space is now covered and position fixes are available.

10 Magee Campus Need 3 for HABITS  The application of HABITS enables position fixes to be got in areas that where previously signal black spots without the addition of extra access points

11 Magee Campus Ekahau RTLS

12 Magee Campus What is RTLS?  Real-Time Location System for tracking high-value assets and people Deployed in hospitals, mines, retail, car parks, warehouses... Find assets and people Improve safety (alarms) Optimize workflow (reports) Prevent theft  Ekahau RTLS uses your Wi-Fi network for tracking No additional infrastructure required  Battery powered Wi-Fi tags, and other Wi- Fi devices such as VoIP phones, laptops and PDAs are used for tracking Various tag models available Ekahau Positioning Client software available to make standard Wi-Fi devices trackable

13 Magee Campus RSSI Fingerprinting  Active measurements of all RSSI data from each access point is taken via WLAN Survey.  RF characteristics such as multi-path, etc. are recorded and do not harm signal measurement or location accuracy.  Measurement of AP signal values is relatively easy, however, all important areas where tracking is to occur must be physically surveyed.

14 Magee Campus Tags tracked by the Ekahau RTLS

15 Magee Campus Signal strength map Black spots

16 Magee Campus Context of HABITS

17 Magee Campus Node positions in a house

18 Magee Campus Connected graph with node connections

19 Magee Campus Connected graph with node connections

20 Magee Campus Adjacency matrix for nodes in example house

21 Magee Campus Zones for recording movement history

22 Magee Campus Zones represented as graph nodes MS Ground Floor MS First Floor

23 Magee Campus Initial Transition Matrix between nodes

24 Magee Campus Distance (Travel Time) between nodes MS Ground Floor MS First Floor

25 Magee Campus Wait nodes, Transition Nodes & Exits Toile t Kevin’s Office MS Ground Floor MS First Floor Eoghan Desk Lecture Theatre Reception/Mail Room Board Room Directors Office Canteen Car Park Exit Main Exit Smokers Exit

26 Magee Campus Bayesian Filtering  Bayes filter is commonly used in robotics as a method to infer the position of a robot.  This recursive algorithm enables a position estimate to be continuously updated by including the most recent sensor readings.

27 Magee Campus

28 Magee Campus Bayesian Filter Components

29 Magee Campus HABITS operational scenario

30 Magee Campus MS Ground Floor Preferred Paths – Car park to Desk Toile t Kevin’s Office MS First Floor Eoghan Desk Lecture Theatre Reception/Mail Room Board Room Directors Office Canteen Car Park Exit Main Exit Smokers Exit

31 Magee Campus MS Ground Floor Preferred Paths – Desk to Kevin’s Office Car Park Exit MS First Floor Eoghan Desk Lecture Theatre Reception/Mail Room Board Room Directors Office Toile t Kevin’s Office Canteen Main Exit Smokers Exit

32 Magee Campus Preferred Paths – Desk to Toilet MS Ground Floor MS First Floor Eoghan Desk Lecture Theatre Reception/Mail Room Board Room Directors Office Toilet Kevin’s Office Canteen Car Park Exit Main Exit Smokers Exit

33 Magee Campus Preferred Paths – Desk to Canteen MS Ground Floor MS First Floor Eoghan Desk Lecture Theatre Reception/Mail Room Board Room Directors Office Toile t Kevin’s Office Canteen Car Park Exit Main Exit Smokers Exit

34 Magee Campus Preferred Paths – Desk to Main Exit MS Ground Floor MS First Floor Eoghan Desk Lecture Theatre Reception/Mail Room Board Room Directors Office ToiletKevin’s Office Canteen Car Park Exit Main Exit Smokers Exit

35 Magee Campus

36 Magee Campus

37 Magee Campus HABITS Conceptual Model

38 Magee Campus HABITS in Operation

39 Magee Campus Sample scenario – HABITS System 1.If tag = Eoghan 2.node = 5 and previous node = 4 3.node 5 NOT = wait node 4.Action = calc next node, 5.Next node = Either 2,3,6,7,8 (All have non-zero Probability) 6.Check time period = Lunch 7.If time = Lunch THEN next node is 6 or 3(Probability > 80% )- lunch temporal rule 8.Check other users in area 9.If with John THEN next node = 6(John doesn’t go to the canteen!) – other user rule 10.If with Mary THEN next node = 3(Mary usually goes to the canteen!) 11.If alone then next node = 6(40%) OR 3(40%) – wait for more info! 12.Use speed and distance to cal position at time t 13.Calc and show positions at t+1, t+2..t+n

40 Magee Campus MS Ground Floor MS First Floor First update after stationary period Wait Ekahau E1 p1=0.27 p7=0.01 p6=0.05 p5=0.05 p4=0.01 p3=0.05 p2=0.05 p8=0.01

41 Magee Campus MS Ground Floor MS First Floor Second update – in motion Wait Ekahau E1 E2 p=0.6

42 Magee Campus MS Ground Floor MS First Floor Third Update – many options Wait Ekahau E1 E3 E2 P5 P4 P3 P2 P1

43 Magee Campus MS Ground Floor MS First Floor No clear next node just from past movement Wait Ekahau E1 E3 E2 p5=0.02 p4=0.15 p3=0.31 p2=0.02 p1=0.17

44 Magee Campus MS Ground Floor MS First Floor Check time period = Lunch, probability changes, 2 nodes much higher Wait Ekahau E1 E3 E2 p5=0.02 p4=0.10 p3=0.45 p2=0.02 p1=0.36

45 Magee Campus MS Ground Floor MS First Floor Check other users in area, John doesn’t go to canteen so node 6 is most probable Wait Ekahau E1 E3 E2 p5=0.02 p4=0.10 p3=0.8 p2=0.02 p1=0.06 John

46 Magee Campus EVALUATION AND RESULTS

47 Magee Campus Percentage of time spent in Wait Nodes

48 Magee Campus Frequency of Preferred Paths in Learning data for Subject 1

49 Magee Campus Journeys involving Subject 1 Base Node

50 Magee Campus Journeys Involving the Base Node for all test subjects Test SubjectTo the Base Node (%) From the Base Node (%) Other (%)

51 Magee Campus Next Node Predictions

52 Magee Campus Medium term predictions Test Subject Final Node in journey successfully predicted by the preferred paths From BN (%) To BN (%) Other (%) Average Correct (%)

53 Magee Campus Frequency of Preferred paths during Time periods for Subject Mon Morning Lunch Evening Tue Morning Lunch Evening Wed Morning Lunch Evening Thur Morning Lunch Evening Fri Morning Lunch Evening

54 Magee Campus Long term predictions from Subject 1

55 Magee Campus Predictions for all Test Subjects Test Subject Predictability Short (%) Medium (%) Long (3 or more) (%) Average838170

56 Magee Campus Accuracy of HABITS in signal black spots Accuracy (95%) Ekahau (m)HABITS (m) Black Spot 1 (Left stairwell) Black Spot 2 (Centre stairwell) Black Spot 3 (Right stairwell)

57 Magee Campus Results of testing HABITS Accuracy (m) approx Yield (%) Latency (s) Cost Ekahau (APs configured for data communication) Ekahau RTLS Ekahau plus HABITS2971Ekahau RTLS Ekahau with 5 extra APs per floor Ekahau RTLS plus €100 per AP

58 Magee Campus Long term predictions for User 1

59 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!

60 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.

61 Magee Campus Thank you for your attention. Questions/Comments


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