1 Location Modeling and Machine Learning in Smart Environments Robert Whitaker Supervisor: A/Prof Judy Kay A/Prof Bob Kummerfeld A/Prof Bob Kummerfeld

2 Overview Problem Previous Work Possible Data Sources Tools Available Issues

3 Thesis Topic Explore ways of determining a persons current location and activity Explore ways of predicting a persons location/activity using Location Modeling and Machine Learning The results returned must be scrutable

4 Possible Situation Where’s Boris Scenario Wish to organize a meeting with another person where the time suits both parties

5 Possible Steps Contact the person you wish to meet Both people would look at their schedules and negotiate a time Both parties agree on the time they are to meet

6 Possible Problems One of the persons schedule may be incomplete When you arrive at the meeting time the person is not there. Should you wait? Where is the person? What if you can’t connect the person to organise the meeting

7 High Level View

8 Previous Work Active Badge Project Lancaster Guide Project Doppelganger Activity Compass Project

9 Active Badge Project First Indoor positioning system Users wear badges to emit their location Applied to teleporting Active Bat project extended the basic concepts developed Source: Nigel Davies and Hans-Werner Gellersen Beyond Prototypes: Challenges in Deploying Ubiquitous Systems. IEEE Pervasive Computing, Volume 1 (Jan-March 2002)

10 Lancaster Guide Project A tourist guide for the city of Lancaster Used tablet PC’s connected to a network Limited by the infrastructure capabilities. Source: 1. Nigel Davies and Hans-Werner Gellersen Beyond Prototypes: Challenges in Deploying Ubiquitous Systems. IEEE Pervasive Computing, Volume 1 (Jan-March 2002) The Guide Project,

11 Lancaster Guide Interface Source: The Guide Project,

12 Doppelganger Generalized tool for gathering, processing and providing information about users Learning Techniques  Beta Distribution  Linear Prediction  Markov Models DopMail Source: Orwant, J., Heterogeneous Learning in the Doppelganger User Modeling System. in User Modeling and User-Adapted Interaction, (1995),

13 Doppelganger Applications Beta Distribution Linear Prediction Markov Models Learning Toolbox Sensors Source: Orwant, J., Heterogeneous Learning in the Doppelganger User Modeling System. in User Modeling and User-Adapted Interaction, (1995),

14 Activity Compass Project Location Modeling to help disabled PDA device application developed to assist with location tracking Tracking movements and comparing them to a map Prediction algorithms used Relational Markov Models Source: Patterson, D.J., Etzioni, O. and Kautz, H. The Activity Compass, University of Washington, 2003.

15 Prototype of Activity Compass Source: Patterson, D.J., Etzioni, O. and Kautz, H. The Activity Compass, University of Washington, 2003.

16 Possible Data Sources Bluetooth Devices Machine Learning  Windows Based  Unix Based

17 Tools Personis Elvin Messaging Bspy Markov Modeling Toolkits Manual Logs for Evaluation Purposes

18 Personis User modeling software Accretion representation  Consists of components which model aspects of the user Allows the user model to be scruntised Source: Kay, J., Kummerfeld, B. and Lauder, P., Managing private user models and shared personas. in Workshop on User Modelling for Ubiquitous Computing, (Pittsburgh, USA, 2003).

19 Example of User Model Output from Personis: Modeling the locations where the user has been

20 Elvin Messaging Publish/Subscribe Messaging System Messages routed by content Application: sending messages between sensors and modeling software Elvin Router Client Source: Mantara Software Elvin Administrator's Guide, 2003.

21 Bspy Bluetooth positioning system Detects Bluetooth devices and logs them to a database Uses Elvin messages to send information from sensor to database

22 Example Data

23 Markov Modeling Toolkits Hidden Markov Modeling Package – Python Matlab Hidden Markov Package Markov Chain Algorithm Cambridge Markov Modeling Toolkit

24 Manual Logs Records activity and location in 15 min blocks Provides some example data to develop the algorithms off Used for the evaluation of the learning algorithm

25 Code Sheet

26 Manual Log

27 Research Issues Representation of location and activity Creation of data sets Modeling Time

28 Questions