Title: Location Modeling and Machine Learning in Smart Environments
1Location Modeling and Machine Learning in Smart
Environments
- Robert Whitaker
- Supervisor A/Prof Judy Kay
- A/Prof Bob Kummerfeld
2Overview
- Problem
- Previous Work
- Possible Data Sources
- Tools Available
- Issues
3Thesis 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
4Possible Situation
- Wheres Boris Scenario
- Wish to organize a meeting with another person
where the time suits both parties
5Possible 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
6Possible 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 cant connect the person to organise
the meeting
7High Level View
8Previous Work
- Active Badge Project
- Lancaster Guide Project
- Doppelganger
- Activity Compass Project
9Active 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). 26-35.
10Lancaster Guide Project
- A tourist guide for the city of Lancaster
- Used tablet PCs connected to a 802.11 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). 26-35. 2. The Guide
Project, http//www.guide.lancs.ac.uk
11Lancaster Guide Interface
Source The Guide Project, http//www.guide.lancs.
ac.uk
12Doppelganger
- 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),
107-130.
13Doppelganger
Source Orwant, J., Heterogeneous Learning in the
Doppelganger User Modeling System. in User
Modeling and User-Adapted Interaction, (1995),
107-130.
14Activity 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.
15Prototype of Activity Compass
Source Patterson, D.J., Etzioni, O. and Kautz,
H. The Activity Compass, University of
Washington, 2003.
16Possible Data Sources
- Bluetooth Devices
- Machine Learning
- Windows Based
- Unix Based
17Tools
- Personis
- Elvin Messaging
- Bspy
- Markov Modeling Toolkits
- Manual Logs for Evaluation Purposes
18Personis
- 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).
19Example of User Model
Output from Personis Modeling the locations
where the user has been
20Elvin Messaging
- Publish/Subscribe Messaging System
- Messages routed by content
- Application sending messages between sensors and
modeling software
Source Mantara Software Elvin Administrator's
Guide, 2003.
21Bspy
- Bluetooth positioning system
- Detects Bluetooth devices and logs them to a
database - Uses Elvin messages to send information from
sensor to database
22Example Data
23Markov Modeling Toolkits
- Hidden Markov Modeling Package Python
- Matlab Hidden Markov Package
- Markov Chain Algorithm
- Cambridge Markov Modeling Toolkit
24Manual 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
25Code Sheet
26Manual Log
27Research Issues
- Representation of location and activity
- Creation of data sets
- Modeling Time
28Questions