Paams2011 pvalente-presentation-slides1


Published on

Published in: Education
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Paams2011 pvalente-presentation-slides1

  1. 1. Information Extraction SystemUsing Indoor Location and Activity Plan 9th International Conference on Practical Applications of Agents and Multi-Agent Systems 6-8th April, 2011 Bjørn Grønbæk, Pedro Valente and Kasper Hallenborg The Maersk Mc-Kinney Moller Institute, Odense, Denmark Special thanks to Shabbir Hossain6 April 2011 1
  2. 2. Outline•Problem domain • Context-aware • AAL Butler System•Context scope • Architecture overview • AAL Butler Ontology•Session participation•System evaluation•Conclusion and future work6 April 2011 2
  3. 3. Context-aware “Context is any information that can be used to characterize the situation of an entity” Dey(2001)•Context-Aware components: • Sensing (input)– acquire data or information about physical world or some aspect of the physical world; • Thinking (reasoning)- make sense of it; • Acting (output) – Effectors and actions to be taken;•Target: indoor environments tracking several personssimultaneous (part of IntelliCare project).•Scope: activity recognition.6 April 2011 3
  4. 4. AAL Butler System•AAL Forum 2010 conference on AmbientAssisted Living;•3 days venue;•7 session tracks;•Space more than 7000 m2;•Conference venue include 3 plenaryrooms, 10 session rooms, lunch and breakarea, additional areas and corridors;•Location sytem: WiFi EKAHAU RTLS;•Created a private wireless network with35 AP;•Each participant carry a Wifi Badge tag;6 April 2011 4
  5. 5. Architecture overview Layers: •Sensor: logical and physical Acting data sources; •Data Retrieval: extract data from sensors and match it with subsystem Thinking context model; •Preprocessing: contextual information from multiple data sources into a single context source; subsystem Sensing •Storage and management: interface to applications; •Application: target to end- users;6 April 2011 5
  6. 6. Architecture overview - Agents Agents: •EA - ekahau agent •AIA - AAL Information Agent; •ACA - AAL Context Agent; •ABA – AAL Butler Agent; •APA – AAL Participant Agent; •MA – Mapping Agent; •SEA – Session Evaluation Agent;6 April 2011 6
  7. 7. AAL Butler Ontology •Ontologies provide a comprehensive model of different types of context information, which can be used to describe situations for particular domains •Context information can be reasoned about in a logical way and different entities can understand and utilize the knowledge.6 April 2011 7
  8. 8. Session participation•Aim: detect which session aparticipant spend time in;•We have used DBSCANdatamining technique forposition classification intoclusters;•Analyse each time slotsindividually allows representtime dimension;•Each participant have their ownposition cluster, for each sessiontime-slot;6 April 2011 8
  9. 9. System evaluation•Live and historic position data • Have tracked 268 of 736 participants; • Collect 2.6 million position records;•Session participation extraction • Each user represented as an agent in the system; • Each agent was able to keep track of the RTLS tag location; • Observe participant’s session history; • Perform a simple prediction on the next session;•Indoor localization system: Ekahau RTLS • System accuracy decrease with user gathering • System calibration and AP signal coverage can influence clustering techniques;6 April 2011 9
  10. 10. Conclusion and future work•Initial attempt at creating a system for providing contextual information onpersons or agents, based on their current and past location data;•Aggregate time and space dimension with predefined knowledge on timeschedules and locations of activities;•Describe an approach to do mapping between information presented aspredefined knowledge and the location data of a person using and ontologybased context model;•We designed an agent-based system with cooperating agents, that usedcontext model to provide data access to the location of a person as wellperson’s activities;•Conduct a live experiment with multiple participants and large indoorenvironment - collect data and provide user services during the venue;•Next steps use BDI agent architecture, focus on prediction andrecommendation functionality;•Convert this agent system to use in elder care environments focus onactivity support.6 April 2011 10
  11. 11. Thank you for your attention Email: Pedro Valente prnv@mmmi.sdu.dk6 April 2011 11