This document presents an information extraction system that uses indoor location and activity data to summarize what sessions participants are attending at a conference venue. The system architecture includes sensors to collect physical location data, agents to reason about the context data, and an ontology to model context information. It evaluates tracking 268 of 736 participants over 3 days at the conference, collecting 2.6 million location records. By analyzing participant location clusters for each session time slot, the system aims to detect which sessions a participant spent time in and predict their next session. Future work involves improving prediction capabilities and applying the system to elder care environments.

1. Information Extraction System
Using 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 Hossain
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2. Outline
•Problem domain
• Context-aware
• AAL Butler System
•Context scope
• Architecture overview
• AAL Butler Ontology
•Session participation
•System evaluation
•Conclusion and future work
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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 persons
simultaneous (part of IntelliCare project).
•Scope: activity recognition.
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4. AAL Butler System
•AAL Forum 2010 conference on Ambient
Assisted Living;
•3 days venue;
•7 session tracks;
•Space more than 7000 m2;
•Conference venue include 3 plenary
rooms, 10 session rooms, lunch and break
area, additional areas and corridors;
•Location sytem: WiFi EKAHAU RTLS;
•Created a private wireless network with
35 AP;
•Each participant carry a Wifi Badge tag;
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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;
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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;
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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.
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8. Session participation
•Aim: detect which session a
participant spend time in;
•We have used DBSCAN
datamining technique for
position classification into
clusters;
•Analyse each time slots
individually allows represent
time dimension;
•Each participant have their own
position cluster, for each session
time-slot;
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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;
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10. Conclusion and future work
•Initial attempt at creating a system for providing contextual information on
persons or agents, based on their current and past location data;
•Aggregate time and space dimension with predefined knowledge on time
schedules and locations of activities;
•Describe an approach to do mapping between information presented as
predefined knowledge and the location data of a person using and ontology
based context model;
•We designed an agent-based system with cooperating agents, that used
context model to provide data access to the location of a person as well
person’s activities;
•Conduct a live experiment with multiple participants and large indoor
environment - collect data and provide user services during the venue;
•Next steps use BDI agent architecture, focus on prediction and
recommendation functionality;
•Convert this agent system to use in elder care environments focus on
activity support.
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11. Thank you for your attention
Email:
Pedro Valente
prnv@mmmi.sdu.dk
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