Capturing regular human activity through a learning context memory

Philipp H. Mohr*, Nick Ryan, Jon Timmis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


A learning context memory consisting of two main parts is presented. The first part performs lossy data compression, keeping the amount of stored data at a minimum by combining similar context attributes - the compression rate for the presented GPS data is 150:1 on average. The resulting data is stored in an appropriate data structure highlighting the level of compression. Elements with a high level of compression are used in the second part to form the start and end points of episodes capturing common activity consisting of consecutive events. The context memory is used to investigate how little context data can be stored containing still enough information to capture regular human activity.

Original languageEnglish
Title of host publicationModeling and Retrieval of Context - Papers from the AAAI Workshop, Technical Report
Number of pages6
Publication statusPublished - 2006
Event2006 AAAI Workshop - Boston, MA, United States of America
Duration: 16 Jul 200617 Jul 2006

Publication series

NameAAAI Workshop - Technical Report


Conference2006 AAAI Workshop
Country/TerritoryUnited States of America
CityBoston, MA
Period16 Jul 200617 Jul 2006

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