Capturing Regular Human Activity through a Learning Context Memory

Mohr, Philipp H. and Ryan, Nick S. and Timmis, Jon (2006) Capturing Regular Human Activity through a Learning Context Memory. In: 3rd International Workshop on Modeling and Retrieval of Context , 16-17 July 2006, Boston, Massachusetts (USA). (Full text available)

Download (394kB) Preview


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.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04 UTC
Last Modified: 06 Jun 2014 10:40 UTC
Resource URI: (The current URI for this page, for reference purposes)
  • Depositors only (login required):


Downloads per month over past year