Long-term behavioural change detection through pervasive sensing

John Kemp, Elena Gaura, Ramona Rednic, James Brusey

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)

Abstract

The paper proposes an information generation and summarisation algorithm to detect behavioural change in applications such as long-term monitoring of vulnerable people. The algorithm learns the monitored subject's behaviour autonomously post-deployment and provides time-suppressed summaries of the activity types engaged with by the subject over the course of their day to day life. It transmits updates to external observers only when the summary changes by more than a defined threshold. This technique substantially reduces the number of transmission required by a wearable monitoring system, both through summarisation of the raw data into useful information and by preventing transmission of duplicated or predictable data and information. Based on evaluation using simulated activity data, the proposed algorithm results in an average of one transmission per month following an initial convergence period (reaching less than 1 transmission per day after only three days) and detects a change in behaviour after an average of 1.1 days.

Original languageEnglish
Title of host publicationSNPD 2013 - 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages629-634
Number of pages6
ISBN (Print)9780769550053
DOIs
Publication statusPublished - 2013
Event14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2013 - Honolulu, HI, United States
Duration: 1 Jul 20133 Jul 2013

Other

Other14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2013
CountryUnited States
CityHonolulu, HI
Period1/07/133/07/13

Keywords

  • behavioural change detection
  • body sensor networks
  • pervasive sensing

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