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.
|Title of host publication||2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing|
|Number of pages||5|
|Publication status||Published - 16 Sep 2013|
|Event||14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing - Honolulu, United States|
Duration: 1 Jul 2013 → 3 Jul 2013
|Conference||14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing|
|Period||1/07/13 → 3/07/13|
Bibliographical notePaper presented at the 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2013), held 1–3 July 2013, Honolulu Hawaii, U.S.A.
This paper is due to be published by IEEE in the conference proceedings, and full citation details will be updated once available.
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- body sensor networks
- pervasive sensing
- behavioural change detection
- Biomedical monitoring
- Legged locomotion
- Temperature measurement
- Change detection algorithms
- ubiquitous computing
- simulated activity data
- long-term behavioural change detection
- information generation
- information summarisation algorithm
- wearable monitoring system
Kemp, J., Gaura, E., Rednic, R., & Brusey, J. (2013). Long-term behavioural change detection through pervasive sensing. In 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (pp. 629-634). IEEE. https://doi.org/10.1109/SNPD.2013.69