Long-term behavioural change detection through pervasive sensing

John Kemp; Elena Gaura; Ramona Rednic; James Brusey

doi:10.1109/SNPD.2013.69

Long-term behavioural change detection through pervasive sensing

John Kemp, Elena Gaura, Ramona Rednic, James Brusey

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding

3 Citations (Scopus)

74 Downloads (Pure)

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 language	English
Title of host publication	2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
Publisher	IEEE
Pages	629-634
Number of pages	5
ISBN (Electronic)	978-0-7695-5005-3
ISBN (Print)	978-1-4799-0371-9
DOIs	https://doi.org/10.1109/SNPD.2013.69
Publication status	Published - 16 Sept 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

Conference	14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
Country/Territory	United States
City	Honolulu
Period	1/07/13 → 3/07/13

Bibliographical note

Paper 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.

Keywords

body sensor networks
pervasive sensing
behavioural change detection
Monitoring
Biomedical monitoring
Sensors
Legged locomotion
TV
Temperature measurement
Change detection algorithms
ubiquitous computing
simulated activity data
long-term behavioural change detection
information generation
information summarisation algorithm
wearable monitoring system

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10.1109/SNPD.2013.69

Post-PrintAccepted author manuscript, 261 KB

Cite this

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. IEEE, pp. 629-634, 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Honolulu, Hawaii, United States, 1/07/13. https://doi.org/10.1109/SNPD.2013.69

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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.",

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author = "John Kemp and Elena Gaura and Ramona Rednic and James Brusey",

note = "Paper 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. {\textcopyright} 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.; 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing ; Conference date: 01-07-2013 Through 03-07-2013",

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N2 - 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.

AB - 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.

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Y2 - 1 July 2013 through 3 July 2013

ER -

Long-term behavioural change detection through pervasive sensing

Abstract

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Bibliographical note

Keywords

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