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User activity pattern analysis in telecare data

Angelova, Maia, Ellman, Jeremy, Gibson, Helen, Oman, Paul, Rajasegarar, Sutharshan and Zhu, Ye 2018, User activity pattern analysis in telecare data, IEEE access, vol. 6, pp. 33306-33317, doi: 10.1109/ACCESS.2018.2847294.

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Title User activity pattern analysis in telecare data
Author(s) Angelova, MaiaORCID iD for Angelova, Maia orcid.org/0000-0002-0931-0916
Ellman, Jeremy
Gibson, Helen
Oman, Paul
Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736
Zhu, Ye
Journal name IEEE access
Volume number 6
Start page 33306
End page 33317
Total pages 12
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018-06-14
ISSN 2169-3536
Keyword(s) aging care
data analytics
machine learning
statistical analysis
telecare
Summary Telecare is the use of devices installed in homes to deliver health and social care to the elderly and infirm. The aim of this paper is to identify patterns of use for different devices and associations between them. The data were provided by a telecare call center in the North East of England. Using statistical analysis and machine learning, we analyzed the relationships between users' characteristics and device activations. We applied association rules and decision trees for the event analysis, and our targeted projection pursuit technique was used for the user-event modeling. This study reveals that there is a strong association between users' ages and activations, i.e., different age group users exhibit different activation patterns. In addition, a focused analysis on the users with mental health issues reveals that the older users with memory problems who live alone are likely to make more mistakes in using the devices than others. The patterns in the data can enable the telecare call center to gain insight into their operations and improve their effectiveness in several ways. This study also contributes to automatic analysis and support for decision making in the telecare industry.
Language eng
DOI 10.1109/ACCESS.2018.2847294
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30110859

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.