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Journal of Healthcare Engineering
Volume 5, Issue 2, Pages 205-228
Research Article

PM2: A Partitioning-Mining-Measuring Method for Identifying Progressive Changes in Older Adults’ Sleeping Activity

Qiang Lin,1 Daqing Zhang,1,2 Kay Connelly,3 Xingshe Zhou,1 and Hongbo Ni1

1Shaanxi Key Lab of Embedded System Technology, School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
2Network & Services Department, Institut Mines-Télécom/Télécom SudPais, Evry Cedex, France
3School of Informatics and Computing, Indiana University, USA

Received 1 September 2013; Accepted 1 February 2014

Copyright © 2014 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As people age, their health typically declines, resulting in difficulty in performing daily activities. Sleep-related problems are common issues with older adults, including shifts in circadian rhythms. A detection method is proposed to identify progressive changes in sleeping activity using a three-step process: partitioning, mining, and measuring. Specifically, the original spatiotemporal representation of each sleeping activity instance was first transformed into a sequence of equal-sized segments, or symbols, via a partitioning process. A data-mining-based algorithm was proposed to find symbols that are not present in all instances of a sleeping activity. Finally, a measuring process was responsible for evaluating the changes in these symbols. Experimental evaluation conducted on a group of datasets of older adults showed that the proposed method is able to identify progressive changes in sleeping activity.