Document Type

Conference Paper


Department of Computer Science


Extracting behavioral motifs for characterizing human daily activities in smart environments




In view of the aging population and the growing need of assisted living, smart houses with basic sensors installed have been investigated to make 24-hour monitoring and tracking of the residents’ indoor activities of daily living possible. Based on the sensor data, healthcare professionals can carry out in-depth examination on residents’ activities of daily living for monitoring their health status. This paper aims to develop a computational algorithm to infer from the sensor data behavioral motifs for characterizing the residents living in such smart environments. The motifs are the over-represented event patterns exhibited by a resident when compared with others. A particular three-step approach is proposed for the motif extraction and a mixture model is adopted. We evaluated the proposed approach using the WSU CASAS dataset (which contains sensor data of 20 persons living in the smart house) and provided detailed interpretation to demonstrate how the behavioral motifs extracted could be used to characterize the residents’ behaviors. We anticipate that such a behavioral motif extraction tool can help healthcare professionals analyze human daily activities more effectively.


Activity of Daily Living, smart environments, behavioral motifs

Publication Date


Source Publication Title

ACM SIGKDD Workshop on Health Informatics (HI-KDD 2012)

Conference Location

Beijing, China




Copyright © 2012 ACM


This work was supported by the General Research Fund (HKBU210410) from the Research Grant Council of the Hong Kong Special Administrative Region, China.

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