Modeling patterns of activities using activity curves

详细信息    查看全文

• 作者：Prafulla N. Dawadi^a ; ^{pdawadi@eecs.wsu.edu" class="auth_mail" title="E-mail the corresponding author} ; Diane J. Cook^a ; ^{cook@eecs.wsu.edu" class="auth_mail" title="E-mail the corresponding author} ; Maureen Schmitter-Edgecombe^b ; ^{schmitter-e@wsu.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Activity curve ; Smart environments ; Functional assessment ; ​Permutation
• 刊名：Pervasive and Mobile Computing
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：28
• 期：Complete
• 页码：51-68
• 全文大小：1212 K

文摘

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual’s normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.