Modeling patterns of activities using activity curves |
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Affiliation: | 1. School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States;2. Department of Psychology, Washington State University, Pullman, WA, United States;1. Computer Science Department, UCLA, Los Angeles, CA 90095, United States;2. Medical Imaging Informatics, UCLA, Los Angeles, CA 90095, United States;1. DeustoTech – University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain;2. School of Computer Science and Informatics – De Montfort University, LE19BH Leicester, United Kingdom |
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Abstract: | 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. |
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Keywords: | Activity curve Smart environments Functional assessment Permutation |
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