Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 23, Issue 4, Pages 527-539Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2010.148
Keywords
Activity recognition; data mining; sequence mining; clustering; smart homes
Categories
Funding
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [0914371, 0852172] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1064628] Funding Source: National Science Foundation
- NIBIB NIH HHS [R01 EB015853, R01 EB009675, R01 EB009675-01A1] Funding Source: Medline
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The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.
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