Zhang, Shuai, McClean, SI and Scotney, BW (2012) Probabilistic Learning from Incomplete Data for Recognition of Activities of Daily Living in Smart Homes. IEEE Transactions on Information Technology in Biomedicine, 16 (3). pp. 454-462. [Journal article]
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Learning behavioral patterns for activities of daily living in a smart home environment can be challenged by the limited number of training data that may be available. This may be due to the infrequent repetition of routine activities (e.g., once daily), the expense of using observers to label activities, and the intrusion that would be caused by the presence of observers over long time periods. It is important, therefore, to make as much use of any labeled data that are collected, however, incomplete these data may be. In this paper, we propose an algorithm for learning behavioral patterns for multi-inhabitants living in a single smart home environment, by making full use of all limited labeled activities, including incomplete data resulting from unreliable low-level sensors in this environment. Through maximum-likelihood estimation, using Expectation-Maximization, we build a model that captures both environmental uncertainties from sensor readings and user uncertainties, including variations in how individuals carry out activities. Our algorithm outperforms models that cannot handle data incompleteness, with increasing performance gains as incompleteness increases. The approach also enables the impact of particular sensors to be assessed and can thus inform sensor maintenance and deployment.
|Item Type:||Journal article|
|Keywords:||Activity recognition; activities of daily living (ADLs); Expectation–Maximization (EM) algorithm; incomplete data; probabilistic learning|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Information and Communication Engineering
|Deposited By:||Professor Bryan Scotney|
|Deposited On:||24 Sep 2012 14:06|
|Last Modified:||01 Aug 2013 10:16|
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