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Probabilistic Learning from Incomplete Data for Recognition of Activities of Daily Living in Smart Homes

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

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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DOI: 10.1109/TITB.2012.2188534

Abstract

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
ID Code:23413
Deposited By:Professor Bryan Scotney
Deposited On:24 Sep 2012 15:06
Last Modified:01 Aug 2013 11:16

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