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Using duration to learn activities of daily living in a smart home environment

Zhang, Shuai, McClean, Sally, Bryan, Scotney, Chaurasia, Priyanka and Nugent, Chris (2010) Using duration to learn activities of daily living in a smart home environment. In: Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010 4th International Conference on-NO PERMISSIONS, Munich. IEEE. 8 pp. [Conference contribution]

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DOI: 10.4108/ICST.PERVASIVEHEALTH2010.8804

Abstract

Recognition of inhabitants' activities of daily living (ADLs) is an important task in smart homes to support assisted living for elderly people aging in place. However, uncertain information brings challenge to activity recognition which can be categorised into environmental uncertainties from sensor readings and user uncertainties of variations in the ways to carry out activities in different contexts, or by different users within the same environment. To address the challenges of these two types of uncertainty, in this paper, we introduce the innovative idea of incorporating activity duration into the framework of learning inhabitants' behaviour patterns on carrying out ADLs in smart home environment. A probabilistic learning algorithm is proposed with duration information in the context of multi-inhabitants in a single home environment. The prediction is for both inhabitant and ADL using the learned model representing what activity is carried out and who performed it. Experiments are designed for the evaluation of duration information in identifying activities and inhabitants. Real data have been collected in a smart kitchen laboratory, and realistic synthetic data are generated for evaluation. Evaluations show encouraging results for higher-level activity identification and improvement on inhabitant and activity prediction in the challenging situation of incomplete observation due to unreliable sensors compared to models that are derived with no duration information. The approach also provides a potential opportunity to identify inhabitants' concept drift in long-term monitoring and respond to a deteriorating situation at as early stage as possible.

Item Type:Conference contribution (Paper)
Keywords:home automation;learning systems;probability;activity recognition;daily living activities;multi-inhabitants context;probabilistic learning algorithm;smart home environment;smart kitchen laboratory;Aging;Dementia;Home computing;Intelligent sensors;Monitoring;Predictive models;Senior citizens;Sensor phenomena and characterization;Smart homes;Uncertainty;ADL;duration;probabilistic learning;reasoning;smart home
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
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 > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:33129
Deposited By: Dr Priyanka Chaurasia
Deposited On:26 Jan 2016 16:16
Last Modified:26 Jan 2016 16:16

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