Decision Support for Alzheimer's Patients in Smart Homes

Authors:
Shuai Zhang;Sally McClean;Bryan Scotney;Xin Hong;Chris Nugent;Maurice Mulvenna
Affiliations:
-;-;-;-;-;-
Venue:
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Year:
2008

Citing 0
Cited 3

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Abstract

Assistive technology in smart homes for elderly people with Alzheimer's disease is needed to support 'aging in place'. In this paper, we propose a probabilistic learning approach to characterise behavioural patterns for multi-inhabitants in smart homes. Decision support is then provided to monitor and assist patients to complete activities of daily living (ADL). Reasoning is based on the learned profiles and partially observed low-level sensors information. Data are stored in the proposed snow-flake schema based on homeML (an XML based schema for representation of information within smart homes). A laboratory has been developed for studying activities of 'making drinks' for multiple users. Evaluations of our learning and decision support approach are carried out on both real and simulated data. The potential of our approach to support assistive living and home-health monitoring of Alzheimer's patients is demonstrated.