Decision support for Alzheimer's patients in smart homes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
Pages236-241
Number of pages6
DOIs
Publication statusPublished - Jun 2008
Event21st IEEE International Symposium on Computer-Based Medical Systems (CBMS 2008) - Jyvaskyla, Finland
Duration: 1 Jun 2008 → …

Publication series

NameCOMPUTER-BASED MEDICAL SYSTEMS : PROCEEDINGS OF THE ANNUAL IEEE SYMPOSIUM

Conference

Conference21st IEEE International Symposium on Computer-Based Medical Systems (CBMS 2008)
Period1/06/08 → …

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Zhang, S., McClean, SI., Scotney, BW., Hong, X., Nugent, CD., & Mulvenna, MD. (2008). Decision support for Alzheimer's patients in smart homes. In Unknown Host Publication (pp. 236-241). (COMPUTER-BASED MEDICAL SYSTEMS : PROCEEDINGS OF THE ANNUAL IEEE SYMPOSIUM). https://doi.org/10.1109/CBMS.2008.16