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Rapid specification and automated generation of prompting systems to assist people with dementia

Lookup NU author(s): Dr Thomas Ploetz, Professor Andrew Monk, Cuong Pham, Professor Patrick OlivierORCiD


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Activity recognition in intelligent environments could play a key role for supporting people in their activities of daily life. Partially observable Markov decision process (POMDP) models have been used successfully, for example, to assist people with dementia when carrying out small multistep tasks such as hand washing. POMDP models are a powerful, yet flexible framework for modeling assistance that can deal with uncertainty and utility in a theoretically well-justified manner. Unfortunately, POMDPs usually require a very labor-intensive, manual set-up procedure. This paper describes a knowledge-driven method for automatically generating POMDP activity recognition and context-sensitive prompting systems for complex tasks. It starts with a psychologically justified description of the task and the particular environment in which it is to be carried out that can be generated from empirical data. This is then combined with a specification of the available sensors and effectors to build a working prompting system. The method is illustrated by building a system that prompts through the task of making a cup of tea in a real-world kitchen. The case is made that, with further development and tool support, the method could feasibly be used in a clinical or industrial setting.

Publication metadata

Author(s): Hoey J, Ploetz T, Jackson D, Monk A, Pham C, Olivier P

Publication type: Article

Publication status: Published

Journal: Pervasive and Mobile Computing

Year: 2011

Volume: 7

Issue: 3

Pages: 299-318

Print publication date: 01/12/2010

ISSN (print): 1574-1192

ISSN (electronic): 1873-1589

Publisher: Elsevier BV


DOI: 10.1016/j.pmcj.2010.11.007


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