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Lookup NU author(s): Dr Thomas Ploetz,
Professor Andrew Monk,
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.
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
Print publication date: 01/12/2010
ISSN (print): 1574-1192
ISSN (electronic): 1873-1589
Publisher: Elsevier BV
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