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Activities of daily life recognition using process representation modelling to support intention analysis

Activities of daily life recognition using process representation modelling to support intention analysis

Naeem, Usman, Bashroush, Rabih, Anthony, Richard, Azam, Muhammad Awais, Tawil, Abdel Rahman, Lee, Sin Wee and Wong, M.L. Dennis (2015) Activities of daily life recognition using process representation modelling to support intention analysis. International Journal of Pervasive Computing and Communications, 11 (3). pp. 347-371. ISSN 1742-7371 (doi:10.1108/IJPCC-01-2015-0002)

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Abstract

Purpose
– This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge.

Design/methodology/approach
– This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients.

Findings
– A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches.

Originality/value
– The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features.

Item Type: Article
Uncontrolled Keywords: Assisted living, Activity recognition, Alzheimer’s disease, Activities of daily life, Elderly monitoring, Inference engine
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 18 Nov 2016 12:37
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/15681

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