Online from: 2005
Subject Area: Information and Knowledge Management
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|Title:||Ontology-based activity recognition in intelligent pervasive environments|
|Author(s):||Liming Chen, (School of Computing and Mathematics, University of Ulster, Newtownabbey, UK), Chris Nugent, (School of Computing and Mathematics, University of Ulster, Newtownabbey, UK)|
|Citation:||Liming Chen, Chris Nugent, (2009) "Ontology-based activity recognition in intelligent pervasive environments", International Journal of Web Information Systems, Vol. 5 Iss: 4, pp.410 - 430|
|Keywords:||Automation, Decision making|
|Article type:||Research paper|
|DOI:||10.1108/17440080911006199 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
Purpose – This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state-of-the-art in the area of activity recognition, in particular, in the area of object-based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity-based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy-of-use.
Design/methodology/approach – The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology-based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse-grained and fine-grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.
Findings – Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as
Originality/value – The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology-based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.
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