A Knowledge-driven Approach to Composite Activity Recognition in Smart Environments

George Okeyo; Liming Chen; Hui Wang; Roy Sterritt

doi:10.1007/978-3-642-35377-2_44

A Knowledge-driven Approach to Composite Activity Recognition in Smart Environments

George Okeyo, Liming Chen, Hui Wang, Roy Sterritt

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

9 Citations (Scopus)

Abstract

Knowledge-driven activity recognition has recently attracted increasing attention but mainly focused on simple activities. This paper extends previous work to introduce a knowledge-driven approach to recognition of composite activities such as interleaved and concurrent activities. The approach combines ontological and temporal knowledge modelling formalisms for composite activity modelling. It exploits ontological reasoning for simple activity recognition and rule-based temporal inference to support composite activity recognition. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. The initial experimental results have shown that average recognition accuracy for simple and composite activities is 100% and 88.26%, respectively.

Original language	English
Title of host publication	Ubiquitous Computing and Ambient Intelligence, Lecture Notes in Computer Science
Publisher	Springer
Pages	322-329
Volume	7656
ISBN (Print)	978-3-642-35376-5
DOIs	https://doi.org/10.1007/978-3-642-35377-2_44
Publication status	Published (in print/issue) - 3 Dec 2012

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A Knowledge-driven Approach to Composite Activity Recognition in Smart Environments

Abstract

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