A Knowledge-Driven Approach to Activity Recognition in Smart Homes

Abstract—This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. Firstly, it makes extensive use of domain knowledge in the lifecycle of activity recognition. Secondly, it uses ontologies for explicit context and activity modeling and representation. Thirdly and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper we analyse the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity re...

• Type: Journal
• Year: 2012
• Where: TKDE
• Authors: Liming Chen, Chris D. Nugent, Hui Wang
• Keywords: Centric Methods, Explicit Context, Formal Methods, Reasoning Algorithms, TKDE 2012