A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Sukor, Abdul Syafiq Abdull, Zakaria, Ammar, Rahim, Norasmadi Abdul, Kamarudin, Latifah Munirah, Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544, Nishizaki, Hiromitsu, Vijayakumar, V., Subramaniyaswamy, V., Abawajy, Jemal and Yang, Longzhi 2019. A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes. Journal of Intelligent and Fuzzy Systems 36 (5) , pp. 4177-4188. 10.3233/JIFS-169976

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Abstract

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	IOS Press
ISSN:	1064-1246
Date of First Compliant Deposit:	19 August 2019
Date of Acceptance:	7 June 2019
Last Modified:	06 Nov 2023 17:34
URI:	https://orca.cardiff.ac.uk/id/eprint/123112

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