Paper Info
Title
A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes.
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.
Year
DOI
Venue
2019
10.3233/JIFS-169976
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Activity recognition,knowledge-driven approaches,data-driven approaches,activity model,hybrid reasoning
Activity recognition,Data-driven,Artificial intelligence,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
36
SP5
1064-1246
Citations 
PageRank 
References 
1
0.36
19
Authors
6
Name
Order
Citations
PageRank
Abdul Syafiq Abdull Sukor110.36
Ammar Zakaria25411.45
Norasmadi Abdul Rahim321.06
Latifah Munirah Kamarudin4204.17
Rossi Setchi5305.40
Hiromitsu Nishizaki616329.49