Recognition of Activities of Daily Living from Topic Model

Ihianle, Isibor Kennedy and Naeem, Usman and Tawil, Abdel-Rahman H. (2016) Recognition of Activities of Daily Living from Topic Model. In: Procedia Computer Science. Elsevier, pp. 24-31.

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Abstract

Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.

Item Type: Book Section
Uncontrolled Keywords: Activity discoveryActivity recognitionPattern AnalysisProbabilistic Latent Semantic Analysis
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cyber Security
UoA Collections > UoA11: Computer Science and Informatics
Depositing User: Oana-Andreea Dumitrascu
Date Deposited: 29 Jun 2017 13:18
Last Modified: 29 Jun 2017 13:18
URI: http://www.open-access.bcu.ac.uk/id/eprint/4754

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