Recognition of Activities of Daily Living from Topic Model

Article

Ihianle, I., Naeem, U. and Tawil, A. 2016. Recognition of Activities of Daily Living from Topic Model. Procedia Computer Science. 98, pp. 24-31. https://doi.org/10.1016/j.procs.2016.09.007
AuthorsIhianle, I., Naeem, U. and Tawil, A.
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.

JournalProcedia Computer Science
Journal citation98, pp. 24-31
ISSN1877-0509
Year2016
PublisherElsevier
Publisher's version
License
CC BY-NC-ND
Digital Object Identifier (DOI)https://doi.org/10.1016/j.procs.2016.09.007
Publication dates
Print21 Sep 2016
Publication process dates
Deposited23 Sep 2016
Accepted20 Jun 2016
Copyright information© 2016 The Authors
Additional information

Published in Procedia Computer Science (Volume 98, 2016) special issue:
The 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2016)/The 6th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2016)/Affiliated Workshops.

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