Recognition of activities of daily living from topic model

Ihianle, IK ORCID: https://orcid.org/0000-0001-7445-8573, Naeem, U and Tawil, A-R, 2016. Recognition of activities of daily living from topic model. Procedia Computer Science, 98, pp. 24-31. ISSN 1877-0509

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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:	Journal article
Publication Title:	Procedia Computer Science
Creators:	Ihianle, I.K., Naeem, U. and Tawil, A.-R.
Publisher:	Elsevier
Date:	2016
Volume:	98
ISSN:	1877-0509
Identifiers:	Number Type 10.1016/j.procs.2016.09.007 DOI S1877050916321287 Publisher Item Identifier 1314878 Other
Rights:	© 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	15 Apr 2020 09:11
Last Modified:	15 Apr 2020 09:11
URI:	https://irep.ntu.ac.uk/id/eprint/39614

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