Ontology Evolution for Personalized and Adaptive Activity Recognition

Journal article


Safyan, M, Ul Qayyum, Z, Sarwar, S, Iqbal, M, Castro, R G and Al-Dulaimi, A (2019). Ontology Evolution for Personalized and Adaptive Activity Recognition. IET Wireless Sensor Systems.
AuthorsSafyan, M, Ul Qayyum, Z, Sarwar, S, Iqbal, M, Castro, R G and Al-Dulaimi, A
Abstract

Ontology-based knowledge driven Activity Recognition (AR) models play a vital role in realm of Internet of Things (IoTs). However, these models suffer the shortcomings of static nature, inability of self-evolution and lack of adaptivity. Also, AR models cannot be made comprehensive enough to cater all the activities and smart home inhabitants may not be restricted to only those activities contained in AR model. So, AR models may not rightly recognize or infer new activities. In this paper, a framework has been proposed for dynamically capturing the new knowledge from activity patterns to evolve behavioural changes in AR model (i.e. ontology based model). This ontology based framework adapts by learning the specialized and extended activities from existing user-performed activity patterns. Moreover, it can identify new activity patterns previously unknown in AR model, adapt the new properties in existing activity models and enrich ontology model by capturing change representation to enrich ontology model. The proposed framework has been evaluated comprehensively over the metrics of accuracy, statistical heuristics and Kappa Coefficient. A well-known dataset named DAMSH has been used for having an empirical insight to the effectiveness of proposed framework that shows a significant level of accuracy for AR models

This paper is a postprint of a paper submitted to and accepted for publication in IET Wireless Sensor Systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library

Year2019
JournalIET Wireless Sensor Systems
PublisherInstitution of Engineering and Technology
ISSN2043-6386
Digital Object Identifier (DOI)doi:10.1049/iet-wss.2018.5209
Web address (URL)https://digital-library.theiet.org/content/journals/10.1049/iet-wss.2018.5209
Publication dates
Print01 Mar 2019
Publication process dates
Deposited01 Apr 2019
Accepted27 Feb 2019
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open
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https://openresearch.lsbu.ac.uk/item/86771

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