Recognition Framework for Inferring Activities of Daily Living Based on Pattern Mining

Article


Nasreen, Shamila, Azam, Muhammad Awais, Naeem, U., Ghazanfar, Mustansar Ali and Khalid, Asra 2016. Recognition Framework for Inferring Activities of Daily Living Based on Pattern Mining. Arabian Journal for Science and Engineering. 41 (8), pp. 3113-3126.
AuthorsNasreen, Shamila, Azam, Muhammad Awais, Naeem, U., Ghazanfar, Mustansar Ali and Khalid, Asra
Abstract

Ambient assisted living applications are very much dependent on robust activity recognition frameworks, which allow these applications to provide services based on the contextual information that has been discovered. Existing frameworks have generally focused on the application of traditional classifiers and semantics reasoning to recognize activities. Nevertheless, being able to recognize unexpected actions remains a challenge. The work in this paper presents an approach that is able to recognize activities that have been conducted in an unordered manner. The recognition framework extends an existing approach that recognizes activities by exploiting the different levels of abstraction within an activity. A frequent pattern mining algorithm has been applied to the recognition framework in order to find patterns within the stream of captured events, which in turn increases the adaptive learning ability of the proposed recognition framework. This paper also presents experimental results that validate the recognition ability of the recognition framework. The motivation of this work is to be able to detect the functional decline among elderly people suffering from Alzheimer’s disease by recognizing their daily activities.

JournalArabian Journal for Science and Engineering
Journal citation41 (8), pp. 3113-3126
ISSN1319-8025
Year2016
PublisherSpringer Verlag for King Fahd University of Petroleum & Minerals
Digital Object Identifier (DOI)doi:10.1007/s13369-016-2091-9
Web address (URL)https://doi.org/10.1007/s13369-016-2091-9
Publication dates
Online18 Mar 2016
Publication process dates
Deposited09 Nov 2018
Accepted17 Feb 2016
Accepted17 Feb 2016
Copyright information© 2016 King Fahd University of Petroleum & Minerals
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