Publication Type

Conference Proceeding Article

Publication Date

7-2014

Abstract

In this paper, we propose a multi-memory model, ADLART model, to discover the daily activity pattern of a sensor monitored user from his/her activities of daily living (ADL). The proposed model mimics the human multiple memory system which comprises a working memory, an episodic memory, and a semantic memory. Through encoding user's daily activities patterns in episodic memory and extracting the regularities of activity routines in semantic memory, the ADLART system is able to learn, recognize, compare, and retrieve daily ADL patterns of the user. Experiments are presented to show the performance of the ADLART model using different parameter settings and its performance is discussed in details

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the International Joint Conference on Neural Networks (IJCNN 2014), 6-11 Jul

First Page

1542

Last Page

1548

ISBN

99781479914845

Identifier

10.1109/IJCNN.2014.6889908

Publisher

IEEE

City or Country

New York

Additional URL

https://doi.org/10.1109/IJCNN.2014.6889908

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