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
Citation
GAO, Shan and TAN, Ah-hwee.
User daily activity pattern learning: A multi-memory modeling approach. (2014). Proceedings of the International Joint Conference on Neural Networks (IJCNN 2014), 6-11 Jul. 1542-1548.
Available at: https://ink.library.smu.edu.sg/sis_research/6562
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/IJCNN.2014.6889908