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User Daily Activity Pattern Learning: A Multi-memory Modeling Approach


Authors: S. Gao and A.-H. Tan
Title: User Daily Activity Pattern Learning: A Multi-memory Modeling Approach
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.
Keywords: Assisted living; Geriatrics; Learning (artificial intelligence); Data structures; Hidden Markov models; Pattern recognition; Semantics; Senior citizens; Subspace constraints; Vectors
Conference Name: International Joint Conference on Neural Networks (IJCNN’14)
Location: Beijing, China
Publisher: IEEE
Year: 2014
Accepted PDF File: User_Daily_Activity_Pattern_Learning_A_Multi-memory_Modeling_Approach_accepted.pdf
Permanent Link: http://dx.doi.org/10.1109/IJCNN.2014.6889908
Reference: S. Gao and A.-H. Tan, “User daily activity pattern learning: A multi-memory modeling approach,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN’14). IEEE, July 2014, pp. 1542–1548.
bibtex: 
@inproceedings{LILY-c26, 
   author	= {Gao, Shan and Tan, Ah-Hwee}, 
   title	= {User Daily Activity Pattern Learning: A Multi-memory Modeling Approach}, 
   booktitle	= {Proceedings of the International Joint Conference on Neural Networks (IJCNN'14)}, 
   year		= {2014}, 
   month	= {July}, 
   pages	= {1542-1548},
   location	= {Beijing, China}, 
   publisher	= {IEEE},
}