User daily activity pattern learning: A multi-memory modeling approach
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 semant...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2014
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/6562 https://ink.library.smu.edu.sg/context/sis_research/article/7565/viewcontent/User_Daily_Activity_Pattern_Learning___IJCNN_2014.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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 |
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