Citation

Poh, Soon Chang (2019) Anomaly detection algorithms for home activities data. Masters thesis, Multimedia University.

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Abstract

The population of elderly around the world is projected to have continuous growth. It is speculated that the number of solitude elderly is on the rise. Research finding shown that elderly living alone has a higher mortality rate. Thus, there is a need to continuously monitor elderly condition. Hiring caregivers is not affordable for some young adults due to its relatively expensive cost. Researchers proposed using activity recognition to monitor elderly daily routine. This activity recognition system generates historical dataset of home activities for the elderly subject. The health condition of a person is closely related to that person’s life pattern. Changes in behaviour or home activities pattern may indicate illness, Therefore, anomaly detection on home activities data is important.

Item Type:	Thesis (Masters)
Additional Information:	Call No.: TK5105.59 .P64 2019
Uncontrolled Keywords:	Anomaly detection (Computer security)
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	21 Aug 2024 08:16
Last Modified:	21 Aug 2024 08:16
URII:	http://shdl.mmu.edu.my/id/eprint/12849

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