Anomaly detection algorithms for home activities data

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. Hirin...

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Bibliographic Details
Main Author:	Poh, Soon Chang
Format:	Thesis
Published:	2019
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
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id	my-mmu-ep.12849
record_format	uketd_dc
spelling	my-mmu-ep.128492024-08-21T08:16:31Z Anomaly detection algorithms for home activities data 2019-10 Poh, Soon Chang TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television 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. 2019-10 Thesis https://shdl.mmu.edu.my/12849/ http://erep.mmu.edu.my/ masters Multimedia University Faculty of Engineering (FOE) EREP ID: 12273
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Poh, Soon Chang Anomaly detection algorithms for home activities data
description	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.
format	Thesis
qualification_level	Master's degree
author	Poh, Soon Chang
author_facet	Poh, Soon Chang
author_sort	Poh, Soon Chang
title	Anomaly detection algorithms for home activities data
title_short	Anomaly detection algorithms for home activities data
title_full	Anomaly detection algorithms for home activities data
title_fullStr	Anomaly detection algorithms for home activities data
title_full_unstemmed	Anomaly detection algorithms for home activities data
title_sort	anomaly detection algorithms for home activities data
granting_institution	Multimedia University
granting_department	Faculty of Engineering (FOE)
publishDate	2019
_version_	1811768007995686912

Anomaly detection algorithms for home activities data

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