Citation

ELhadad, Rawan and Tan, Yi Fei (2023) Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification. Journal of ICT Research and Applications, 17 (1). pp. 46-57. ISSN 2337-5787

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Abstract

In most countries, the old-age people population continues to rise. Because young adults are busy with their work engagements, they have to let the elderly stay at home alone. This is quite dangerous, as accidents at home may happen anytime without anyone knowing. Although sending elderly relatives to an elderly care center or hiring a caregiver are good solutions, they may not be feasible since it may be too expensive over a long-term period. The behavior patterns of elderly people during daily activities can give hints about their health condition. If an abnormal behavior pattern can be detected in advance, then precautions can be taken at an early stage. Previous studies have suggested machine learning techniques for such anomaly detection but most of the techniques are complicated. In this paper, a simple model for detecting anomaly patterns in human activity sequences using Random forest (RF) and K-nearest neighbor (KNN) classifiers is presented. The model was implemented on a public dataset and it showed that the RF classifier performed better, with an accuracy of 85%, compared to the KNN classifier, which achieved 73%.

Item Type:	Article
Uncontrolled Keywords:	Anomaly detection, classification, elderly, home activities, sequence pattern
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:	02 Jun 2023 00:50
Last Modified:	02 Jun 2023 00:50
URII:	http://shdl.mmu.edu.my/id/eprint/11439

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