Citation

Poh, Soon Chang and Tan, Yi Fei and Cheong, Soon Nyean and Ooi, Chee Pun and Tan, Wooi Haw (2019) Anomaly Detection for Home Activity based on Sequence Pattern. International Journal of Technology, 10 (7). pp. 1276-1285. ISSN 2086-9614

Text 109.pdf - Published Version Restricted to Repository staff only Download (501kB)

Abstract

In Malaysia, the elderly population continues to grow. At the same time, young adults are unable to take care of their elderly parents due to work commitments. This results in an increasing number of elderly people living in solitude. Therefore, it is crucial to monitor elderly people’s behavior, especially the pattern of their daily home activities. Abnormal behaviors in carrying out home activities may indicate health concerns in elderly people. Past studies have proposed the use of complex machine learning algorithms to detect anomalies in daily sequences of home activities. In this paper, a simple, alternative method for detecting anomalies in daily sequences of home activities is presented. The experiment results demonstrate that the model achieved a test accuracy of 90.79% on a public dataset.

Item Type:	Article
Uncontrolled Keywords:	Anomaly detection; Elderly; Home activities; Sequence pattern
Subjects:	Q Science > QP Physiology > QP351 Neurophysiology and Neuropsychology
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	28 Oct 2021 02:48
Last Modified:	28 Oct 2021 02:48
URII:	http://shdl.mmu.edu.my/id/eprint/8843

Downloads

Edit (login required)