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Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification

Journal of ICT Research and Applications (JICTRA) · Vol. 17 No. 1 (2023) · pp. 46–57

DOI
10.5614/itbj.ict.res.appl.2023.17.1.4
ISSN
2338-5499, 2337-5787
Published
2023-04-17
Publisher
DRI - ITB
Language
eng

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%.

Keywords

Collaboration network of the authors

9 authors · 17 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Multimedia University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Chee‐Pun OoiMultimedia University2
Wooi-Haw TanMultimedia University2
Xiaoning GuoMultimedia University1
Soon-Chang PohMultimedia University1
Soon Chang PohMultimedia University1
Chin-Kit NgMultimedia University1
Soon-Nyean CheongMultimedia University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification</dc:title>
	<dc:creator>Rawan Mohammed Elhadad</dc:creator>
	<dc:creator>Tan, Yi-Fei</dc:creator>
	<dc:subject xml:lang="en-US">anomaly detection</dc:subject>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">elderly</dc:subject>
	<dc:subject xml:lang="en-US">home activities</dc:subject>
	<dc:subject xml:lang="en-US">sequence pattern</dc:subject>
	<dc:description xml:lang="en-US">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%.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2023-04-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/17768</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2023.17.1.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 17 No. 1 (2023); 46-57</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2022.17.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/17768/6159</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of ICT Research and Applications</dc:rights>
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