Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification

Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification

anomaly detection; classification; elderly; home activities;sequence patterns.

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

Rawan Mohammed Elhadad & Yi-Fei Tan*

https://journals.itb.ac.id/index.php/jictra/article/view/17768/6159

Faculty of Engineering, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia

2023

Fajar bagus W

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