Title: Anomaly detection for elderly home care

Authors: Kurnianingsih; Lukito Edi Nugroho; Widyawan; Lutfan Lazuardi; Anton Satria Prabuwono; Mahardhika Pratama

Addresses: Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No. 2 Kampus UGM, Yogyakarta 55281, Indonesia; Department of Electrical Engineering, Politeknik Negeri Semarang, Jl. Prof. H. Soedarto, S.H., Tembalang, Semarang 50275, Indonesia ' Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No. 2 Kampus UGM, Yogyakarta 55281, Indonesia ' Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No. 2 Kampus UGM, Yogyakarta 55281, Indonesia ' Department of Health Policy and Management, Faculty of Medicine, Universitas Gadjah Mada, Jl. Farmako Sekip Utara, Yogyakarta 55281, Indonesia ' Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia ' School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, N4-02b-69a, 639798, Singapore

Abstract: In this paper, we propose a model for detecting anomalies in elderly home care. Two scenarios are investigated in detecting anomalies: 1) the elderly person's vital signs and their surrounding environment; 2) the mobility patterns of the elderly. We evaluated our proposed model by employing the isolation forest which detects anomalies using an isolation approach on a random forest of decision trees. We compare isolation forest on unlabeled data with statistical methods on labelled data. Subsequently, to show the reliability of the isolation concept, we compare it with a distance measure concept. The experiment shows that isolation forest has higher detection accuracy and lower error prediction for two attributes in the first scenario: skin temperature and heart rate, whereas, in the second scenario, multi-covariance determinant has a slightly better accuracy compared to isolation forest (3.9% difference in accuracy) and has a small number of prediction errors compared to isolation forest.

Keywords: anomaly detection; isolation forest; elderly home care.

DOI: 10.1504/IJBIDM.2020.107545

International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.4, pp.418 - 444

Received: 27 Apr 2017
Accepted: 30 Oct 2017

Published online: 02 Apr 2020 *

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