Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors

doi:10.1109/JBHI.2019.2896976

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/118870

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題名:	Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors
作者:	Shang, Cuijuan;Chang, Chih-Yung;Chen, Guilin;Zhao, Shenghui;Lin, Jiazao
關鍵詞:	Senior citizens;Feature extraction;Biomedical monitoring;Unsupervised learning;Monitoring;Analytical models;Hardware
日期:	2019-02
上傳時間:	2020-07-07 12:10:23 (UTC+8)
摘要:	The irregularity detection of daily behaviors for the elderly is an important issue in homecare. Plenty of mechanisms have been developed to detect the health condition of the elderly based on the explicit irregularity of several biomedical parameters or some specific behaviors. However, few research works focus on detecting the implicit irregularity involving the combination of diverse behaviors, which can assess the cognitive and physical wellbeing of elders but cannot be directly identified based on sensor data. This paper proposes an Implicit IRregularity Detection (IIRD) mechanism that aims to detect the implicit irregularity by developing the unsupervised learning algorithm based on daily behaviors. The proposed IIRD mechanism identifies the distance and similarity between daily behaviors, which are important features to distinguish the regular and irregular daily behaviors and detect the implicit irregularity of elderly health condition. Performance results show that the proposed IIRD outperforms the existing unsupervised machine-learning mechanisms in terms of the detection accuracy and irregularity recall.
關聯:	IEEE Journal of Biomedical and Health Informatics 24(1), p.131-143
DOI:	10.1109/JBHI.2019.2896976
顯示於類別:	[資訊工程學系暨研究所] 期刊論文

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