Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors
學年 108
學期 1
出版(發表)日期 2020-01-06
作品名稱 Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors
著者 Cuijuan Shang; Chih-Yung Chang; Guilin Chen; Shenghui Zhao; Jiazao Lin
著錄名稱、卷期、頁數 IEEE Journal of Biomedical and Health Informatics 24(1), p.131-143
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.
關鍵字 Senior citizens;Feature extraction;Biomedical monitoring;Unsupervised learning;Monitoring;Analytical models;Hardware
語言 en_US
期刊性質 國外
收錄於 SCI
通訊作者 Chih-Yung Chang
國別 USA
出版型式 ,電子版

機構典藏連結 ( )