關鍵字查詢 | 類別:期刊論文 | | 關鍵字:Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors

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序號 學年期 教師動態
1 108/1 資工系 張志勇 教授 期刊論文 發佈 Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors , [108-1] :Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors期刊論文Implicit Irregularity Detection Using Unsupervised Learning on Daily BehaviorsCuijuan Shang; Chih-Yung Chang; Guilin Chen; Shenghui Zhao; Jiazao LinSenior citizens;Feature extraction;Biomedical monitoring;Unsupervised learning;Monitoring;Analytical models;HardwareIEEE Journal of Biomedical and Health Informatics 24(1), p.131-143The 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 (I
2 107/2 資工系 張志勇 教授 期刊論文 發佈 Implicit Irregularity Detection using Unsupervised Learning on Daily Behaviors , [107-2] :Implicit Irregularity Detection using Unsupervised Learning on Daily Behaviors期刊論文Implicit Irregularity Detection using Unsupervised Learning on Daily BehaviorsC. J. Shang; C. Y. Chang; G. L. Chen; S. H. Zhao; J. Z. LinSenior citizens;Feature extraction;Biomedical monitoring;Unsupervised learning;Monitoring;Analytical models;HardwareIEEE Journal of Biomedical and Health Informatics, p.1-12The 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 researches 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, which aim
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