Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors

• Published in: IEEE journal of biomedical and health informatics 2020-01, Vol.24 (1), p.131-143
• Main Authors: Shang, Cuijuan, Chang, Chih-Yung, Chen, Guilin, Zhao, Shenghui, Lin, Jiazao
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Analytical models; Biomedical monitoring; Cognitive ability; Daily behavior; Databases, Factual; elderly; Feature extraction; Geriatrics; Hardware; Home Care Services; homecare; Human Activities - classification; Humans; implicit irregularity; Irregularities; Learning algorithms; Machine learning; Monitoring; Monitoring, Physiologic; Older people; Senior citizens; Unsupervised learning; Unsupervised Machine Learning
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2019.2896976

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.