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 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 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, p.1-12