Fine-Regularity Feature Extraction of Elderly Behaviors Based on Unsupervised Learning

Behavioral analysis of the elderly is very important for improving the quality of elderly healthcare services. This paper proposes a Fine-Regularity Feature Extraction Mechanism (FRFEM) based on unsupervised learning algorithm, which can extract fine features including occurrence, duration and frequency regularities. The proposed FRFEM can be applied to detect abnormal behaviors and assess the health status of the elderly. Simulation study exhibits the effectiveness of the proposed FRFEM on feature extraction.