Irregularity Detection of Daily Behavior Pattern Based on Regularity Feature Extraction for Home Elderly

Abstract

Daily behavior irregularity detection is important for assessment of the health status for the elderly in homecare. This paper proposes a Daily Behavior Irregularity Detection (DBID) mechanism which outputs the irregularity probability of daily behaviors based on the extracted regularity features using unsupervised learning algorithm. The regular behaviors which satisfy the time-regular and frequency-regular properties are identified as the regularity of daily behaviors. Then, the irregularity probability of the daily behaviors in one days can be calculated based on the selected regular behaviors. Experiments show that the proposed DBID has a good performance in terms of F measure, compared the existing mechanisms.