Abstract
This paper describes an anomaly behavior detection system for elderly people based on a temperature sensor. The proposed monitoring system automatically learns daily behavior patterns and detects anomaly behavior patterns. Some feature values such as position and posture are extracted from the captured temperature data and anomaly behavior is detected as an outlier by One-Class SVM with Virtual Outliers. Some experiments are performed to verify the effectiveness of the proposed system.
