Real-Time Anomaly Detection in Elderly Behavior

The rapid growth of the aging population and the increasing cost of the hospitalization are arousing the urgent need of the remote health monitoring system. Using the physiological sensing devices enable early detecting of health issues and allow for prompt treatment to help elderly towards changing their anomalous behavior and having a healthy lifestyle. Our approach, exploited task models to produce scenarios (which is the expected user behavior) and a middleware software, Context Manager to detect the events happened in the real context. Later, our real-time algorithm compares the expected user behavior to the real one detected in user context to find the anomalies if there is any. Finally, we validated our approach via a simulator, which automatically generates the anomalous sequences of user activities. The experimental results show that our system can detect abnormal user behavior precisely and effectively. Besides, the system should be able to generate proper action based on the detected deviation to motivate older people towards a healthy lifestyle.