Real-Time Anomaly Detection for Traveling Individuals

We study real-time anomaly detection in a context that considers user trajectories as input and tries to identify anomaly for users following normal routes such as taking public transportation from the workplace to home or vice versa. Trajectories are modeled as a discrete-time series of axis-parallel constraints ("boxes") in the 2D space. The incremental comparison between two trajectories where one trajectory has the current movement pattern and the other is a norm can be calculated according to similarity between two boxes. The proposed system was implemented and evaluated with eight individuals with cognitive impairments. The experimental results showed that recall was 95.0% and precision was 90.9% on average without false alarm suppression. False alarms and false negatives dropped when axis rotation was applied. The precision with axis rotation was 97.6% and the recall was 98.8%. The average time used for sending locations, running anomaly detection, and issuing warnings was in the range of 15.1 to 22.7 seconds.