Personalized Behavior Pattern Recognition and Unusual Event Detection for Mobile Users

Abstract

Mobile phones have become widely used for obtaining help in emergencies, such as accidents, crimes, or health emergencies. The smartphone is an essential device that can record emergency situations, which can be used for clues or evidence, or as an alert system in such situations. In this paper, we focus on mobile-based identification of potentially unusual, or abnormal events, occurring in a mobile user's daily behavior patterns. For purposes of this research, we have classified events as for a mobile user when an event is an infrequently occurring one from the user's normal behavior patterns --all of which are collected and recorded on a user's mobile phone. We build a general unusual event classification model to be automated on the smartphone for use by any mobile phone users. To classify both normal and unusual events, we analyzed the activity, location, and audio sensor data collected from 20 mobile phone users to identify these users' personalized normal daily behavior patterns and any unusual events occurring in their daily activity. We used binary fusion classification algorithms on the subjects' recorded experimental data and ultimately identified the most accurately performing fusion algorithm for unusual event detection.