Title
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 "unusual" 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.
Year
DOI
Venue
2013
10.3233/MIS-130155
Mobile Information Systems
Keywords
Field
DocType
unusual event detection,mobile user,daily activity,unusual event,mobile phone,personalized behavior pattern recognition,abnormal event,normal behavior pattern,general unusual event classification,mobile phone user,daily behavior pattern
Mobile computing,Behavioral pattern,Binary classification,Computer science,Computer security,Computer network,Human–computer interaction,Behavioral analysis,Mobile phone,Statistical classification,Personalization
Journal
Volume
Issue
ISSN
9
2
1574-017X
Citations 
PageRank 
References 
2
0.39
20
Authors
2
Name
Order
Citations
PageRank
Junho Ahn1736.61
Richard Han22771200.83