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.
Highlights
According to statistics [19] from a 1999 national survey reported on the Keep Schools Safe website, more than one in three high school students had been in a physical fight within the previous year
We develop an unusual event classification model for real world mobile users, that is automated on the smartphone, and analyze various binary fusion classification algorithms to find the best one for use with our system
We investigated the seven behavior patterns of our mobile users, measured by the audio
Summary
According to statistics [19] from a 1999 national survey reported on the Keep Schools Safe website, more than one in three high school students had been in a physical fight within the previous year. In an emergency situation, people are often too injured, or incapable, of calling 911 or a relative to get help They might be involved in a fight, violent attack, health emergency, or trapped in a vehicle or structure. To classify normal and abnormal events, we analyzed the activity, location, and audio sensor data collected from 20 mobile phone users to identify these users’ personalized regular daily behavior patterns and any unusual events occurring in their daily behavior patterns. We built behavior classifiers to identify each mobile user’s daily activity patterns (e.g., walking, stationary, running), audio patterns (e.g., low level sound, music, talking, loud emotional voice), and location data that was collected from the mobile sensors of the 20 mobile users.
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