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Monitoring the spatial movement of devices to detect disasters

IP.com Disclosure Number: IPCOM000247467D
Publication Date: 2016-Sep-09
Document File: 2 page(s) / 58K

Publishing Venue

The IP.com Prior Art Database

Abstract

This article describes a method of profiling user's typical activity so that when there is a sudden change in activity for many people in one area, an event is triggered. An epicentre of the event is found and automates notifying emergency services of the location to attend, and the radius affected.

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Monitoring the spatial movement of devices to detect disasters

In unpredictable disasters, the speed of detection for the emergency services and other organisations is vital. In some cases the response time is limited to individuals reporting the disaster location, which may not happen until a while after the event. This idea helps to speed up the identification of disasters by highlighting the rapid or unusual movement of people away from locations.

    The process begins by building a profile of individuals movement patterns. E.g. some people are much more sedentary and less likely to run. When an individual is moving in a different way to their profile, an event is triggered and logged. When events across a number of individuals occur at the same time in the same location, the system flags up a potential disaster site. The direction of movement of the individuals is logged to try to identify the epicenter of the disaster, and alert the authorities immediately. This method is advantageous because it is quicker at reporting events than individuals, who will often focus on saving

themselves first before reporting the event. Also, panic and confusion in disasters can lead to human error in reporting the exact location of the disaster.

    There are two major components to this method - the first being the use of a phones accelerometer to see if a person's movement is unusual, and the second being the use of GPS to pinpoint locations. The accelerometer will be able to tell how a person is moving eg. using the up-down movement of the phone to tell if the person is running quickly, and will compare the pattern of movement to a profile for that individual that has been built up over time. The novel part comes with tracking this and flagging when a number of people are moving in an unusual way to their profiles at the same time.

    In the event of a disaster you would see this flag and then look at the people's GPS movements to try to predic...