Difference between revisions of "Predicting Floor Level For 911 Calls with Neural Network and Smartphone Sensor Data"

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(Introduction)
(Introduction)
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In high populated cities,  where there are many buildings locating individuals in the case of an emergency is an important task. For emergency responders, time is of the essence. Therefore, accurately locating a 911 caller plays an integral role in this process.
 
In high populated cities,  where there are many buildings locating individuals in the case of an emergency is an important task. For emergency responders, time is of the essence. Therefore, accurately locating a 911 caller plays an integral role in this process.
  
The motivation for this problem in the context of 911 calls:  Victims trapped in a tall building who seeks immediate medical attention, locating emergency personnel such as firefighters or paramedics, or a minor calling on behalf of an incapacitated adult. In this paper they present a novel approach to accurately predicting floor level for 911 calls by leveraging neural networks and sensor data from smartphones.
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The motivation for this problem in the context of 911 calls:  Victims trapped in a tall building who seeks immediate medical attention, locating emergency personnel such as firefighters or paramedics, or a minor calling on behalf of an incapacitated adult. In this paper a novel approach is presented to accurately predict floor level for 911 calls by leveraging neural networks and sensor data from smartphones.
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In large cities with tall buildings, relying on GPS or Wi-Fi signals are not able to to provide an accurate location of a caller.
  
 
=Related Work=
 
=Related Work=

Revision as of 21:27, 6 November 2018


Introduction

In high populated cities, where there are many buildings locating individuals in the case of an emergency is an important task. For emergency responders, time is of the essence. Therefore, accurately locating a 911 caller plays an integral role in this process.

The motivation for this problem in the context of 911 calls: Victims trapped in a tall building who seeks immediate medical attention, locating emergency personnel such as firefighters or paramedics, or a minor calling on behalf of an incapacitated adult. In this paper a novel approach is presented to accurately predict floor level for 911 calls by leveraging neural networks and sensor data from smartphones.

In large cities with tall buildings, relying on GPS or Wi-Fi signals are not able to to provide an accurate location of a caller.

Related Work

Data Description

Methods

Future Work

References