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.
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A few motivations for this problem is in the case of 911 emergency calls:  For example, 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 a 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.
  
 
=Related Work=
 
=Related Work=

Revision as of 21:22, 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.

A few motivations for this problem is in the case of 911 emergency calls: For example, 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 a 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.

Related Work

Data Description

Methods

Future Work

References