Predicting Floor Level For 911 Calls with Neural Network and Smartphone Sensor Data
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 important 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.
In this work there are two major contributions. The first is that they trained a recurrent neural network to classify whether a smartphone was either inside or outside of a buildings. The second contribution is that they used the output of their previously trained classifier to aid in predicting the change in the barometric pressure of the smartphone from once it entered the building to its current location. In the final part of their algorithm they are able to predict the floor level by clustering the measurements of height.
In general, previous work falls under two categories. The first category of methods are classification methods based on the user's activity. Therefore, some current methods leverages the user's activity to predict which is based from the offset in their movement . These activities include running, walking, and moving through the elevator. The second set of methods focus more on the use of a barometer which measures the atmospheric pressure. As a result utilizing a barometer can provide the changes in altitude.
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