Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data

TL;DR

This paper presents a novel neural network-based system that accurately predicts the floor level of 911 callers in tall buildings using only smartphone sensor data, without requiring infrastructure modifications.

Contribution

We introduce a two-step system combining GPS and barometric data analysis that does not rely on beacons or prior building knowledge, demonstrating real-world effectiveness.

Findings

Achieved 100% accuracy in predicting floor levels across five NYC buildings.

System operates without beacons or prior infrastructure knowledge.

Validated through 63 real-world experiments.

Abstract

In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a building via GPS signal changes. Second, we use a barometer equipped smartphone to measure the change in barometric pressure from the entrance of the building to the victim's indoor location. Unlike impractical previous approaches, our system is the first that does not require the use of beacons, prior knowledge of the building infrastructure, or knowledge of user behavior. We demonstrate real-world feasibility through 63 experiments across five different tall buildings throughout New York City where our system predicted the correct floor level with 100% accuracy.