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.

该研究介绍了一种通过智能手机的传感器数据估计受害者楼层位置的系统，首先利用神经网络确定智能手机进入或离开建筑物，然后利用装有气压计的智能手机测量从建筑物入口到受害者室内位置的气压变化，不需要信标、建筑基础设施的先验知识或用户行为知识，通过 63 次实验在纽约市五栋不同高层建筑中预测正确 100% 的楼层位置，实现了真实可行性。