Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
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发表时间：
2017-10
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通讯作者：
William Falcon;H. Schulzrinne
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作者：
William Falcon;H. Schulzrinne
在拥有高楼大厦的城市，急救人员需要准确的楼层位置才能快速找到911呼叫者。我们介绍了一个系统，通过受害者移动设备的传感器数据，分两步估计受害者的地板水平。首先，我们训练神经网络，通过GPS信号的变化来确定智能手机何时进入或离开大楼。其次，我们使用配备气压计的智能手机来测量从建筑物入口处到受害者室内位置的气压变化。与以前不切实际的方法不同，我们的系统是第一个不需要使用信标、预先了解建筑基础设施或了解用户行为的系统。我们通过在纽约市五座不同的高层建筑上进行的63次实验证明了现实世界的可行性，在这些实验中，我们的系统预测了正确的楼层水平，准确率为100%。
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.