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Follow @grandma: Long-term device-free localization for residential monitoring

机译:跟随@grandma:用于住宅监控的长期无设备本地化

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Device-free localization (DFL) enables localizing people by monitoring the changes in the radio frequency (RF) attenuation field of an area where a wireless network is deployed. Notably, this technology does not require people to participate in the localization effort by carrying any electronic device. This paper presents a DFL system for long-term residential monitoring. Due to the daily activities carried out by the people being monitored, the radio signals' propagation patterns change continuously. This would make a system relying only on an initial calibration of the radio environment highly inaccurate in the long run. In this paper, we use an on-line recalibration method that allows the system to adapt to the changes in the radio environment, and then provide accurate position estimates in the long run. A finite-state machine (FSM) defines when the person is located at specific areas-of-interest (AoI) inside the house (e.g. kitchen, bathroom, bed, etc.). Moreover, each time a state transition is triggered, the system tweets the new AoI in a Twitter account. The FSM allows extracting higher level information about the daily routine of the person being monitored, enabling interested parties (e.g. caretakers, relatives) to check that everything is proceeding normally in his life. In the long-term experiment carried out in a real domestic environment, the system was able to accurately and reliably localize the person during his daily activities.
机译:无需设备定位(DFL)可以通过监视部署无线网络的区域的射频(RF)衰减场的变化来对人员进行定位。值得注意的是,该技术不需要人们通过携带任何电子设备来参与本地化工作。本文提出了一种用于住宅长期监控的DFL系统。由于被监视人员的日常活动,无线电信号的传播方式不断变化。从长远来看,这将使系统仅高度依赖于无线电环境的初始校准会非常不准确。在本文中,我们使用一种在线重新校准方法,该方法可使系统适应无线电环境的变化,然后从长远角度提供准确的位置估计。有限状态机(FSM)定义人员何时位于房屋内的特定兴趣区域(AoI)(例如厨房,浴室,床等)。此外,每次触发状态转换时,系统都会在Twitter帐户中发布新的AoI。 FSM允许提取有关被监视者的日常活动的更高级别的信息,从而使感兴趣的各方(例如看护人,亲戚)能够检查自己一生中一切是否正常进行。在真实的家庭环境中进行的长期实验中,该系统能够准确,可靠地定位该人的日常活动。

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