Device-free Multiple People Localization through Floor Vibration
Device-free Multiple People Localization through Floor Vibration
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DOI：
10.1145/3360773.3360887
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发表时间：
2019-11
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通讯作者：
Laixi Shi;Mostafa Mirshekari;Jonathon Fagert;Yuejie Chi;H. Noh;Pei Zhang;Shijia Pan
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文献类型：
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作者：
Laixi Shi;Mostafa Mirshekari;Jonathon Fagert;Yuejie Chi;H. Noh;Pei Zhang;Shijia Pan
基于结构振动的人体感知为无设备人体监测提供了一种替代方法，用于医疗保健，空间和能源使用管理等。这种方法的先前工作主要集中在一个人行走的场景中，这限制了其广泛的应用。多个步行者的挑战在于，观察到的振动响应是每个步行者的脚步引起的响应的混合，并且难以识别1）存在多少个并行步行者，以及2）他们的脚步撞击地板的时间。结果，每个步行者的详细位置信息的提取是错误的。为了解决这一挑战，我们提出了一种结构通知振动信号表征方法，使重叠的振动信号的检测和定位引起多个并发的步行者。直觉是，由于人们行为的随机性，他们的脚步不会完全同时撞击地板，而是部分重叠。我们将信号分解到一个非基频带，其中包含脚跟撞击开始信息。利用这个分解的信号，我们可以识别步行者的数量，并使用初始峰值信息来独立地定位每个人。我们进行了真实世界的实验，最多有三个并发的步行者，我们的系统实现了高达90%的检测率和0.65米的平均定位误差（2.9倍基线改进）。
Structural vibration-based human sensing provides an alternative approach for device-free human monitoring, which is used for healthcare, space and energy usage management, etc. Prior work on this approach mainly focused on one person walking scenarios, which limits their widespread application. The challenge with multiple walkers is that the observed vibration response is a mixture of each walker's footstep-induced response, and it is difficult to identify 1) how many concurrent walkers are present, and 2) the timing of their footstep impacts on the floor. As a result, the extraction of detailed location information for each walker is erroneous. To address this challenge, we propose a structure-informed vibration signal characterization method to enable the detection and localization of overlapping vibration signals induced by multiple concurrent walkers. The intuition is that, due to the randomness in people's behavior, their footsteps do not impact the floor exactly at the same time and overlap partially. We decompose the signal to a non-fundamental frequency band which contains the heel strike onset information. With this decomposed signal, we can identify the number of walkers and use the initial peak information to localize each person independently. We conducted real-world experiments with up to three concurrent walkers and our system achieved a detection rate of up to 90% and an average localization error of 0.65m (2.9X baseline improvement).