Recursive Sparse Representation for Identifying Multiple Concurrent Occupants Using Floor Vibration Sensing
Recursive Sparse Representation for Identifying Multiple Concurrent Occupants Using Floor Vibration Sensing
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DOI：
10.1145/3517229
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发表时间：
2022-03-01
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通讯作者：
Noh, Hae Young
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作者：
Fagert, Jonathon;Mirshekari, Mostafa;Noh, Hae Young
在本文中，我们提出了一个多个并发的乘员识别方法，通过脚步引起的地板振动传感。人类居住者的识别在各种室内智能结构场景中是有用的，在建筑安全、空间分配和医疗保健中具有应用。现有方法利用诸如视觉、声学、RF和可穿戴设备的感测模态，但是由于诸如视线要求、对噪声的敏感性、密集传感器部署以及要求每个步行者穿戴/携带设备的部署约束而受到限制。为了克服这些限制，我们使用脚步感应结构振动传感。脚步感生信号包含关于居住者的独特步态特征的信息，并且通过结构介质传播，这使得能够稀疏和被动地识别室内居住者。主要的研究挑战是，多人的脚步引起的振动响应是一个混合的结构相互依赖的重叠的个人响应与未知的时间，频谱内容，和混合比。因此，难以确定信号的哪个部分对应于每个乘员。我们通过基于余弦距离的递归稀疏表示方法克服了这一挑战，该方法以其信号生成的顺序识别脚步事件中的每个占用者，重建其信号部分，并将其从混合响应中移除。通过利用稀疏表示，我们的方法可以同时识别和分离混合/重叠的响应，并且使用余弦距离误差函数减少了结构相关性对多个步行者信号的影响。通过这种方式，我们隔离并识别多个居住者的脚步响应中的每一个。我们评估我们的方法进行真实世界的步行实验与三个并行步行者，并实现了平均F1得分为0.89识别所有的人（1.3倍基线改善），以及10人“混合”数据集（模拟的单步行者真实世界数据的组合），我们确定了2，3，和4个并发步行者，跟踪级准确率为100%，93%，和73%，并观察到与朴素基线方法相比多达2.9倍的误差减少。
In this paper, we present a multiple concurrent occupant identification approach through footstep-induced floor vibration sensing. Identification of human occupants is useful in a variety of indoor smart structure scenarios, with applications in building security, space allocation, and healthcare. Existing approaches leverage sensing modalities such as vision, acoustic, RF, and wearables, but are limited due to deployment constraints such as line-of-sight requirements, sensitivity to noise, dense sensor deployment, and requiring each walker to wear/carry a device. To overcome these restrictions, we use footstep-induced structural vibration sensing. Footstep-induced signals contain information about the occupants' unique gait characteristics, and propagate through the structural medium, which enables sparse and passive identification of indoor occupants. The primary research challenge is that multiple-person footstep-induced vibration responses are a mixture of structurally-codependent overlapping individual responses with unknown timing, spectral content, and mixing ratios. As such, it is difficult to determine which part of the signal corresponds to each occupant. We overcome this challenge through a recursive sparse representation approach based on cosine distance that identifies each occupant in a footstep event in the order that their signals are generated, reconstructs their portion of the signal, and removes it from the mixed response. By leveraging sparse representation, our approach can simultaneously identify and separate mixed/overlapping responses, and the use of the cosine distance error function reduces the influence of structural codependency on the multiple walkers' signals. In this way, we isolate and identify each of the multiple occupants' footstep responses. We evaluate our approach by conducting real-world walking experiments with three concurrent walkers and achieve an average F1 score for identifying all persons of 0.89 (1.3x baseline improvement), and with a 10-person "hybrid" dataset (simulated combination of single-walker real-world data), we identify 2, 3, and 4 concurrent walkers with a trace-level accuracy of 100%, 93%, and 73%, respectively, and observe as much as a 2.9x error reduction over a naive baseline approach.