Human sensing based on commodity Wi-Fi devices has become a promising technique in human tracking, gesture recognition, walking speed monitoring, in-home healthcare, etc. However, past human sensing systems usingWi-Fi capture limited information about humans. Hence in this letter, we try to make commodity Wi-Fi devices act as cameras to directly capture human poses, i.e., fine-grained human skeleton images. We use a synchronized camera to capture human skeletons as annotations for Wi-Fi signals and design a novel neural network to convert Wi-Fi signals into images. We utilize three transceivers coordinately and use amplitude and phase information of Channel State Information (CSI) jointly to improve the resolution of Wi-Fi signals. We also introduce a method to extract useful and accurate CSI corresponding to humans and construct CSI images which are input of the neural network. Experimental results show that commodity Wi-Fi devices can capture human poses almost as fine-grained as cameras.

中文翻译：

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从信号到图像：使用商品 Wi-Fi 捕捉精细的人体姿势

基于商用 Wi-Fi 设备的人体感应已成为人体跟踪、手势识别、步行速度监控、家庭医疗保健等领域的一项很有前途的技术。然而，过去使用 Wi-Fi 的人体感应系统捕获的有关人类的信息有限。因此，在这封信中，我们试图让商用 Wi-Fi 设备充当相机来直接捕捉人体姿势，即细粒度的人体骨骼图像。我们使用同步相机捕捉人体骨骼作为 Wi-Fi 信号的注释，并设计了一种新颖的神经网络将 Wi-Fi 信号转换为图像。我们协调使用三个收发器，并结合使用信道状态信息 (CSI) 的幅度和相位信息来提高 Wi-Fi 信号的分辨率。我们还介绍了一种提取与人类对应的有用且准确的 CSI 并构建作为神经网络输入的 CSI 图像的方法。实验结果表明，商用 Wi-Fi 设备几乎可以像相机一样精细地捕捉人体姿势。