WiFi has recently established itself as a powerful medium for radio frequency (RF) sensing due to its low cost and convenience. Many tasks, such as gesture recognition, activity recognition, and fall detection, can be implemented by measuring and calculating how the propagation of WiFi signals is affected by human activities. However, current WiFi-based sensing solutions have limited scales as they are designed for only a few activities and need to collect data and create training models in the same domain because the model established in a deployment environment is usually not applicable to new objects in the target domain. This paper presents TransferSense, an environment independent and one-shot WiFi sensing method based on deep learning. Firstly, amplitude and phase information of channel state information (CSI) are combined to increase the number of features to solve the problem of insufficient features due to single-source information. Secondly, TransferSense converts RF sensing tasks to image classification tasks and fuses low-level and high-level semantic features extracted from a pre-trained convolutional neural network to achieve an end-to-end high-precision sensing for activity recognition. Finally, TransferSense applies a transfer learning method with a small number of labeled samples in the target domain to perform high-precision cross-domain sensing, which can reduce the data collection cost in the target domain. We verified the effectiveness of TransferSense using two representative WiFi sensing applications, gait identification and sign recognition. In a single deployment environment, TransferSense achieved more than 97% human gait identification accuracy for 44 users and more than 81% sign language recognition for 100 isolated sign language words. In the case of new object recognition in the cross-domain sensing, TransferSense achieved more than 77% human gait identification accuracy for 10 new users, more than 88% sign language recognition for 10 new isolated sign language words, and more than 81% gesture identification for 2 new gestures.

由于其低成本和便利性，WiFi最近已将自身确立为射频（RF）感应的强大媒介。可以通过测量和计算WiFi信号的传播如何受到人类活动影响来执行许多任务，例如手势识别，活动识别和跌倒检测。但是，当前基于WiFi的传感解决方案规模有限，因为它们仅针对少数活动而设计，并且需要在同一域中收集数据并创建训练模型，因为在部署环境中建立的模型通常不适用于部署环境中的新对象。目标域。本文提出了TransferSense，这是一种基于深度学习的独立于环境的一次性WiFi感知方法。首先，信道状态信息（CSI）的幅度和相位信息被组合以增加特征的数量，以解决由于单源信息而导致的特征不足的问题。其次，TransferSense将RF传感任务转换为图像分类任务，并融合从预训练的卷积神经网络中提取的低级和高级语义特征，从而实现端到端的高精度活动识别。最后，TransferSense将转移学习方法应用于目标域中带有少量标记样本的转移学习方法，以执行高精度的跨域感知，从而可以降低目标域中的数据收集成本。我们使用两种具有代表性的WiFi感应应用（步态识别和信号识别）验证了TransferSense的有效性。在单一部署环境中，TransferSense对44个用户的步态识别准确性达到了97％以上，对于100个隔离的手语单词则达到了81％以上。在跨域感测中的新对象识别中，TransferSense对10个新用户的步态识别准确率达到77％以上，对10个新隔离手语单词的手语识别率达到88％以上，而手势率则超过81％识别2个新手势。