# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 1 Introduction  
With the advancement of wireless technology, Wi-Fi-enabled intelligent devices (e.g., cameras, air conditioners, smart audio, etc.) have been deployed everywhere [1], with many Wi-Fi devices in homes and offices. No matter what environment we are in, the radio frequency (RF) signals emitted by these devices surround us all the time. Therefore, people can use ubiquitous Wi-Fi signals in their daily lives to enable applications for sensing purposes (such as monitoring the usage of public restrooms). This type of device-free sensing technology is currently gaining increasing attention. Research on human detection using Wi-Fi signals has received much attention recently. However, most of the early work was related to something other than through-the-wall detection. Recently, through-the-wall sensing has received much attention because it can monitor human activities without privacy and security [2]. With powerful capabilities across different rooms, through-the-wall sensing can provide an application solution for elderly monitoring, intruder detection, and emergency rescue [3]. Recent works on device-free human detection can be divided into two main categories: active mode and passive mode. The difference between the two is that the active mode requires outdoor devices to send RF signals indoors continuously. In contrast, the passive mode can always obtain the Wi-Fi signals sent by indoor Wi-Fi devices and thus conduct human detection, which can more easily obtain the state data of indoor personnel. The passive mode shown in Figure 1 allows easy acquisition of Wi-Fi signals sent by indoor Wi-Fi devices [4] for human detection. As the human body moves through the space between various Wi-Fi devices, theWi-Fi signal is refracted, reflected, and diffracted through the human body. The resulting signals are in a different state than those generated after passing directly through an interfering object. These signals have different levels of interference, based on which we can distinguish whether a person is in the room. Due to the movement of a person, the Wi-Fi signal may pass through a wall via the LOS path or the NLOS path, both of which receive different signal strengths. We can also roughly determine the location and status of a person based on this method. Regarding research on human body detection, traditional methods for human sensing and detection often require users to wear specialized wearable sensors [5] or use cameras, RFID [6], smartphones, and other devices. In these cases, the operations become more complicated, deployment costs increase, and people's mobility is significantly affected, causing considerable inconvenience to their daily activities and routines. Moreover, some devices are susceptible to environmental influences and restrictions and may even raise privacy concerns for users. Unlike traditional approaches based on vision and devices, wireless technology is used to sense and detect the state of the human body without installing more devices or wearable devices using ubiquitous Wi-Fi signals. The advantage of this approach is that Wi-Fi devices are very inexpensive and widely available. However, the complexity of indoor environments and the varying structures of walls can affect the effectiveness of person detection. Channel State Information (CSI) is a fine-grained measurement of the physical layer that contains the amplitude and phase information of each orthogonal subcarrier in the channel [7]. CSI obtained from Wi-Fi signals may be more suitable for human detection. However, most relevant research focuses on indoor human perception, recognition, and detection using Wi-Fi signals. There has been relatively little research on human perception after Wi-Fi signals have penetrated through-the-wall or other obstacles. The reason for this is that through-the-wall human detection poses significant challenges.