# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 2 Related Work
### Wi-Fi Through-the-Wall  
Although there has been much research on human sensing for CSI, through-the-wall applications still need to be improved compared to non-through-the-wall applications. This is because Wi-Fi signals are more severely attenuated after through-the-wall, which has a complex impact on recognition accuracy, and through-the-wall is still facing tremendous difficulties and challenges, so there is less technical and related research work on Wi-Fi through-the-wall for human detection. However, through-the-wall sensing technology enables us to monitor human activity more effectively and accurately, and many potential applications would benefit from powerful capabilities across different rooms, such as elderly monitoring, intruder detection, and emergency rescue. In the actual wall-penetrating environment, as there is no enclosed space, Wi-Fi signals may cross to the other side of the wall through various gaps in the environment (e.g., door cracks, small holes, etc.) through reflection, refraction, scattering, diffraction, etc. Therefore, it is critical to determine the method that can penetrate the wall and sense the target. Furthermore, Wi-Fi devices provide a good alternative.  
[PERSON] et al. [12] proposed a Wi-Fi-based system for device-less behavioral recognition of wall penetration, which recognizes behavioral activities by extracting and analyzing the amplitude values of the subcarriers in the wireless channel and classifying the preprocessed activity samples using Bi-LSTM. [PERSON] et al. [13] proposed a crowd base counting system using Wi-Fi signals, which utilizes commercially available Wi-Fi devices to extract the phase difference data of channel state information (CSI), removes the uncorrelated noise, extracts the feature group by combining the subcarrier correlation, and then uses a BP neural network to realize human detection and head counting through-the-wall. [PERSON] et al. [14] proposed a new system that extracts finer features from the time off (ToF) of the signal and then trains a neural network to classify these features to determine if there is a stationary person behind the wall. Experiments were conducted in a typical office, and good performance was achieved. [PERSON] et al. proposed a device-free through-the-wall human detection and localization system, TWPalo [15], by iterating the obtained AoA, ToF, and DFS, all with channel reconstruction and pair-cancellation, so that the CSI of each propagation path could be separated. Finally, the human-induced reflection (HIR) parameters and an ellipse-based model are established by the obtained parameters and the spatial geometric relationship between the human movement and the position of the transceiver pair to achieve human detection and localization in the through-the-wall scenario, where the experiments are conducted mainly in conference rooms and offices using wall materials of glass and concrete.  
Although the articles mentioned earlier on the perception of Wi-Fi through-the-wall have achieved good results, many experiments have been conducted in austere environments. Therefore, these experiments only sometimes guarantee the robustness of the system. However, in wall-penetrating scenarios, many factors, such as wall material and thickness, and changes in personnel positions, can significantly affect recognition performance. However, many studies have yet to analyze this problem in experiments, so we will design multi-class comparative experiments to verify the reliability of the system proposed in this paper.