# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 1 Introduction
After Wi-Fi signals pass through-the-wall, they are subjected to interference from wall materials and multiple propagation paths behind the walls, resulting in severe signal attenuation and affecting detector accuracy. Existing through-the-wall detection methods rely on densely distributed transmitters and receivers or require specialized signal transmission equipment, which is unsuitable for commercial devices. Many existing device-free Wi-Fi human detection systems based on CSI experience significant performance degradation in through-the-wall scenarios. Therefore, utilizing CSI variations for human perception in through-the-wall conditions is a challenging problem. To solve the problem, Wi-Fi signals through-the-wall increase the difficulty of human detection. In this paper, we propose a highly robust through-the-wall human detection method based on a ubiquitous commercial Wi-Fi device (TwSense). The robust principal component analysis algorithm (OR-PCA) [8] removes the complex noise caused by walls from the acquired data. Thus, we extracted CSI's correlation and selected the subcarriers with significant features for conversion to Doppler frequency shift images. We then used a clustering algorithm (K-means) to segment the Doppler shift images based on the motions. Figure 1: Indoor passive mode human detection. Then, the feature vectors obtained by the HOG algorithm were fed into the G-SVM classifier to enable human detection through-the-wall. For four materials of walls: concrete, plaster, wooden door, and glass, we analyzed the effects of different parameter settings (different personnel, thickness, personnel position, and device spacing) on the experimental results and evaluated the robustness of the whole system. The experimental results showed that TwSense had high detection accuracy. At the same time, based on the experimental results, we provided a better deployment scheme for future practical through-the-wall applications. In summary, the main contributions of the paper are as follows:  
* We proposed a highly robust method for through-the-wall human detection based on a ubiquitous commercial Wi-Fi device (TwSense). This method used the passive mode to detect the presence of people in the room using the CSI in the Wi-Fi signal. It also provided a solution for the emergency rescue and health monitoring of older people. * In this paper, we adopted the OR-PCA method to extract the correlation of CSI, eliminated the noise generated from other obstacles, such as walls, used the clustering algorithm to segment the Doppler-shifted images caused by motion and then the HOG algorithm to obtain the critical features of the images, and finally fed them into the SVM classifier optimized by the grid search algorithm (G-SVM) for motion classification. This method not only distinguished the indoor personnel state (unoccupied, occupied) well but also improved the accuracy of human body detection in the case of through-the-wall detection. * We used commercially available Wi-Fi devices to collect various data for different wall materials and thicknesses, as well as for different personnel locations and device distances. The reliability and stability of the system were verified by adjusting various parameters. The final experimental results provided usage boundaries and deployment scenarios for through-the-wall practical applications. The rest of this article is organized as follows. The second part introduces the related work and research; the third part introduces the related technical theory; the fourth part describes the system structure and design in detail; the fifth part evaluates and analyzes the experiments; and the sixth part summarizes the whole work and proposes a future outlook.