# Location Adaptive Motion Recognition Based on Wi-Fi Feature Enhancement
###### Abstract  
Action recognition is essential in security monitoring, home care, and behavior analysis. Traditional solutions usually leverage particular devices, such as smart watches, infrared/visible cameras, etc. These methods may narrow the application areas due to the risk of privacy leakage, high equipment cost, and over/under-exposure. Using wireless signals for motion recognition can effectively avoid the above problems. However, the motion recognition technology based on Wi-Fi signals currently has some defects, such as low resolution caused by narrow signal bandwidth, poor environmental adaptability caused by the multi-path effect, etc., which make it hard for commercial applications. To solve the above problems, we first propose and implement a position adaptive motion recognition method based on Wi-Fi feature enhancement, which is composed of an enhanced Wi-Fi features module and an enhanced convolution Transformer network. Meanwhile, we improve the generalization ability in the signal processing stage to avoid building an extremely complex model and reduce the demand for system hardware. To verify the generalization of the method, we implement real-world experiments using 9300 network cards and the PicoScenes software platform for data acquisition and processing. By contrast with the baseline method using original channel state information(CSI) data, the average accuracy of our algorithm is improved by 14% in different positions and over 16% in different orientations. Meanwhile, our method has best performance with an accuracy of 90.33% compared with the existing models on public datasets WiAR and WiDAR. human motion recognition; channel state information; multi-signal classification algorithm; wireless perception +
Footnote †: journal: Medical  
of source signals in space and traverses the spectral function values corresponding to the incidence angles. Then, the maximum value of them can be used to deduce the source position. However, the MUSIC algorithm is mainly used in the field of radar direction finding, which requires a huge antenna array for accurate estimation. For common Wi-Fi devices, the number of antennas and bandwidth are limited, and the estimation accuracy will be greatly reduced when applied to human body location scenes due to the multi-path effect. Moreover, among all the parameters of the signal affected by human actions (Time of Flight, Angle of Arrival, Doppler Frequency Shift (DFS) and Received Signal Strength Information (RSSI)), Doppler Frequency Shift reflects the human action speed information best [19]. When the human body is running, walking, or any other actions, the movement distance is usually more than 2m, and DFS changes significantly. Nevertheless, for the actions with small displacement such as standing up and sitting down, DFS changes slightly, which makes it difficult to estimate the velocity distribution of such actions. From above considerations, a position-adaptive motion recognition method based on Wi-Fi feature enhancement is proposed to solve the multi-path effect under constrained conditions and improve the generalization ability of Wi-Fi motion recognition. Taking the human body as the reference, the three-dimensional velocity distribution independent of the human body's position is extracted from the Doppler frequency shift as the feature to enhance its generalization performance of the position. In the action classification stage, a dynamic convolution transformer network is designed to realize action classification, which is used to improve the ability to extract local features and classify different actions. Ultimately, we implement improving motion classification performance through Wi-Fi signal with good generalization ability and high accuracy. Our main contributions can be summarized as follows:  
(1) Based on the MUSIC algorithm, a novel method adding time of flight (ToF) and the offset parameters is proposed to improve the positioning accuracy to obtain an accurate human position.