Efficient Transmission-Based Human Behavior Recognition Algorithm

Abstract

In the contemporary field of wireless sensing, passive sensing leveraging channel state information (CSI) has found widespread applications across diverse scenarios, including behavior recognition, keystroke recognition, breath detection, and indoor localization. To ensure optimal sensing performance, wireless devices often collect a substantial number of CSI packets. However, when these packets need to be transmitted to a server or the cloud for time series analysis, the transmission load on the passive sensing system escalates rapidly, thereby impeding the system’s real-time performance. To address this challenge, we introduce the KCS algorithm, a novel compressed sensing (CS) algorithm grounded in K-Singular Value Decomposition (KSVD). The primary objective of the KCS algorithm is to enable the efficient transmission of CSI data. Departing from the use of a universal sparse matrix in traditional CS, the KCS algorithm constructs an overcomplete sparse matrix. This construction not only substantially bolsters the sparse representation capacity but also fine-tunes the compression performance. By doing so, it ensures the secure and efficient transmission of data. We applied the KCS algorithm to human behavior recognition and prediction. The experimental outcomes reveal that even when the volume of CSI data is reduced by 90%, the system still attains an average accuracy of 90%. This showcases the effectiveness of the KCS algorithm in balancing data compression and recognition performance, offering a promising solution for realistic applications where efficient data transmission and accurate sensing are crucial.