Simulation of Human Behavior Recognition Based on WiFi Signal

WiFi (wireless fidelity) signals, renowned for their extensive coverage, absence of electromagnetic pollution, and robust penetration capabilities, are exceptionally well suited for serving as an external radiation source in target detection and environmental perception applications. The current paper delves into the viability of leveraging WiFi signals for the purpose of human behavior recognition. Initially, the paper elucidates the distinctive attributes of typical WiFi signals. Subsequently, it formulates a parametric mathematical model to represent human walking, encompassing an analysis of several prevalent translational and rotational motions. Building upon this human body kinematic model, this study generates echo data corresponding to human walking patterns. A comprehensive simulation, analysis, and validation of the micro-Doppler characteristics associated with various body parts and the whole body in motion are then conducted. The findings from these simulations and analyses affirm the efficacy of the proposed methodology.