Research on Human Posture Recognition System Using Wireless Signals Based on Deep Neural Networks

In recent years, the rapid development of applications such as smart homes and health monitoring has led to a gradual increase in research interest in the use of wireless signals for non-contact human posture recognition. In this study, a human posture recognition system based on wireless signals and a deep neural network was proposed. This system was designed to accurately recognize different postures by analyzing the perturbation characteristics of wireless signals during movement. In particular, the system initially generates signal timing data by acquiring reflection and scattering information from wireless signals (such as Wi-Fi). It then employs signal pre-processing technology to eliminate environmental noise. Subsequently, a deep neural network model was designed to extract high-dimensional features from the signals. The model structure employed a long short-term memory network (LSTM) to capture the change patterns of human actions in the spatiotemporal dimension. The network is trained on a substantial corpus of annotated data, which enables it to learn intricate mapping relationships between attitude and signal alterations. The experimental results demonstrate that the system exhibits high accuracy in a multitude of complex environments, particularly in non-line-of-sight (NLOS) scenarios.