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In the current research landscape, human pose recognition technology is increasingly gaining attention, particularly in the areas of interactive design, health monitoring, and security applications. However, traditional computer vision-based methods for human pose recognition rely heavily on lighting conditions and face limitations when dealing with complex environments. To overcome these constraints, this paper explores a method of human pose recognition based on wireless signal's Channel State Information (CSI). This method, unlike traditional computer vision technologies, does not depend on lighting conditions, making it more suitable for complex environments and offering better protection of user privacy. Traditional CSI-based methods for human pose recognition mainly employ Long Short-Term Memory (LSTM) neural networks, but these methods are limited in computational efficiency due to constraints in parallelization capabilities. To address this, the paper introduces a novel network architecture named “CSI Transformer,” which combines Temporal Convolutional Networks (TCN) and the Transformer architecture for efficient processing of CSI data. Initially, the data undergoes preliminary feature extraction using temporal convolutional networks, followed by deep feature analysis and pose prediction through the Vision Transformer architecture. Experimental results indicate that, compared to traditional LSTM-based methods, the CSI Transformer can recognize human poses more efficiently while ensuring accuracy and safeguarding data privacy. This study offers a new perspective and method for the application of wireless signals in human pose recognition, highlighting its significance in advancing related technologies and applications. Moreover, the introduction of the CSI Transformer provides an effective solution to issues such as low computational efficiency and privacy protection, suggesting a broader application prospect for wireless signal-based human pose recognition technology in the future.
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