Human Behavior Recognition in Retail Environments With Graph-Driven RFID Signal Embedding

Abstract

With the rapid development of the retail industry, enhancing customer experience and operational efficiency has become increasingly critical, where technological integration is key. This study introduces an innovative framework for human behavior recognition that combines graph neural network (GNN) and radio frequency identification (RFID) technology. By embedding RFID signals into the graph structure, we effectively capture the spatial dependencies hidden in the data. Furthermore, the spline convolution technique is utilized to address the spatial dependencies of the signals, achieving accurate and robust human behavior recognition. Facing challenges such as dynamic changes in data dimensions in the retail environment, over-smoothing issues in GNNs, and the effective fusion of multidimensional features, we adopted a graph-based modeling approach. We constructed an adjacency matrix with small-world characteristics using the TopK mechanism and Pearson correlation coefficients, and introduced inception structures and residual connections to increase network width, thereby mitigating over-smoothing phenomena. The introduction of bidirectional long short-term memory network (BiLSTM) readout methods further enhanced the model’s ability to process time series information. Experimental results demonstrate the framework’s excellent performance in human behavior recognition tasks, with high accuracy and strong robustness, proving not only theoretically feasible but also highly effective in practical applications. Through qualitative analysis, we have improved the interpretability of the framework, providing retailers with a powerful tool for gaining in-depth insights into customer behavior, which helps to optimize customer experience and enhance operational efficiency.