RFID-Based Indoor Human Behavior Recognition Using SATCN: A Self-Attention Enhanced Temporal Convolutional Network

Zhenchao Chen, Mulan Yang, Xuehan Hou, Lvqing Yang, Wensheng Dong, Bo Yu, Qingkai Wang

Abstract: In the field of indoor human behavior recognition, traditional deep learning methods often underperform in adequately capturing temporal features and core information in RFID behavior data, leading to suboptimal predictive performance. We note that human behavior is primarily composed of complex action sequences, where certain sequences formed by Received Signal Strength Indicator (RSSI) values may exhibit more representative and information-rich patterns. Therefore, we propose SATCN: an innovative model that leverages Temporal Convolutional Networks (TCN) and self-attention mechanisms for advanced RFID-based human behavior recognition. The SATCN model employs TCN to adeptly capture the inherent temporal dependencies within behavior data, while integrating self-attention mechanisms to focus on high-value features in the data. Experimental validation on RFID indoor human behavior recognition dataset reveals that SATCN achieves an accuracy of 94.51%, a 10.83 percentage point increase over existing models, highlighting SATCN’s potential in enhancing the accuracy of RFID-based indoor human behavior recognition.