Body RFID Skeleton-Based Human Activity Recognition Using Graph Convolution Neural Network

Ziyi Wang, Yihong Chen, Hao Zheng, Meng Liu, Ping Huang

Abstract: Human activity recognition (HAR) has become a hotspot. However, the existing HAR has some shortcomings, such as few recognized human activities, no identification, privacy leakage, and battery maintenance. Therefore, we design body RFID skeleton which fully senses the key features of human activity, and further propose human activity recognitions by taking advantage of RFID (privacy protection, identification, and battery-less maintenance). First, we devise RFID skeleton activity graph as human activity model. Second, we propose baseline RFID skeleton activity graph convolution network (FSCN) by using the graph convolution network (GCN), and FSCN classifies the RFID skeleton activity graph for human activity recognition. Third, to solve the tag response signal feature data over-smoothing and the different information aggregation degree in FSCN, we propose improved FSCN with the residual network (R-FSCN). Finally, we design the parallel RFID skeleton activity graph convolution network (PR-FSCN) for optimizing R-FSCN. Massive experiments show that PR-FSCN has comprehensive superiority to the existing HARs. As far as we know, this study is the first work that appropriately designs body RFID skeleton for HAR, and that successfully introduces GCN to investigate the new bound-RFID HAR with high recognition performance.