CWNU-RDA

Human activity recognition (HAR) has attracted much attention. However, the existing HARs have shortcomings, such as few recognized activities, no identification, privacy leakage, and battery maintenance. Aiming at these shortcomings, this team has devised a body RFID skeleton that fully senses human activity and further proposes highly-accurate and fine-grained (total of 21 activities) HARs. The body RFID skeleton senses human activity by collecting tag response records of the skeleton node. The tag response records form the human activity dataset which is suitable for benchmarking the bound-RFID HAR. The proposed data standardization solves the problems caused by the differences in the size and the amount of the tag response signal feature data. Some body RFID skeleton-based HARs are proposed by utilizing machine learning and deep learning. Experiments show the validity of the body RFID skeleton-based HAR when faced with highly accurately recognizing fine-grained activities, that the body RFID skeleton-based HAR utilizing BiLSTM has the best accuracy (97.71%) among the proposed HARs, and that the body RFID skeleton-based HAR is easier to recognize activity which is expressed by RSSI and Phase. The proposed HARs can be used in consumer electronics such as healthcare. The dataset and source code are available at http://www.cwnuiot.net/BRS/.