High-Accuracy and Fine-Granularity Human Activity Recognition Method Based on Body RFID Skeleton

Abstract

Human Activity Recognition (HAR) has attracted much attention due to the application in consumer electronics. To address the shortcomings of existing HARs, such as no identification, privacy leakage, battery maintenance, low accuracy, and few recognized activities, we have proposed new bound-RFID HARs. Firstly, we have devised body RFID skeleton to sense human activity by collecting tag response records of the skeleton node, which form the human activity dataset suitable for a benchmark. Secondly, we propose data standardization to solve the problem caused by the differences in the size and the amount of the tag response signal feature data. Thirdly, some body RFID skeleton-based HARs are proposed by utilizing machine learning, recurrent neural networks, and graph convolutional networks. In these proposed HARs, the best HAR is an improvement on the state-of-the-art HAR in the computer vision. The experimental results show that the proposed best HAR improves 0.27% than the state-of-the-art HAR by utilizing RSSI, Phase, and Doppler Frequency with only 4.5MB parameters, and achieves a recognition accuracy of 98.52%. Therefore, the devised body RFID skeleton effectively senses human activities, and the proposed HAR recognizes human activities with high accuracy and fine granularity. The dataset and source code are available at http://www.cwnuiot.net/BRS/.