RFAR: Action Recognition Based on Single Tag

Human action recognition in classrooms has recently become a research hotspot. Traditional solutions usually rely on sensors or computer vision methods. However, these methods have some disadvantages, such as difficulty in deployment, susceptibility to ambient light, and privacy and security issues. This paper proposes RFAR, a contactless method for classroom action recognition. This method utilizes an RFID tag placed on the desktop to capture various actions and subsequently evaluate the student's learning status. To enhance the reliability of singletag identification, fused data consisting of two or three types of data sequences (RSSI, phase, and Doppler shift) are incorporated. Furthermore, a dynamic antenna system is utilized to identify the optimal angle for tag-antenna alignment. Notably, the single-tagper-person design eliminates severe interference among multiple tags and simplifies device deployment in multi-person scenarios. This method is proposed based on COTS RFID devices and shows high robustness across different environments and equipment. Experimental results show a recognition accuracy of 93.9% in single-person scenarios and 81.5% in five-person scenarios.