Wear-free gesture recognition based on residual features of RFID signals

Abstract

Traditionally, RFID is frequently used in identification and localization. In this paper, an extension application of RFID is designed to recognize gestures. Currently, gesture recognition is mainly used for feature extraction through wearable sensors and video cameras, which have shortcomings such as inconvenience to carry and interference with obstacles. This paper proposes a gesture recognition system based on radio frequency identification (RFID), where users do not need to wear devices. In the proposed model, the interference information generated by the gesture action on the tag signal is used as the fingerprint feature of the action. To obtain satisfactory recognition, the signal diversity is first increased through the tag array. Then, the RSSI and phase signal are normalized to eliminate offset and noise before training. Furthermore, a residual neural network (ResNet) is carefully built as a gesture classification model. The experimental results show that the recognition system achieves more recognition accuracy than existing methods, and the average gesture recognition accuracy reaches 95.5%.