Abstract
Recently, gesture recognition has gained considerable attention in emerging applications (e.g., AR/VR systems) to provide a better user experience for human-computer interaction. Existing solutions usually recognize the gestures based on wearable sensors or specialized signals (e.g., WiFi, acoustic and visible light), but they are either incurring high energy consumption or susceptible to the ambient environment, which prevents them from efficiently sensing the fine-grained finger movements. In this paper, we present RF-finger, a device-free system based on Commercial-Off-The-Shelf (COTS) RFID, which leverages a tag array on a letter-size paper to sense the fine-grained finger movements performed in front of the paper. Particularly, we focus on two kinds of sensing modes: finger tracking recovers the moving trace of finger writings; multi-touch gesture recognition identifies the multi-touch gestures involving multiple fingers. Specifically, we build a theoretical model to extract the fine-grained reflection feature from the raw RF -signal, which describes the finger influence on the tag array in cm- level resolution. For the finger tracking, we leverage K-Nearest Neighbors (KNN) to pinpoint the finger position relying on the fine-grained reflection features, and obtain a smoothed trace via Kalman filter. Additionally, we construct the reflection image of each multi-touch gesture from the reflection features by regarding the multiple fingers as a whole. Finally, we use a Convolutional Neural Network (CNN) to identify the multi-touch gestures based on the images. Extensive experiments validate that RF -finger can achieve as high as 88% and 92% accuracy for finger tracking and multi-touch gesture recognition, respectively.