Wifinger: Leveraging Commodity Wifi For Fine-Grained Finger Gesture Recognition

Gesture recognition has become increasingly important in human-computer interaction (HCI) and can support a broad array of emerging applications, such as smart home, virtual reality, and mobile gaming. Traditional approaches usually rely on dedicated sensors that are worn by the user or cameras that require line of sight. In this paper, we present fine-grained finger gesture recognition by using a single commodity WiFi device without requiring user to wear any sensors. Our low-cost system, WiFinger, takes advantages of the fine-grained Channel State Information (CSI) available from commodity WiFi devices and the prevalence of WiFi network infrastructures. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. In WiFigner, we devise environmental noise removal mechanism to mitigate the effect of signal dynamic due to the environment changes. Moreover, we propose to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency. Our experimental evaluation in both home and office environments demonstrates that our system can achieve over 93% recognition accuracy and is robust to both environment changes and individual diversity. Results also show that our system can work with WiFi beacon signals and provides accurate gesture recognition under NLOS scenarios.