GWrite: Enabling Through-the-Wall Gesture Writing Recognition Using WiFi

Abstract

Recognizing in-air gestures can enable intelligent Human-Computer Interaction (HCI) applications and facilitate human lives. However, existing sensor/camera-based methods for gesture recognition are either non-ubiquitous, intrusive to privacy, or inconvenient to carry around. Contemporary device-free approaches require the person to be in the line of sight and proximity to the sensing device. This paper shows that WiFi signals can recognize hand-drawn in-air gestures even when the gesture location is non-line-of-sight/beyond walls to the WiFi transceivers. The proposed GWrite system utilizes the CSI time-series information from commercial WiFi chipsets. GWrite employs a unique approach for performing hand gestures, thus enabling the design of a hand movement model. Using the model and the time-reversal (TR) technique, this work derives a correspondence between the similarity of CSIs and the relative distance moved by the hand. This relation gave rise to unique features such as the number of segments, angle, and the intersection between segments that can classify a set of gesture shapes consisting of straight-line segments. GWrite achieved an accuracy of 92% on a group of 15 gestures. The proposed approach can be applied to a broader set of gestures, unlike the current systems that function over a limited gesture set.