Wifi-Based Device-Free Gesture Recognition Through-The-Wall

Device-free (passive) gesture recognition offers an enormous potential to simplify Human-Computer Interaction (HCI) in future smart environments. WIFI-based gesture recognition approaches have attained acclaim amongst others due to the omnipresence, privacy-preservation, and broad coverage of WIFI. However, there is no universal solution built on off-the-shelf devices that can accommodate an expandable set of gestures in a through-the-wall setting. In this work, we propose such a gesture recognition system that can recover information about the actual trajectory of the hand movement allowing an expandable set of gestures. Further, we leverage the rich multipath in a through-the-wall setting to develop a statistical model for the channel variations induced by a hand gesture. This model is used to derive a correspondence between the relative distance moved by the hand and the Time Reversal Resonating Strength (TRRS) decay. Based on this relation and the geometry of the gesture shape, we design feature extraction modules to enable gesture classification. We built a prototype of the proposed system on off-the-shelf WIFI devices and achieved a classification accuracy of 87% on a set of 6 uppercase English alphabets.