Gesture-Radar: Enabling Natural Human-Computer Interactions with Radar-Based Adaptive and Robust Arm Gesture Recognition

Human behavior recognition is an effective way to realize natural human-computer interactions. Existing wireless sensing enabled gesture recognition technologies require a single person environment or an absolutely fixed position between the device and the user, which is not practical for daily use. In this paper, we present a non-contact radar-based gesture recognition system, named Gesture-Radar, which is able to capture arm gestures with low environmental dependence using a single Doppler radar. Our prototype design of Gesture-Radar is based on the dual channel Doppler information which contains specific Doppler shift and some other information reflected from the user while performing a certain gesture, and concretely we propose a two-stage classification method to identify arm gestures. Experimental result shows while in a single user environment, Gesture-Radar achieves up to 96.4% average classification accuracy for recognizing 5 different kinds of gestures and can work effectively while the distance between the user and the radar is within 3 meters. We also demonstrate that Gesture-Radar can be well adapted to multi-person environments.