Radar-Based Gesture Recognition System using Spiking Neural Network

Hand gesture recognition has become an increasingly important functionality in intelligent human-computer interfaces, finding applications in automotive, gaming and consumer industries. In this paper, we present an embedded gesture recognition system using frequency modulated continuous wave radar operating at 60 GHz. To facilitate low-power and low latency operation of the proposed system, spiking neural networks are used which are sparse in time and space, and event-driven. The experimental results demonstrate that the proposed neuromorphic implementation is capable of achieving a high recognition rate of 97.5% which is comparable to its deep learning counterparts in identifying radar gestures, namely up-down, down-up, swipe and finger rub.