Robust Gesture Recognition Using Millimetric-Wave Radar System

Gesture recognition is one of the most intuitive forms of human-computer interface. Gesture sensing can replace interfaces such as touch and clicks needed for interacting with a device. In this article, we present a short-range compact 60-GHz mm-wave radar sensor that is sensitive to fine dynamic hand motions. A series of range-Doppler images are extracted and processed using a long recurrent all-convolution neural network for real-time dynamic hand gesture recognition. Furthermore, we make use of novel data augmentation techniques for the proposed gesture recognition system to generalize for multiple users and operating environments. The results show accurate classification performance requiring very low processor footprint facilitating implementation in embedded platforms with real-time user feedback.