Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar

Hand gesture recognition with radar sensors is essential because they can detect gestures despite environmental factors like lighting, dust, and complex backgrounds. Considering the complexity of a system, it is challenging to design CNNs on CPU devices and realize the carry-on mid-air gesture recognition. We propose a mid-air gesture recognition method based on a novel discriminant feature, and it be used as part of a measurement system of hand movements using an ultrawideband (UWB) radar. The Gesture-ProxylessNAS (GPNAS) is presented to enhance the adaptability of model search and overcome the challenge of the network's computational complexity. In order to fully extract local spatial discriminant features and prevent information loss, local binary pattern (LBP) encoders are utilized to extract local spatial information. In the meantime, multilayer ShuffleNet with depthwise separable convolution is used to gradually leverage high-level spatial features. The GPNAS module revisits the multilayer ShuffleNet's design spaces using an optimization problem, greatly reducing the network's parameters and computational complexity. According to experimental verification on real UWB hand gestures, the proposed framework provides more satisfactory recognition performance and efficiency with a deeper network structure and fewer parameters. The proposed hand gesture recognition system can recognize gestures with a promising accuracy of 96.52% on the UWB-gestures public dataset.