In this paper, we investigate novel data collection and training techniques towards improving classification accuracy of non-moving (static) hand gestures using a convolutional neural network (CNN) and frequency-modulated-continuous-wave (FMCW) millimeter-wave (mmWave) radars. Recently, non-contact hand pose and static gesture recognition have received considerable attention in many applications ranging from human-computer interaction (HCI), augmented/virtual reality (AR/VR), and even therapeutic range of motion for medical applications. While most current solutions rely on optical or depth cameras, these methods require ideal lighting and temperature conditions. mmWave radar devices have recently emerged as a promising alternative offering low-cost system-on-chip sensors whose output signals contain precise spatial information even in non-ideal imaging conditions. Additionally, deep convolutional neural networks have been employed extensively in image recognition by learning both feature extraction and classification simultaneously. However, little work has been done towards static gesture recognition using mmWave radars and CNNs due to the difficulty involved in extracting meaningful features from the radar return signal, and the results are inferior compared with dynamic gesture classification. This article presents an efficient data collection approach and a novel technique for deep CNN training by introducing ``sterile'' images which aid in distinguishing distinct features among the static gestures and subsequently improve the classification accuracy. Applying the proposed data collection and training methods yields an increase in classification rate of static hand gestures from $85\%$ to $93\%$ and $90\%$ to $95\%$ for range and range-angle profiles, respectively.

翻译：本文研究利用卷积神经网络（CNN）和调频连续波（FMCW）毫米波雷达，通过新型数据采集与训练技术提升静态（非移动）手势分类精度。近年来，非接触式手部姿态与静态手势识别在人机交互（HCI）、增强/虚拟现实（AR/VR）乃至医疗领域治疗性关节活动度检测等众多应用中受到广泛关注。虽然现有解决方案大多依赖光学或深度摄像头，但这些方法需要理想的光照与温度条件。毫米波雷达器件作为有前景的替代方案近年兴起，其系统级芯片传感器成本低廉，输出信号即使在非理想成像条件下也能提供精确空间信息。此外，深度卷积神经网络通过同时学习特征提取与分类，已在图像识别领域得到广泛应用。然而，由于从雷达回波信号中提取有效特征存在困难，基于毫米波雷达与CNN的静态手势识别研究尚不充分，且其结果劣于动态手势分类。本文提出一种高效数据采集方法与新型深度CNN训练技术，通过引入"无菌"图像帮助区分静态手势间的独特特征，进而提升分类精度。应用所提出的数据采集与训练方法后，基于距离剖面与距离-角度剖面的静态手势分类率分别从85%提升至93%和从90%提升至95%。