In the fast-paced field of human-computer interaction (HCI) and virtual reality (VR), automatic gesture recognition has become increasingly essential. This is particularly true for the recognition of hand signs, providing an intuitive way to effortlessly navigate and control VR and HCI applications. Considering increased privacy requirements, radar sensors emerge as a compelling alternative to cameras. They operate effectively in low-light conditions without capturing identifiable human details, thanks to their lower resolution and distinct wavelength compared to visible light. While previous works predominantly deploy radar sensors for dynamic hand gesture recognition based on Doppler information, our approach prioritizes classification using an imaging radar that operates on spatial information, e.g. image-like data. However, generating large training datasets required for neural networks (NN) is a time-consuming and challenging process, often falling short of covering all potential scenarios. Acknowledging these challenges, this study explores the efficacy of synthetic data generated by an advanced radar ray-tracing simulator. This simulator employs an intuitive material model that can be adjusted to introduce data diversity. Despite exclusively training the NN on synthetic data, it demonstrates promising performance when put to the test with real measurement data. This emphasizes the practicality of our methodology in overcoming data scarcity challenges and advancing the field of automatic gesture recognition in VR and HCI applications.

翻译：在人机交互（HCI）与虚拟现实（VR）这一快速发展的领域中，自动手势识别已变得日益重要，尤其是手语手势的识别，为直观地操作和控制VR及HCI应用提供了便捷途径。鉴于对隐私保护需求的提升，雷达传感器凭借其相较于可见光更低的波长分辨率和独特性质，成为摄像头的极具吸引力的替代方案——它们能在弱光条件下有效工作，且不会捕捉到可识别的人体细节。不同于以往主要利用多普勒信息进行动态手势识别的雷达传感器研究，我们的方法着重于基于空间信息（如图像类数据）的成像雷达分类技术。然而，为神经网络（NN）生成大规模训练数据集既耗时又充满挑战，往往难以覆盖所有潜在场景。针对这些难题，本研究探究了由先进雷达射线追踪模拟器生成的合成数据的有效性。该模拟器采用可调整的直观材质模型以增强数据多样性。尽管神经网络仅基于合成数据进行训练，但在真实测量数据的测试中仍展现出卓越性能。这充分证明了我们的方法在克服数据稀缺挑战方面的实用性，以及推动VR与HCI领域自动手势识别发展的潜力。