Hand gesture recognition using radar often relies on computationally expensive fast Fourier transforms. This paper proposes an alternative approach that bypasses fast Fourier transforms using resonate-and-fire neurons. These neurons directly detect the hand in the time-domain signal, eliminating the need for fast Fourier transforms to retrieve range information. Following detection, a simple Goertzel algorithm is employed to extract five key features, eliminating the need for a second fast Fourier transform. These features are then fed into a recurrent neural network, achieving an accuracy of 98.21% for classifying five gestures. The proposed approach demonstrates competitive performance with reduced complexity compared to traditional methods

翻译：基于雷达的手势识别通常依赖于计算成本高昂的快速傅里叶变换。本文提出了一种替代方法，利用共振放电脉冲神经元绕过快速傅里叶变换。这些神经元直接在时域信号中检测手部，无需通过快速傅里叶变换来获取距离信息。检测完成后，采用简单的戈泽尔算法提取五个关键特征，从而避免了第二次快速傅里叶变换。这些特征随后被输入循环神经网络，在对五种手势进行分类时达到了98.21%的准确率。与传统方法相比，所提出的方法在降低复杂度的同时展现了具有竞争力的性能。