  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
