Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach
This work addresses computational efficiency in radar-based gesture recognition, though it is incremental as it builds on existing methods with a hybrid approach.
The paper tackled hand gesture recognition using radar by bypassing computationally expensive fast Fourier transforms with resonate-and-fire neurons for detection and a Goertzel algorithm for feature extraction, achieving 98.21% accuracy for five gestures.
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