Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach

TL;DR

This paper introduces a hybrid approach using resonate-and-fire neurons for hand gesture recognition via radar, significantly reducing computational complexity while maintaining high accuracy.

Contribution

It presents a novel method that bypasses fast Fourier transforms with resonate-and-fire neurons and employs a simple feature extraction, improving efficiency in gesture recognition.

Findings

Achieved 98.21% accuracy in classifying five gestures.

Reduced computational complexity compared to traditional Fourier-based methods.

Demonstrated competitive performance with a simpler processing pipeline.

Abstract

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