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

• URL: http://arxiv.org/abs/2405.19351v1
• Date: Wed, 22 May 2024 14:40:02 GMT
• Title: Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach
• Authors: Ahmed Shaaban, Zeineb Chaabouni, Maximilian Strobel, Wolfgang Furtner, Robert Weigel, Fabian Lurz,
• Abstract summary: 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. The proposed approach demonstrates competitive performance with reduced complexity compared to traditional methods.
• Score: 0.8802544215891168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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