Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach - Shaaban, Ahmed; Chaabouni, Zeineb; Strobel, Maximilian; Furtner, Wolfgang; Weigel, Robert; Lurz, Fabian (2024 Smart Systems Integration Conference and Exhibition (SSI), 2024)
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.
CRIS-Team Scopus Importer:2025-02-04
Keywords: FMCW Radar; Gesture Recognition; Machine Learning; Resonate-and-Fire; Spiking Neural Networks