Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach
<|reference_start|>Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach: 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<|reference_end|>
@article{shaaban2024resonate-and-fire,
    title={Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture
  Recognition: A Hybrid Approach},
    author={Ahmed Shaaban, Zeineb Chaabouni, Maximilian Strobel, Wolfgang Furtner,
  Robert Weigel, Fabian Lurz},
    journal={2024 Smart Systems Integration Conference and Exhibition (SSI)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19351},
    primaryClass={eess.SP cs.LG cs.NE}
}