Shaaban A, Chaabouni Z, Strobel M, Furtner W, Weigel R, Lurz F (2024)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2024
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: 2024 Smart Systems Integration Conference and Exhibition (SSI)
ISBN: 979-8-3503-8878-7
DOI: 10.1109/SSI63222.2024.10740507
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.
APA:
Shaaban, A., Chaabouni, Z., Strobel, M., Furtner, W., Weigel, R., & Lurz, F. (2024). Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach. In 2024 Smart Systems Integration Conference and Exhibition (SSI). Hamburg, DE: Institute of Electrical and Electronics Engineers Inc..
MLA:
Shaaban, Ahmed, et al. "Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach." Proceedings of the 2024 Smart Systems Integration Conference and Exhibition, SSI 2024, Hamburg Institute of Electrical and Electronics Engineers Inc., 2024.
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