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

arXiv: https://arxiv.org/abs/2405.19351 · HTML: https://mlsanity.com/paper/2405.19351/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition with Short Range Radars](https://mlsanity.com/paper/2006.16281/) (2020)
- [Gesture Recognition for FMCW Radar on the Edge](https://mlsanity.com/paper/2310.08876/) (2023)
- [Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform](https://mlsanity.com/paper/2005.10145/) (2020)

## Abstract (arXiv)

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