Spiking Neural Network-Based Radar Gesture Recognition System Using Raw ADC Data

One of the main challenges in developing embedded radar-based gesture recognition systems is the requirement of energy efficiency. To facilitate this, we present an embedded gesture recognition system using a 60 GHz frequency modulated continuous wave radar using spiking neural networks (SNNs) applied directly to raw analog-to-digital converter (ADC) data. The SNNs are sparse in time and space, and event driven, which makes them energy efficient. In contrast to the previous state-of-the-art methods, the proposed system is only based on the raw ADC data of the target, thus avoiding the overhead of performing the slow-time and fast-time Fourier transforms (FFTs). Furthermore, the preprocessing slow-time FFT is mimicked in the proposed SNN architecture, where the proposed model processing speed of 112 ms advances the state-of-the-art methods by a factor of more than 2. The experimental results demonstrate that despite the simplification, the proposed implementation achieves recognition accuracy of
$98.1 \%$
, which is comparable with the conventional approaches.