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Spiking Neural Network-Based Radar Gesture Recognition System Using Raw ADC Data
  • Muhammad Arsalan ,
  • Avik Santra ,
  • Vadim Issakov
Muhammad Arsalan
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Avik Santra
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Vadim Issakov
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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 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 pre-processing slow-time FFT is mimicked in the proposed SNN architecture, where the proposed model processing speed of 12 ms advances the state-of-the-art by a factor of 17.7. The experimental results demonstrate that despite the simplification the proposed implementation achieves recognition accuracy of 98.1 %, which is comparable to the conventional approaches.
Jun 2022Published in IEEE Sensors Letters volume 6 issue 6 on pages 1-4. 10.1109/LSENS.2022.3173589