Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency

• URL: http://arxiv.org/abs/2509.23303v1
• Date: Sat, 27 Sep 2025 13:31:11 GMT
• Title: Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency
• Authors: Riccardo Mazzieri, Eleonora Cicciarella, Jacopo Pegoraro, Federico Corradi, Michele Rossi,
• Abstract summary: We present the first use of Spiking Neural Networks (SNNs) for radar-based Human Activity Recognition (HAR)<n>Our novel hybrid architecture combines convolutional modules for spatial feature extraction with Leaky Integrate-and-Fire neurons for temporal processing.<n>We demonstrate the trade-offs of spiking computation in terms of accuracy, latency, memory, and energy, establishing SNNs as an efficient and competitive solution for radar-based HAR.
• Score: 3.6205625120193354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radar-based Human Activity Recognition (HAR) offers privacy and robustness over camera-based methods, yet remains computationally demanding for edge deployment. We present the first use of Spiking Neural Networks (SNNs) for radar-based HAR on aircraft marshalling signal classification. Our novel hybrid architecture combines convolutional modules for spatial feature extraction with Leaky Integrate-and-Fire (LIF) neurons for temporal processing, inherently capturing gesture dynamics. The model reduces trainable parameters by 88\% with under 1\% accuracy loss compared to baselines, and generalizes well to the Soli gesture dataset. Through systematic comparisons with Artificial Neural Networks, we demonstrate the trade-offs of spiking computation in terms of accuracy, latency, memory, and energy, establishing SNNs as an efficient and competitive solution for radar-based HAR.

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