{"ID":2864717,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23303","arxiv_id":"2509.23303","title":"Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency","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.","short_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 com...","url_abs":"https://arxiv.org/abs/2509.23303","url_pdf":"https://arxiv.org/pdf/2509.23303v1","authors":"[\"Riccardo Mazzieri\",\"Eleonora Cicciarella\",\"Jacopo Pegoraro\",\"Federico Corradi\",\"Michele Rossi\"]","published":"2025-09-27T13:31:11Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[]","has_code":false}