Work

Spatiotemporal radar gesture recognition with hybrid spiking neural networks: balancing accuracy and efficiency

Year: 2026

Type: article

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 application of spiking neural networks (SNNs) for radar-based HAR on aircraft marshaling signal classification. Our novel hybrid architecture combines a pre-trained convolutional backbone for spatial feature extraction and leaky integrate-and-fire neurons for temporal processing, inherently capturing gesture dynamics. The model reduces trainable parameters by 88% with under 1% accuracy loss compared to existing state of the art methods, and generalizes well to the Soli gesture dataset. Through systematic comparisons with other three artificial neural network architectures, 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.

Source: Neuromorphic Computing and Engineering

DOI: 10.1088/2634-4386/ae4648

Authors: Riccardo Mazzieri, Eleonora Cicciarella, Jacopo Pegoraro, Federico Corradi, Michele Rossi

Institutions: University of Padua, Eindhoven University of Technology

Language: English

Topic: Hand Gesture Recognition Systems

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,835

Funders: European Commission, HORIZON EUROPE Framework Programme