Wi-Spike: A Low-power WiFi Human Multi-action Recognition Model with Spiking Neural Networks
WiFi-based human action recognition (HAR) has gained significant attention due to its non-intrusive and privacy-preserving nature. However, most existing WiFi sensing models predominantly focus on improving recognition accuracy, while issues of power consumption and energy efficiency remain insufficiently discussed. In this work, we present Wi-Spike, a bio-inspired spiking neural network (SNN) framework for efficient and accurate action recognition using WiFi channel state information (CSI) signals. Specifically, leveraging the event-driven and low-power characteristics of SNNs, Wi-Spike introduces spiking convolutional layers for spatio-temporal feature extraction and a novel temporal at...
| Authors | Nengbo Zhang, Yao Ying, Lu Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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