Wi-Spike: A Low-power WiFi Human Multi-action Recognition Model with Spiking Neural Networks

Abstract

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 attention mechanism to enhance discriminative representation. The extracted features are subsequently encoded and classified through spiking fully connected layers and a voting layer. Comprehensive experiments on three benchmark datasets (NTU-Fi-HAR, NTU-Fi-HumanID, and UT-HAR) demonstrate that Wi-Spike achieves competitive accuracy in single-action recognition and superior performance in multi-action recognition tasks. As for energy consumption, Wi-Spike reduces the energy cost by at least half compared with other methods, while still achieving 95.83% recognition accuracy in human activity recognition. More importantly, Wi-Spike establishes a new state-of-the-art in WiFi-based multi-action HAR, offering a promising solution for real-time, energy-efficient edge sensing applications.

Figures (13)

• Figure 1: Overview of WiFi sensing model based on Spiking Neural Network. Conventional WiFi-based human sensing systems, relying on artificial neural networks, suffer from high energy consumption. Inspired by brain-like models, we propose a novel WiFi human action recognition model by replacing traditional neurons with spiking neurons. This approach,termed Wi-Spike, achieves a low-power WiFi-based human action recognition system.
• Figure 2: Description of the basic principles of Spiking Neural Networks and Artificial Neural Network.
• Figure 3: A detailed comparison is provided in the single-action WiFi recognition and multi-action WiFi recognition problems. This includes WiFi CSI signals for one action, two actions, and three actions. In the right part of Fig. \ref{['fig:dense']}, clearly, the more human motion patterns in the WiFi CSI, the greater the number of step signals.
• Figure 4: Overview of Wi-Spike Architecture. From left to right, the raw WiFi CSI human activity signals sequentially pass through the spike encoder, spike feature extraction, spike feature encoding, and voting classification modules.
• Figure 5: Detailed structure of the Temporal Attention Layer.