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

TL;DR

Wi-Spike introduces a bio-inspired spiking neural network framework for WiFi-based human action recognition, achieving high accuracy and significantly reduced energy consumption, suitable for real-time edge sensing.

Contribution

The paper presents Wi-Spike, a novel SNN-based model that enhances energy efficiency and accuracy in WiFi human action recognition, especially for multi-action tasks.

Findings

Achieves 95.83% accuracy in human activity recognition.

Reduces energy consumption by at least 50% compared to existing methods.

Sets new state-of-the-art in WiFi-based multi-action HAR.

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.