LipHS : A Lightweight WiFi-enabled Human Sensing For Multi-Class Scenarios

Abstract

WiFi-based human sensing technology utilizing Channel State Information (CSI) has garnered significant attention due to its reduced privacy concerns and the widespread availability of existing infrastructure, demonstrating broad development prospects in the field of intelligent computing. Deployment on edge devices represents its most prevalent application scenario. However, the high-complexity algorithms commonly employed to enhance sensing accuracy face substantial challenges when deployed on devices with limited computational resources. Furthermore, most existing studies conduct experiments only on datasets with a small number of categories. Although these approaches achieve high accuracy, they fail to meet practical sensing requirements. Consequently, developing high-accuracy, low-complexity, and practical WiFi-based human sensing systems remains considerably challenging. To construct an efficient and lightweight feature extraction network, we presents LipHS, a lightweight feature extraction framework capable of simultaneously capturing multi-level information from CSI signals. To further reduce the number of model parameters, we employ a channel pruning method based on Layer-Adaptive Magnitude-based Pruning (LAMP) scores. LipHS achieves model lightweighting while maintaining robust feature extraction capabilities. Experimental results demonstrate that the proposed LipHS method outperforms other baseline algorithms in sensing performance on complex multi-class gesture datasets.