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

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.