Exposing Data Leakage in Wi-Fi CSI-Based Human Action Recognition: A Critical Analysis

Abstract

Wi-Fi channel state information (CSI)-based human action recognition systems have garnered significant interest for their non-intrusive monitoring capabilities. However, the integrity of these systems can be compromised by data leakage, particularly when improper dataset partitioning strategies are employed. This paper investigates the presence and impact of data leakage in three published Wi-Fi CSI-based human action recognition methods that utilize deep learning techniques. The original studies achieve precision rates of 95% or higher, attributed to the lack of human-based dataset splitting. By re-evaluating these systems with proper subject-based partitioning, our analysis reveals a substantial decline in performance, underscoring the prevalence of data leakage. This study highlights the critical need for rigorous dataset management and evaluation protocols to ensure the development of robust and reliable human action recognition systems. Our findings advocate for standardized practices in dataset partitioning to mitigate data leakage and enhance the generalizability of Wi-Fi CSI-based models.