Wi-Fi CSI-Based Human Activity Recognition and Indoor Localization With Sampling Irregularity Mitigation

This paper introduces a dual-task recognition system that uses Wi-Fi channel state information (CSI) for human Activity Recognition (AR) and Indoor Localization (IL), effectively addressing the limitations of single-task recognition approaches. The proposed system leverages complementary information derived from dual-stream signal characteristics by capitalizing on the relationship between the two tasks. Moreover, it incorporates a technique to mitigate sampling irregularities, effectively reducing data misrepresentation. Extensive experimental evaluations on two datasets substantiate the efficacy of the proposed method in managing dual-task recognition scenarios, consistently outperforming specialized single-task recognition solutions. These results highlight its potential as a versatile solution for Wi-Fi CSI-based recognition applications, including IoT monitoring, surveillance, and healthcare.