A Short Overview of Multi-Modal Wi-Fi Sensing

TL;DR

This paper reviews recent advances in multi-modal Wi-Fi sensing, highlighting its applications, challenges, and future directions, with a focus on the last two years of research developments.

Contribution

It provides a comprehensive survey of multi-modal Wi-Fi sensing techniques, emphasizing recent progress, limitations, and potential future research avenues.

Findings

Multi-modal Wi-Fi sensing improves robustness and accuracy.

Recent methods leverage additional modalities for enhanced sensing.

The field faces challenges in data collection and model robustness.

Abstract

Wi-Fi sensing has emerged as a significant technology in wireless sensing and Integrated Sensing and Communication (ISAC), offering benefits such as low cost, high penetration, and enhanced privacy. Currently, it is widely utilized in various applications, including action recognition, human localization, and crowd counting. However, Wi-Fi sensing also faces challenges, such as low robustness and difficulties in data collection. Recently, there has been an increasing focus on multi-modal Wi-Fi sensing, where other modalities can act as teachers, providing ground truth or robust features for Wi-Fi sensing models to learn from, or can be directly fused with Wi-Fi for enhanced sensing capabilities. Although these methods have demonstrated promising results and substantial value in practical applications, there is a lack of comprehensive surveys reviewing them. To address this gap, this paper reviews the multi-modal Wi-Fi sensing literature \textbf{from the past 24 months} and highlights the current limitations, challenges and future directions in this field.