Fine-grained Textile Moisture Sensing with Commodity UWB

RF sensing has attracted a tremendous amount of attention and achieved promising progress in applications such as human gesture recognition and vital sign monitoring. This paper delves into sensing the moisture level of fabrics---an important metric for smart clothing, wound care, and textile manufacturing. We present TMSense, an innovative contact-free fabric moisture measurement system that leverages UWB signals for sensing. We introduce a set of signal processing methods to tackle the challenge of weak fabric reflections that can be easily overwhelmed by noise interference. Additionally, we adopt a model-driven approach to get rid of reliance on extensive datasets. By exploiting the changes in the dielectric properties induced by moisture in textile fabrics, we establish a theoretical model that bridges the characteristics of the RF signal with the moisture content. Based on this model, we successfully eliminate interfering factors such as target-device distance and target attributes through delicate signal processing and parameter calibration. Comprehensive experiments conducted under various conditions, including different materials, sample forms, and parameter settings, demonstrate an impressively low median error of 1.4% on textile moisture measurements, outperforming commodity moisture sensors on the market.