LiqState: Liquid Identification and State Monitoring Using mmWave IoT Sensing

Traditional RF-based liquid identification methods generally rely on a single characteristic, such as refractive index or permittivity, and often assume prior container knowledge, limiting their versatility. These approaches also face challenges in scenarios involving gradual state changes in the liquid. We propose LiqState, a contactless framework for fine-grained liquid identification and continuous state monitoring, capable of operating without prior container information. To mitigate container effects, we developed a LiqState reflection model that analyzes frequency-dependent changes, leveraging the diverse permittivity profiles of liquids across the mmWave frequency range. Our approach introduces a novel feature extraction method, VRCP, which captures four distinct physical and chemical properties for robust identification and state monitoring. Using LiqNet, a service-oriented and customized deep learning model, LiqState achieves an average classification accuracy of 97.3% across diverse conditions, accurately distinguishing 12 liquid types. Additionally, case studies highlight LiqState’s capability to monitor complex processes, such as milk fermentation (RMSE: 0.251) and fruit juice ripening (RMSE: 0.162), and differentiate between similar liquids with minimal alcohol concentration variations.