WiFi CSI based Liquid Temperature Prediction: A Physics-Guided Machine Learning Approach

Sensing the temperature of liquids in containers is a critical process in various applications such as food safety, healthcare and environmental monitoring. Traditional approaches usually require a physical contact with the liquid, which may not be suitable for the liquids in sealed containers, and may also pose a risk for contamination in particular for the products used in healthcare. The contactless temperature sensing approaches that mainly rely on optical or laser based solutions, however, can only detect the temperature of a certain point or only the surface temperature of the liquid. Sensing through thermal cameras can provide scalability, however, they come with high costs. In this study, leveraging the ubiquitous availability of low-cost devices integrated with WiFi interfaces, we explore the feasibility of measuring the liquid temperature using fine-grained WiFi features. The proposed approach utilizes the amplitude variations across WiFi channel subcarriers extracted from Channel State Information (CSI) to detect the liquid temperature. In order to enhance the prediction accuracy, we also integrate principles from Newton’s Law of Cooling for regularization during the training of the neural network and propose a physics-guided machine learning framework (PMLF). Through our experiments with different liquids, temperature ranges and containers, we have demonstrated that the proposed cost-effective and scalable solution provides promising results in predicting the liquid temperatures.