Vi-liquid: unknown liquid identification with your smartphone vibration

Abstract

Traditional liquid identification instruments are often unavailable to the general public. This paper shows the feasibility of identifying unknown liquids with commercial lightweight devices, such as a smartphone. The wisdom arises from the fact that different liquid molecules have various viscosity coefficients, so they need to overcome dissimilitude energy barriers during relative motion. With this intuition in mind, we introduce a novel model that measures liquids' viscosity based on active vibration. Yet, it is challenging to build up a robust system utilizing the built-in accelerometer in smartphones. Practical issues include under-sampling, self-interference, and volume change impact. Instead of machine learning, we tackle these issues through multiple signal processing stages to reconstruct the original signals and cancel out the interference. Our approach could achieve the liquid viscosity estimates with a mean relative error of 2.9% and distinguish 30 kinds of liquid with an average accuracy of 95.47%.