Smartphone-Based Identification of Unknown Liquids via Active Vibration Sensing
This enables accessible liquid identification for the general public, representing a novel application but incremental in method.
The paper tackled the problem of identifying unknown liquids using smartphones by developing a system based on active vibration sensing to measure viscosity, achieving a mean relative error of 2.9% in viscosity estimation and distinguishing 30 liquid types with 95.47% accuracy.
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 key insight is that different liquid molecules have different viscosity coefficients and therefore must overcome different energy barriers during relative motion. With this intuition in mind, we introduce a novel model that measures liquids' viscosity based on active vibration. However, building a robust system using built-in smartphone accelerometers is challenging. Practical issues include under-sampling, self-interference, and the impact of liquid-volume changes. 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 estimates liquid viscosity with a mean relative error of 2.9% and distinguishes 30 types of liquids with an average accuracy of 95.47%.