Smartphone-Based Identification of Unknown Liquids via Active Vibration Sensing

TL;DR

This paper demonstrates that smartphones can identify unknown liquids by measuring their viscosity through active vibration sensing, achieving high accuracy without machine learning.

Contribution

It introduces a novel vibration-based viscosity measurement model and a robust signal processing system for liquid identification using smartphones.

Findings

Estimated liquid viscosity with 2.9% mean relative error.

Distinguished 30 liquid types with 95.47% accuracy.

Overcame practical challenges like under-sampling and interference.

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 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%.