# Smartphone-Based Identification of Unknown Liquids via Active Vibration Sensing

arXiv: https://arxiv.org/abs/2603.28787 · HTML: https://mlsanity.com/paper/2603.28787/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 57 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [EarLog: Opportunistic Ear Physiology Sensing toward Personalized Hearing Risk Awareness](https://mlsanity.com/paper/2610.12501/) (2026)

## Abstract (arXiv)

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%.