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Smartphone-Based Identification of Unknown Liquids via Active Vibration Sensing

Yongzhi Huang

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%.

Smartphone-Based Identification of Unknown Liquids via Active Vibration Sensing

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%.

Paper Structure

This paper contains 47 sections, 22 equations, 30 figures, 1 table.

Table of Contents

  1. Introduction
  2. Approach.
  3. Challenges.
  4. Our response.
  5. Results.
  6. Contributions.
  7. Vibration Model for Viscosity Measurement
  8. Liquid Viscosity and the Shearing Force
  9. Viscosity Calculation Leveraging Vibration
  10. Feasibility Study
  11. Experimental Setup
  12. The Viscosity Uniqueness of Liquids
  13. The Impact of Mass
  14. The Volume Interference
  15. Attenuation in Decaying-State
  16. ...and 32 more sections

Figures (30)

  • Figure 1: (a) Using an iPhone 7 to detect a liquid. (b) The user interface of Vi-Liquid.
  • Figure 2: Physical measurement model of Vi-Liquid.
  • Figure 3: Feasibility-study setup with a separate motor, accelerometer, container, and alternating-current power supply.
  • Figure 4: Dissolving different masses of sucrose and NaCl in solution. (a) Change of viscosity. (b) Change of average peak value.
  • Figure 5: Distribution of different volumes of liquid in the mass--amplitude plane.
  • ...and 25 more figures