Smart Cup: An impedance sensing based fluid intake monitoring system for beverages classification and freshness detection

TL;DR

This paper introduces a beverage monitoring system using impedance sensing with electrodes on a cup to accurately classify drinks and assess their freshness, demonstrating high accuracy across various beverage types.

Contribution

The study develops a novel impedance-based system for beverage classification and freshness detection, highlighting the importance of low-frequency features and machine learning for high accuracy.

Findings

High classification accuracy for 20 beverages using impedance features.

Effective freshness detection for milk and fruit juices.

Low-frequency impedance features are most informative.

Abstract

This paper presents a novel beverage intake monitoring system that can accurately recognize beverage kinds and freshness. By mounting carbon electrodes on the commercial cup, the system measures the electrochemical impedance spectrum of the fluid in the cup. We studied the frequency sensitivity of the electrochemical impedance spectrum regarding distinct beverages and the importance of features like amplitude, phase, and real and imaginary components for beverage classification. The results show that features from a low-frequency domain (100 Hz to 1000 Hz) provide more meaningful information for beverage classification than the higher frequency domain. Twenty beverages, including carbonated drinks and juices, were classified with nearly perfect accuracy using a supervised machine learning approach. The same performance was also observed in the freshness recognition, where four different kinds of milk and fruit juice were studied.