Smart Cup: An impedance sensing based fluid intake monitoring system for beverages classification and freshness detection
This addresses fluid intake monitoring for health applications, but it is incremental as it applies existing sensing and machine learning methods to a new domain.
The paper tackled beverage classification and freshness detection by measuring electrochemical impedance spectra with carbon electrodes on a cup, achieving nearly perfect accuracy for 20 beverages and similar performance for freshness recognition of milk and fruit juice.
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.