Food and Liquid Sensing in Practical Environments Using RFIDs

Food and Liquid Sensing in Practical Environments using RFIDs

Unsoo Ha, Junshan Leng, Alaa Khaddaj and Fadel Adib. Usenix NSDI 2020


We present the design and implementation of RF-EATS, a system that can sense food and liquids in closed containers without opening them or requiring any contact with their contents. RF-EATS uses passive backscatter tags (e.g., RFIDs) placed on a container, and leverages near-field coupling between a tag’s antenna and the container contents to sense them noninvasively. 

In contrast to prior proposals that are invasive or require strict measurement conditions, RF-EATS is noninvasive and does not require any calibration; it can robustly identify contents in practical indoor environments and generalize to unseen environments. These capabilities are made possible by a learning framework that adapts recent advances in variational inference to the RF sensing problem. The framework introduces an RF kernel and incorporates a transfer model that together allow it to
generalize to new contents in a sample-efficient manner, enabling users to extend it to new inference tasks using a small number of measurements. 

We built a prototype of RF-EATS and tested it in seven different applications including identifying fake medicine, adulterated baby formula, and counterfeit beauty products. Our results demonstrate that RF-EATS can achieve over 90% classification accuracy in scenarios where state-of-the-art RFID sensing systems cannot perform better than a random guess.