Food and Liquid Sensing in Practical Environments using RFIDs

Authors: 

Unsoo Ha, Junshan Leng, Alaa Khaddaj, and Fadel Adib, Massachusetts Institute of Technology

Abstract: 

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 non- invasive 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.

NSDI '20 Open Access Sponsored by NetApp

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BibTeX
@inproceedings {246304,
author = {Unsoo Ha and Junshan Leng and Alaa Khaddaj and Fadel Adib},
title = {Food and Liquid Sensing in Practical Environments using RFIDs },
booktitle = {17th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 20)},
year = {2020},
isbn = {978-1-939133-13-7},
address = {Santa Clara, CA},
pages = {1083--1100},
url = {https://www.usenix.org/conference/nsdi20/presentation/ha},
publisher = {{USENIX} Association},
month = feb,
}

Presentation Video