Super cheap tags can make our dumb stuff smarter

(Credit: j f grossen/Flickr)

A new RFID-based technology could turn frying pans, pill bottles, yoga mats, coffee cups, and countless other nonelectronic objects into a network of Internet of Things sensors.

The system, called IDAct, bridges the gap between the estimated 14.2 billion “smart” electronic devices that are currently part of the Internet of Things and the hundreds of billions of everyday nonsmart objects left out of the picture.

The researchers say it’s a key step toward creating a truly immersive IoT experience.

“Imagine a world where your pill bottle keeps track of your medication intake and a water glass monitors your hydration level,” says Alanson Sample, associate professor of electrical engineering and computer science at the University of Michigan and an author of a paper researchers recently presented at the iEEE RFID Conference in Phoenix, Arizona.

“Even your yoga mat is aware of your exercises and could adjust lighting, temperature, and background music accordingly.”

The technology could also have applications in elder care, where it could unobtrusively monitor medications and daily activities, helping seniors stay independent longer without the need for expensive and invasive live-in care.

Everything becomes a sensor

Using RFID readers and battery-free RFID tags that cost only a few cents, IDAct can sense the presence and movement of people in a room and detect the movement of objects with enough detail to determine, for example, whether you’ve moved a pill bottle or cooked a meal. The tags can attach to nearly any object in the form of a sticker, and RFID readers can be integrated into everyday objects like light bulbs.

“Given the ubiquity of these objects, there are significant opportunities in enhancing their sensing capabilities and creating interactive applications around them,” says lead author Hanchuan Li, a former graduate researcher in computer science and engineering at the University of Washington.

The technology accurately detected specific activities more than 96 percent of the time in a recent study.

“You could imagine assistive tools that could help the elderly stay in their own homes longer by monitoring their daily activities with this technology,” Sample says. “It could detect changes in eating, sleeping, or medication, for example, before the situation deteriorates and they end up in the emergency room.”

How do RFID tags work?

RFID tags have been used for years to track objects in applications like shipping and theft prevention. The tags absorb just enough electromagnetic energy from the reader’s signal to broadcast a simple, unique code. In the past, the reader simply picked up this code to identify whether the object was present or not—on or off, signal or no signal.

IDAct improves on this by providing a more nuanced reading of the signal from the RFID tags. It can detect minute fluctuations in the signal coming back from tags to detect when an object is moved or whether a person is touching it. It can also detect changes in a room’s electromagnetic field to infer, for example, when a human is present.

“Every object causes electromagnetic interference in a specific way,” Sample says. “We can use that information, along with information from RFID tags, to get a very detailed picture of what’s going on in a given space.”

A machine learning algorithm an onsite computer runs then analyzes these improved signals to infer what’s happening in a room. In the testing phase, a laptop did this processing, but Sample envisions that researchers will eventually integrate the necessary hardware into the RFID reader itself.

The team tested the technology by outfitting a volunteer’s apartment with a series of RFID readers and then tagging household objects with RFID tags. They collected 26 hours of data from each room while users were present, and also collected two hours of data from empty rooms as a control.

The team now plans to look for industry partners that could build out the technology for use in elder care settings.

Additional researchers from the University of Washington and Intel Corp contributed to the work. Intel Labs supported the research.

Source: University of Michigan