The University of Michigan has developed technology that enhances the functionality of RFID enabling tags to report movement and more.

A standard RFID tag absorbs electromagnetic energy from the reader and uses this to broadcast its identity to the reader enabling the reader to record the presence of the tag but nothing more.

The University says IDAct 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, and can also detect changes in a room’s electromagnetic field to infer, for example, when a human is present.

“These improved signals are then analysed by a machine learning algorithm run by an onsite computer to infer what’s happening in a room,” it explains.

At present this processing is done on a laptop, but the University expects the necessary hardware to be integrated into the RFID reader the future.

Alanson Sample, associate professor of electrical engineering and computer science and an author of a paper on IDAct presented recently at the IEEE RFID Conference in Phoenix, said every object caused electromagnetic interference in a specific way.

“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.”

Creating an immersive IoT experience

He envisages the technology being used to create an immersive IoT experience.

“Imagine a world where your pill bottle keeps track of your medication intake and a water glass monitors your hydration level. Even your yoga mat is aware of your exercises and could adjust lighting, temperature and background music accordingly.”

The university claimed that, in a recent study IDAct detected specific activities with 96 percent accuracy.

The researchers 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.

Care of the elderly a priority

Sample suggested the technology could have applications in elder care, where it could be used to unobtrusively monitor medications and daily activities, helping seniors stay independent longer without the need for expensive and invasive live-in care, and the research team plans to look for industry partners that could build out the technology for use in elder care settings.

The research paper IDAct: Towards Unobtrusive Recognition of User Presence and Daily Activities was supported by Intel Labs. Lead author was Hanchuan Li, a former graduate researcher in computer science and engineering at the University of Washington. Sample and Li developed the technology with Shwetak Patel at the University of Washington and Chieh-yih Wan and Raul Shal of Intel.


 

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