A group of researchers led by Massachusetts Institute of Technology professor Dina Katabi has developed software that uses variations in radio signals to recognize human silhouettes through walls and track their movements.
Researchers say the technology will be able to help health care providers and families keep closer tabs on toddlers and the elderly, and it could be a new strategic tool for law enforcement and the military.
"Think of it just like cameras, except that it's not a camera," said Fadel Adib, a researcher on the MIT team developing the device.
"It's a sensor that can monitor people and allow you to control devices just by pointing at them," he said.
Work began in 2012 to determine how wireless signals could be used to "see" what's happening in another room, said Katabi, who directs the MIT Wireless Center.
"At first we were just interested ... can you at all use wireless signals to detect what's happening in occluded spaces, behind a wall, couch, something like that," Katabi said.
"It turned out that we were able to detect that. And when we figured out we could detect that, we started asking more advanced questions: Could we use it to detect exactly how people are moving in a space if they are behind a wall?"
Using radio signals to see through walls: Cool. Handing new spying tools to government: Not Cool.
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Watch Out, MIT’s New AI Model Knows What You’re Doing Behind That Wall:
For better or worse, AI can now figure out what you're doing even without "seeing" you. The MIT Computer Science & AI Lab (CSAIL) has unveiled a neural network model that can detect human actions through walls or in extremely dark places.
Although automating the process of action recognition from visual data has been a computer vision research focus for some time, previous camera-based approaches — much like human eyes — could only sense visible light and were largely limited by occlusions. The MIT CSAIL researchers overcame those challenges by using radio signals in the WiFi frequencies, which can penetrate occlusions.
Their "RF-Action" AI model is an end-to-end deep neural network that recognizes human actions from wireless signals. The model uses radio frequency (RF) signals as input, generates 3D human "skeletons" as an intermediate representation, and can track and recognize actions and interactions of multiple people. The skeleton step enables the model to learn not only from RF-based datasets, but also from existing vision-based datasets.
Researchers say RF-Action is the first model to use radio signals for skeleton-based action recognition. "There are lots of potential applications regarding human behavior understanding and smart homes. For example, monitoring the elderly's abnormal behaviors such as falling down at home, monitoring whether patients take their medicine appropriately, or remote control of smart home devices by actions," says the paper's co-first author Tianhong Li.
Using RF in the "WiFi" bands. 25 hours of data was all it took (or all they collected) to train and test the AI. This article was unclear if the WiFi RF used was active, or passive although earlier reporting specifically mentioned passive.
MIT CSAIL RF Action site has a link to the paper:
Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Tianhong Li*, Lijie Fan*, Mingmin Zhao, Yingcheng Liu, Dina Katabi
International Conference on Computer Vision (ICCV), 2019
[PDF] [arXiv] [BibTeX]
This looks like an update of the story we first published in 2015, but now including AI.
Previously:
- X-Ray Vision? Technology Making It a (Sort-of) Reality for $300
- See Through Walls by Passive Radiation From Wi-Fi
(Score: 2) by Gaaark on Thursday December 24 2015, @11:48AM
I had those glasses when i was a kid! Got em from the back of a comic book.
--- Please remind me if I haven't been civil to you: I'm channeling MDC. ---Gaaark 2.0 ---
(Score: 1) by xpda on Thursday December 24 2015, @03:59PM
Here's an article from October with a little more information:
http://spectrum.ieee.org/tech-talk/semiconductors/optoelectronics/mits-3d-microwave-camera-can-see-through-walls [ieee.org]
As usual, phys.org isn't the best source.
(Score: 0) by Anonymous Coward on Thursday December 24 2015, @05:03PM
Fuck you. Do most researchers have any ethics whatsoever? If this works at all, it will be used to conduct unconstitutional surveillance on the populace. Researchers should demand that this not happen, not list it as yet another tool in the government's oppression toolbox.
(Score: 2) by davester666 on Friday December 25 2015, @04:42AM
at least you found out about it now and can potential do things to prevent or minimize the usefulness of this technique.
I'm certain DHS/FBI/NSA wish they didn't go public.
(Score: 0) by Anonymous Coward on Thursday December 24 2015, @05:20PM
Goddammit MIT now I have to tin-foil my entire house
(Score: 0) by Anonymous Coward on Thursday December 24 2015, @06:33PM
You may find that your house is already foil-lined.
For example some siding is metal, and some insulation has a reflective layer.
During testing I found the WiFi at my parent's house lost 30dB as soon as you walked out the door (but the picture window leaked almost enough to get to the bus-stop).
Apartment dwellers probably have to worry though.
Through-the-Wall Sensing of Personnel Using Passive Bistatic WiFi Radar at Standoff Distances [ieee.org]
(Score: 0) by Anonymous Coward on Thursday December 24 2015, @07:34PM
er, toddlers and the elderly are TOTALLY the wrong demographic.
if you want to increase sales, focus on whether it will it let us see more boobies.
we ♥ boobies!
(Score: 0) by Anonymous Coward on Thursday December 24 2015, @11:30PM
At 6cm resolution, they would have to be at least a B cup (about 5cm from the chest).
(Score: 2) by frojack on Friday December 25 2015, @01:47AM
Mankind just can't resist creating Skynet.
Everybody sees where its leading, everybody agrees its bad, but everybody insists on doing it anyway.
We need to have a law requiring the development of a countermeasure along with each research project that aims to empower police spying.
No, you are mistaken. I've always had this sig.